A national registry to assess the value of cardiovascular magnetic resonance imaging after primary percutaneous coronary intervention pathway activation: a feasibility cohort study

Primary percutaneous coronary intervention

Primary percutaneous coronary intervention is the primary therapeutic approach for restoring blood flow to the heart in ACS patients who have had a ST-elevation myocardial infarction (STEMI). Currently, across the UK ≈97% of patients with a STEMI who received any reperfusion treatment were treated with PPCI. 5 Management of the in-hospital stay and follow-up after PPCI is determined by the final infarct size, the presence or absence of comorbidities and patient characteristics. About 10% of patients with ACS who activate the PPCI pathway (i.e. those that present with chest pain and ST-elevation or chest pain and new-onset left bundle branch block on electrocardiogram) are found to have unobstructed coronary arteries on angiography and, therefore, do not receive PPCI. There are no long-term outcome data from well-designed, multicentre trials on CMR use in different subgroups of patients who activate the PPCI pathway. There are also no studies reporting on the cost-effectiveness of CMR in these patients (or indeed patients from other clinical areas), which makes decision-making regarding the provision of CMR services within the NHS difficult.

Subgroups of patients who activate the primary percutaneous coronary intervention pathway and may benefit from cardiovascular magnetic resonance

Two subgroups of patients were identified in the protocol as having the potential to benefit from CMR: (1) patients with multivessel disease and (2) patients with unobstructed coronary arteries on angiography.

It is estimated that 40–65% of the patients presenting with a STEMI have multivessel disease (depending on the baseline characteristics, e.g. age, of the population studied). 6–11 There is ongoing debate about the optimal treatment strategy for these patients. Three main treatment management strategies have been proposed/are currently used: (1) conservative, PPCI of the infarcted artery followed by medical therapy (unless ischaemia occurs), (2) staged, in which only the infarct-related artery is treated acutely and other lesions are treated at a later date and (3) aggressive, percutaneous coronary intervention (PCI) of all significant lesions at the time of PPCI.

The role of CMR in patients with multivessel disease is undefined. CMR allows the volume of ischaemic or poorly perfused myocardium to be quantified but it is not known whether or not access to CMR to plan further revascularisation influences management. Since the Primary percutaneous coronary Intervention Pathway Activation (PIPA) study was commissioned, there has also been a rapid increase in the use of fractional flow reserve (FFR) testing (a guide wire-based procedure done through a standard diagnostic catheter at the time of a coronary angiography) to assess ischaemia; this technology is available to interventional cardiologists in the catheter laboratory. CMR is comparable to FFR for detecting ischaemia; studies have shown excellent diagnostic accuracy of perfusion CMR to detect areas of ischaemia identified by FFR. 12,13 However, while FFR may be appropriate in symptomatic, high-risk patients for whom the benefit of an invasive approach to ischaemia testing may outweigh the risks (i.e. the likelihood of revascularisation is high; therefore, lesions can be revascularised at the same time as the FFR), the benefit is less clear in asymptomatic multivessel disease patients. CMR may, therefore, have a role in selecting patients who will receive the greatest benefit from these invasive procedures. The non-inferiority of CMR in managing patients with multivessel disease is the subject of the magnetic resonance perfusion imaging to guide management of patients with stable coronary artery disease (MR-INFORM) trial. 14

The reported variation in the incidence of normal coronary angiograms in patients activating the PPCI pathway is between 3% and 16%. 15,16 It is in this group that cardiologists believe that CMR has the greatest potential to change patient management. In these patients, the lack of an accurate diagnosis may result in inappropriate treatment and/or follow-up,17,18 associated with a poorer prognosis. 19 A definitive diagnosis of myocardial infarction (MI) or not is also important so that these patients can be managed and followed up appropriately. Studies have shown that CMR can facilitate differential diagnosis in the context of a normal coronary angiogram,20–22 providing a definitive diagnosis (e.g. MI, myocarditis, Takotsubo cardiomyopathy) in 65–90% of these patients. 20,23,24 Although the prognostic implications of non-MI CMR abnormalities are not known, two studies showed that the pattern of CMR abnormalities in myocarditis was predictive of long-term outcome. 25,26

The benefit of CMR to other subgroups of patients who activate the PPCI pathway is less clear. However, given that not all of these patients will have access to centres with dedicated CMR facilities, there is potential for health inequality across the UK, which needs to be identified. Therefore, the population of interest for this study (target population) includes all ACS patients who activate the PPCI pathway, regardless of whether or not they receive PPCI.

Rationale for the study

We wanted to set up a multicentre, prospective cohort study (registry) in ACS patients who activate the PPCI pathway by linking routinely collected data from hospital information systems (HISs) with follow-up data from Hospital Episode Statistics (HES) and the Office for National Statistics (ONS). The aims of the registry were to document CMR activity in patients who activate the PPCI pathway and to provide information on CMR use, uptake over time, patients’ characteristics associated with being referred for CMR, the impact of CMR on patient management (e.g. early discharge of a patient with a normal coronary angiogram or the placement of an additional stent for a patient with multivessel disease) and the prognostic value of CMR after PPCI.

We were uncertain whether or not it would be feasible to set up a registry in this patient population using routinely collected data. In particular, we were uncertain if we could (1) easily identify the eligible population, (2) implement a ‘light-touch’ consent system across multiple hospitals and (3) extract and link data sets from multiple HISs in different hospitals. We also wanted (4) to provide a reliable estimate of the CMR rate in patients who activate the PPCI pathway and (5) to define a ‘proxy’ primary composite outcome, acceptable to cardiologists and other stakeholders (e.g. clinical commissioners) as representing a clinically important change in management as a result of an eligible patient having undergone CMR (e.g. expected to prevent future MACEs). Item number 5 was included so that the registry would be able to assess effectiveness in the medium term. A long-term objective of the registry would be to compare the incidence of MACEs in patients who do or do not have CMR after the index event, but the low frequency of MACEs means that the study would have to accrue over 37,000 subjects to detect a clinically important reduction in the incidence of MACEs with adequate power.

Study population

We aimed to approach all ACS patients who activated the PPCI pathway. This population included all patients who underwent an emergency angiogram, whether or not they received PPCI. We included patients if they were aged ≥ 18 years and underwent an emergency angiogram, defined as taking place within 2 hours of arrival at the hospital, unless specified otherwise by local protocols. We excluded patients if they were prisoners or lacked mental capacity to consent. There were no changes to the eligibility criteria during the study recruitment period. The study was reviewed and approved by the National Research Ethics Committee South West-Central Bristol (reference number 12/SW/0326).

Setting

Four NHS hospitals in England and Wales hosting 24/7 PPCI centres took part:27 the Bristol Heart Institute (University Hospitals Bristol NHS Foundation Trust), Leeds General Infirmary (Leeds Teaching Hospitals NHS Trust), Morriston Hospital (Abertawe Bro Morgannwg University Health Board, Swansea) and University Hospital of Wales (Cardiff & Vale University Health Board). Two of these hospitals (Bristol and Leeds) were defined as ‘coronary magnetic resonance (CMR) centres’, that is hospitals that had a dedicated CMR service. The other two hospitals (Swansea and Cardiff) were defined as ‘non-CMR centres’, that is hospitals that did not have access to a dedicated CMR service.

Patient identification and consent

Each hospital implemented its own methods for identifying and consenting patients. Most eligible patients were identified manually (e.g. by checking catheter laboratory records, by checking the local cardiology database or both). One of the participating hospitals set up a database query (including the search terms ‘PPCI’, ‘QueryNot’ and ‘QuerProc’) in the local cardiology database to flag up eligible patients. However, the query was broad and relied on data input by staff in the catheter laboratory, which changed in ‘real-time’. This made identification of some eligible patients difficult. For example, if a patient was originally marked as ‘PPCI’ but was subsequently found to have an unobstructed coronary artery on the angiogram, the ‘PPCI’ label was removed, but no further information was added, necessitating manual searches by local research staff to identify whether or not the patient was eligible.

We gave eligible patients information about the study once they had recovered sufficiently from their angiography and consented them before they were discharged from hospital (see Appendix 1 for a copy of the consent form). In some hospitals, this was often not possible because of quick repatriation to the originating hospital or research staff not being available over the weekend. Patients who could not be approached in person were sent study information and consent forms by post. Postal consents were managed locally at each hospital. We requested consent to use all the information collected by the hospital in relation to the patients’ suspected MI (index admission) and all the information related to subsequent NHS care and vital status in the first 12 months following the index admission. 27

Exposure

Patients were classified as exposed if they received CMR within 10 weeks of the index admission (whether during the index admission or subsequently as an outpatient). CMR had to be documented (i.e. CMR report, including the date of the CMR) in the clinical radiology information system (CRIS) or similar imaging database when the date/time of the CMR was < 10 weeks (70 days) after the date of the index admission.

Data collection: index procedure

We asked each hospital to provide the following data from local HISs for all recruited patients:

• basic demography [local Patient Administration System (PAS)/British Cardiovascular Intervention Society (BCIS) Central Cardiac Audit Database (CCAD)]

• clinical characteristics on presentation at the index admission, peri- and post-procedural (PPCI) characteristics {local catheter laboratory database [e.g. Siemens (Munich, Germany) CARDDAS in Bristol]/BCIS-CCAD}

• echocardiography (ECHO) and CMR reports (local imaging databases, e.g. Picture Archiving and Communication System, CRIS, CRIS in Bristol)

• biochemistry [local biochemistry databases, e.g. Virtual Pathology Laboratory System/Integrated Clinical Environment (ICE) in Bristol]

• medications on discharge (local biochemistry database, e.g. ICE in Bristol).

Each hospital had the option to submit a linked data set (i.e. the participating hospital linked data from different HISs within the hospital) or unlinked data extracts from each of the above categories with a unique patient identifier in each extract. We established a data linkage model at Bristol (by liaising with the appropriate HIS managers at University Hospitals Bristol NHS Foundation Trust) and informed the other hospitals that we would undertake linkage at Bristol based on this model, although the decision about how to submit the data was left to each participating hospital. 27

We collected a limited anonymised data set to characterise the eligible patients who were not recruited, that is those who declined to consent or did not respond to an invitation to consent before study recruitment ended. Hospitals were provided with a look-up table containing the Index of Multiple Deprivation (IMD)28 or Welsh Index of Multiple Deprivation (WIMD)29 for English or Welsh postcodes, so that the anonymised data sets could contain information on deprivation, both as scores and ranks, without including postcodes, which might have compromised anonymity. IMD and WIMD are calculated from combined information from several domains (e.g. income, employment, education, health) to produce an overall relative measure of deprivation.

Data collection: follow-up

For subsequent inpatient and outpatient activity in the 12 months following the index admission, we applied to NHS Digital to link our data set with HES (i.e. inpatient, outpatient, A&E and critical care data) for the hospitals in England and to NHS Wales Information Service (NWIS) for PEDW (which collects equivalent information for NHS Wales hospitals) for the hospitals in Wales. Inpatient episode data (HES and PEDW) contain details of hospital admissions (including dates of admission and discharge) and main diagnoses/procedures. Outpatient data contain appointment dates and consultant specialty. A&E data contain dates, methods and sources of arrival at hospital.

NHS Digital routinely links HES with ONS mortality data. Mortality data were unavailable through NWIS, so the two Welsh hospitals provided these data for their patients. Identifiers and evidence of signed consent were submitted via a secure portal to NHS Digital and NWIS; HES, ONS and PEDW data were received with our unique patient identifier embedded within each file for linkage.

Admissions data were provided as data relating to an episode (i.e. admission under one consultant), but we restructured the data to relate to a ‘continuous inpatient spell’ regardless of any transfers to another consultant that may have taken place within the same admission. 30 We created a data set containing the admissions data that included the index admission and the admissions, outpatient, critical care and A&E data in the 12 months after the index admission. This was used to characterise the frequency and duration of outpatient follow-up, unscheduled cardiac-related hospital readmissions and cardiac-related investigations/procedures, such as diagnostic angiography, repeat PCI or cardiac surgery up to 1 year after the index admission.

We assumed that, if patients had any HES/PEDW data (e.g. admissions, outpatient and A&E), data not recorded in any of the data sets were absent (i.e. patient did not have an episode) rather than missing (i.e. data not entered). In addition, for those patients with a CMR within 10 weeks of their index admission, outpatient visits that occurred within this window were excluded from any counts.

Main outcome measures

The main prespecified outcome measures were feasibility parameters (criteria that would have to be met before making the decision to progress to setting up a full registry):

• patient consent implemented at all four hospitals

• data linkage and extraction from multiple local HISs achieved for > 90% of consented patients at all four hospitals

• local data successfully linked with HES and PEDW for > 90% of consented patients at all four hospitals

• CMR scan requested and carried out for ≥ 10% of patients activating the PPCI pathway in CMR hospitals. 27

Feasibility of compiling the registry from hospital episode data

We tested the feasibility of compiling the registry entirely through hospital episode data. We determined whether or not we could identify the following from hospital episode data:

• the index event (PPCI or emergency angiography, cohort entry)

• CMR within 10 weeks of the index event (exposure)

• relevant subgroups of the population in which CMR may influence clinical management [identified through formal consensus, see Formal consensus study (objectives C1 and C2)]

• clinical outcomes [MACEs, comprising death, MI, stroke, repeat PCI and a coronary artery bypass graft (CABG)].

We identified events corresponding to the index admission, exposure or outcome in HES and PEDW data sets (inpatient and outpatient) using the International Classification of Diseases, Tenth Edition (ICD-10)31 and the Office of Population Censuses and Surveys (OPCS) classification procedure codes. 32 The index admission was identified from the inpatient data set using the OPCS codes for PCI (K49, K50, K75) and ICD-10 codes for ST-elevation myocardial infarction (STEMI) (I21.0, I21.1, I21.2, I21.3) to identify PPCI patients, and OPCS codes for angiography (K63.3, K63.5 or K63.6) with a method of admission coded as emergency to identify patients who underwent an emergency angiogram. We identified exposure (OPCS code U10.3) in the inpatient and outpatient data sets. We regarded all procedure codes within 1 day of the index admission (PCI and emergency angiography) or subsequent CMR to relate to the same event recorded in our HIS data set. Clinical outcomes were identified using the following codes: ICD-10 I21 for MI and I64 for stroke, and OPCS K49, K50 or K75 for PCI and OPCS K40, K41, K42, K43, K44, K45 or K46 for CABG. A full list of codes is shown in Appendix 2.

Sample size considerations

This was a feasibility study. We aimed to recruit sufficient patients to address the study objectives, not to provide a definitive answer to questions about the clinical effectiveness and cost-effectiveness of CMR in patients who activate the PPCI pathway. None of the objectives was analytic and, therefore, a power-based sample size justification was not appropriate.

Objective A2 and objective B1 required quantitative estimates to be reported. The target sample size of 1600 patients was assessed as sufficient to allow these proportions to be estimated with satisfactory precision. For example, we expected that ≥ 90% of patients who were invited to take part in the study would give consent; the 95% confidence interval (CI) for this proportion would be 88.4% to 91.4%. Similarly, we estimated that 12.5% of the target population would have CMR, although this proportion was less certain. The 95% CIs for proportions 5%, 10% and 15% would be, respectively, 4.0% to 6.2%, 8.6% to 11.6% and 13.3% to 16.8%.

Data analysis

Data were analysed using Stata/IC^® version 13 (StataCorp LP, College Station, TX, USA). The analysis of feasibility outcomes was descriptive, with quantitative results expressed as counts, percentages and 95% CIs. Feasibility was assessed by comparing descriptive statistics with the corresponding criteria. We compared patients who did not consent (and for whom we received anonymised data) with those who did consent using standardised mean differences (SMDs). 27,33 We used means and standard deviations (SDs), or medians and interquartile ranges (IQRs), for continuous variables and number (per cent) for categorical variables to report the demographic variables and baseline characteristics of patients recruited from the four hospitals.

Clinical events were expressed as rates per 1000 person-years at risk, along with 95% CIs calculated using the Poisson distribution. Time at risk of death was calculated as the time from index admission to death for those known to have died within 12 months, or the time from index admission to the calendar date on which death data were provided for that site. Any time at risk of death of > 12 months was capped at 12 months.

Time at risk for MI, stroke, repeat PCI and CABG was calculated as the time from index admission to the event, or the time from index admission to the calendar date that follow-up data were provided for that site. If the patient died before experiencing the event, the time at risk of death was time from index admission until death. Any time at risk of death of > 12 months was capped at 12 months.

Literature review

We searched for studies reporting the impact of CMR on prognosis, patient management and risk stratification in the population of patients who activate the PPCI pathway. We intended to identify studies providing background knowledge to inform the formulation of statements about how CMR could change patient management in specific subgroups and as supporting evidence for the final consensus statements. We did not intend to carry out a formal quantitative systematic review of the effectiveness of CMR in the target population.

We searched without restriction by study design or search terms related to outcomes, so that we could determine the full extent of the literature in this area and identify all studies that used CMR in our population. Thus, the search identified studies that had diverse objectives and measured a range of outcomes (e.g. clinical outcomes, changes in patient management, classifications of patients into prognostic groups, new diagnoses). The search was conducted on MEDLINE, EMBASE, The Cochrane Library, Web of Science (Citations Index and Proceedings) and Bioscience Information Service (BIOSIS) (date range for all searches: 1950 to 16 January 2014). The following search terms were used, both as free text and medical subject headings (MeSH) when possible: ‘acute coronary syndrome’, ‘myocardial infarction’, ‘angioplasty’, ‘percutaneous coronary intervention’ and ‘cardiovascular magnetic resonance imaging’. We applied no restriction on publication date or language. The literature search is shown in Appendix 3.

We included all studies reporting the use of CMR in patients undergoing PPCI or patients who activated the PPCI pathway but did not proceed to PPCI (e.g. had an emergency angiogram). We excluded studies investigating the use of CMR in populations with stable coronary artery disease or patients with non-ST elevation myocardial infarction (NSTEMI), because these patients do not routinely undergo emergency angiography. One researcher triaged the titles and abstracts identified by the search to remove duplicate reports of the same study and studies that did not meet the inclusion criteria. Reference lists of full-text papers were scanned to ensure that no additional studies had been missed during the search.

The broad search identified many different types of study (e.g. diagnostic, prognostic, controlled or uncontrolled, prospective or retrospective, cohort studies) and case reports. They were grouped as follows: (1) studies assessing the prognostic value of a comprehensive CMR scan or specific CMR markers, (2) studies in which CMR was used to ‘diagnose’ a specific feature (e.g. type of MI) or complication arising from the index MI [e.g. left ventricular (LV) thrombus, ventricular septal defect (VSD)], or procedure-related complications, (3) studies in which CMR was used to ‘diagnose’ incidental findings or conditions unrelated to MI, (4) studies in which indices of heart function (e.g. myocardial viability, necrosis, perfusion, myocardium at risk, ischaemia) assessed by CMR and other imaging modalities [e.g. ECHO and single-photon emission computed tomography (SPECT)] were compared and (5) other studies not fitting into any of the categories above.

Formulation of consensus statements

An expert group was convened, consisting of two cardiologists with CMR expertise (CBD and JPG), two interventional cardiologists from sites participating in the feasibility study (SHD and RAA), one cardiac network director (SB), two methodologists (BCR and MP), two statisticians (CAR and JMH), one health economist (SW or EAS) and the study manager (RCB). Draft statements were generated independently by three cardiologists (CBD, JPG and SHD) based on their cardiological expertise and by one methodologist (MP) based on evidence from the literature review. The cardiologists and methodologist had no prior communication with each other. The cardiologists were advised to be as inclusive as possible at this stage and to not be concerned about the strength or quality of the evidence available to support their draft statements.

Three members of the expert group (study manager, RCB; systematic reviewer, MP; and cardiologist with CMR expertise, CBD) collated the statements, organised them according to patient subgroup and standardised the wording of each statement. A 1-day meeting was organised for all members of the group. The structure of the meeting was as follows: an introduction to the aims of the study and the formal consensus process; a review of each statement in turn, considering the relevance, format and wording of each statement; and a discussion about the structure of the survey. Key papers from the literature review were circulated to all members of the group prior to the meeting and these were also considered in the discussions. At the meeting, it was decided that each statement would benefit from a ‘supporting paragraph’, citing key references from the literature review, to provide background information and put the statement in context.

The structure of the process used to generate the statements is summarised in Table 1. We documented all of the discussions that led to the final statements.

Survey design

Statements and supporting paragraphs were worded in a consistent manner and collated in the form of a web-based survey (SurveyMonkey^®, Palo Alto, CA, USA) (see Appendix 4). The survey included an introductory page explaining the purpose and layout of the survey and instructions about how to complete it. Each statement was followed by its supporting paragraph that contained links to Portable Document Format (PDF) references. Each statement was accompanied by a nine-point Likert scale asking the respondent to indicate whether or not he/she agreed with the statement (with 1 indicating ‘completely disagree’ and 9 ‘completely agree’). There was also a free-text box for each statement for respondents to comment in and justify their score.

Expert panel

Extension of survey to other UK cardiologists

The survey was extended to other UK cardiologists through the British Cardiovascular Society, which advertised the survey in their monthly newsletter to members (over 2 consecutive months). We offered an incentive to encourage participation: the option of entering a prize draw for an iPad (Apple Inc., Cupertino, CA, USA).

Main outcome measure

A definition of a primary outcome measure (acceptable to cardiologists and other stakeholders) that represents a definitive change in clinical management arising from a patient having CMR.

Criteria for consensus

Data for each statement are shown as medians and IQRs. A median score ≥ 7 and an IQRs 6–9 was considered to be in agreement, or consensus, that the change in management described by the statement was clinically important. These statements were used to identify the patient subgroups perceived to benefit from CMR and define the treatment/process outcome that constitutes a definitive management change as a result of having CMR. A median score ≤ 3 and an IQR 1–3 was considered to be in consensus that the statement did not constitute a clinically important change in management. Data were analysed using Stata/IC version 14.

Patient subgroups

Patient subgroups were identified from HIS data extracts provided by hospitals. When this was not possible, hospital episode data (the admissions data sets) were used to identify the ICD-10 diagnostic codes associated with the index admission.

Manual review of patient notes

We undertook a review of patient notes because of the uncertainty regarding the identification of the subgroup of patients with unobstructed coronary arteries. Forty-seven patients were selected, all identified as having unobstructed arteries in hospital A at the time of the index event. We undertook a thorough review of their patient notes to identify the reason why they activated the PPCI pathway.

Identifying changes in management

We used HES/PEDW inpatient and outpatient data to identify changes in management defined through the formal consensus in the 12 months after the index admission (cohort entry). For those patients who underwent CMR, any outpatient appointments that occurred between the index admission and the date of CMR were excluded. We decided not to use the A&E data set because most of the changes in management identified in the formal consensus were unlikely to occur in the A&E setting.

Cardiologist members of the clinical team identified key clinical ‘events’ that they expected to reflect the important changes in management characterised through the consensus process. For example, new non-ischaemic diagnoses (an important change in management resulting from CMR) were identified as Takotsubo cardiomyopathy, myocarditis, pericarditis, endocarditis and coronary spasm.

We then compiled a list of ICD-10 and OPCS codes representing the key clinical events with the help of a clinical coder. Most changes in management encompassed a number of ICD-10/OPCS codes, for example the use of additional diagnostic tests during follow-up required OPCS codes for positron emission tomography (PET), ECHO, intravascular ultrasound (IVUS) or pressure wire. We identified the changes in management in HES/PEDW for each statement (patient subgroup) separately. The full code list used to identify changes in management is shown in Appendix 2.

Implementation of the consensus statements

We calculated the frequency of all events representing changes in management (e.g. number of new diagnoses, additional diagnostic tests, number of outpatient appointments) in patients who did/did not receive CMR for each patient subgroup in the 12 months following the index admission. We also calculated the time to first event for each change in management (e.g. time to first new diagnosis, time to the first diagnostic test, time to next revascularisation in patients with multivessel disease) and summarised these with medians and IQRs. We did not compare differences between patients who did/did not receive CMR in any of the subgroups because we were interested primarily in the feasibility of identifying relevant events; the number of patients in most subgroups was also small.

Cardiovascular magnetic resonance questionnaire

We determined whether or not the changes in management identified in HES/PEDW accurately reflected the changes in management implemented by the cardiologists who referred patients in the study for CMR. We designed a questionnaire based on the patient subgroups identified in the formal consensus (see Appendix 5) and asked all referring cardiologists to complete it for each patient whom they referred for CMR. The questionnaire asked about whether or not and how CMR changed management (using tick boxes and free text). We used the questionnaire to identify whether or not the changes in management reported by cardiologists were accurately reflected in HES/PEDW, and whether or not there were additional changes in management that could not be picked up from HES/PEDW.

Model 1: patients with multivessel disease

This model was designed to address the question ‘Which is the most cost-effective type of ischaemia testing for patients with multivessel disease who activate the PPCI pathway?’

When the study started, stress ECHO was the ischaemia-testing option commonly used and the study was comparing this with CMR. During the study, the use of FFR increased35 and, therefore, this test was included as an additional arm in the model. Thus, the model compares the cost-effectiveness of three ischaemia testing options: (1) CMR, (2) pressure wire and (3) stress ECHO.

The cohort of patients of interest are those who activate the PPCI pathway, have their index angiography and PPCI, and are identified as having multivessel disease (commonly defined as stenosis of > 50% from the angiogram). This model considers the patient pathway thereafter: whether or not to treat non-culprit lesions with a second revascularisation, the need (or not) for ischaemia testing to help determine the need for the revascularisation of a second vessel and any MACEs that occur. Figure 1 summarises the scenarios in the patient pathways included in the model structure for one arm of the model (CMR).

FIGURE 1.

Scenarios in the patient pathway included in the model structure for patients with multivessel disease.

The model structure (shown in detail in Figure 2 splitting into three arms, representing the three different types of ischaemia testing) considered the following: the top branch models the scenario in which the ischaemia-testing option available is CMR, the middle branch models the scenario for pressure wire and the bottom branch models the scenario for stress ECHO. The structure of each of these three main arms is the same for each scenario, but the associated probabilities, costs and outcomes for each arm vary.

FIGURE 2.

Model 1 structure: patients with multivessel disease.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Considering the CMR arm, the decision tree first divides the starting cohort of patients after PPCI in accordance with the results of their index angiography. A clinician may determine that a patient requires or does not require the revascularisation of a second vessel directly from their angiogram, without the need for ischaemia testing; alternatively, there may be uncertainty from the index angiogram of the need for further revascularisation, and, therefore, ischaemia testing is used, in this case CMR. For each of these three categories, patients are then divided by whether or not they truly have ischaemia.

Patients whose index angiography leads to a firm decision on revascularisation are then divided by whether or not they had any MACEs in the 12 months after PPCI. MACE is a composite end point of all-cause mortality, MI, stroke and revascularisation.

Patients for whom ischaemia testing is needed are divided in accordance with whether that test result was positive, and therefore assumed to result in the revascularisation of a second vessel, or negative. Thereafter, patients are divided by whether or not they had a MACE in the 12 months after PPCI.

Although the model structure is the same for each of the ischaemia testing options, there are differences in the timing of events between these arms for patients who have ischaemia testing that leads to a second revascularisation. For CMR and stress ECHO, this is a two-step process, that is a patient has the ischaemia test and then separately has revascularisation of a second vessel. For pressure wire, this all happens in the catheterisation laboratory at the same time, that is a patient has their diagnostic angiography and pressure wire, and, if required, has their PCI at the same time (note that this is entirely separate and subsequent to their index angiography and PPCI). This affects costs, but not the model structure.

Model 2: patients with normal coronary arteries

For a starting cohort of patients with normal coronary arteries, this model compares standard ECHO and CMR with standard ECHO only, and is designed to address the question: ‘Is it cost-effective to introduce CMR in patients with normal coronaries who activate the PPCI pathway?’. For the standard ECHO and CMR arm of the model, Figure 3 summarises the scenarios in the patient pathways included in the model structure.

FIGURE 3.

Scenarios in the patient pathway included in the model structure for patients with normal coronaries.

The model structure, shown in detail in Figure 4, is split into two main arms (branches): the top branch represents standard ECHO and CMR and the bottom branch represents standard ECHO only. The structure is the same in each arm, and starts by dividing patients into those who truly did have a MI and those who truly did not. Patients are then divided according to their ischaemia test result. For those with a positive test result, and therefore assumed to have had a MI, treatment for MI in the form of cardiac medications and cardiac rehabilitation will be given. Those with a negative test result will be treated for other causes of their chest pain, and given fewer cardiac medications or none at all. Thereafter, patients are divided into whether or not they had a MACE within 12 months. A MACE is defined in the same way as above: a composite end point of all-cause mortality, MI, stroke and revascularisation.

FIGURE 4.

Model structure for patients with normal coronaries (no blocked arteries).

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Model analyses

All analyses were conducted in TreeAge Pro 2013 (TreeAge Software Inc., Williamstown, MA, USA). Costs and QALYs were not discounted, as the time horizon was 1 year. Cost-effectiveness analyses were run, and the expected costs and QALYs associated with each arm (each ischaemia testing strategy) to 1 year were calculated. The incremental cost-effectiveness ratio (ICER) was derived from the difference in costs and QALYs between each ischaemia testing option and current practice. The ICER is the cost per QALY gained of introducing a different form of ischaemia testing compared with current practice.

As this is a feasibility study, it is not the actual ICER and cost-effectiveness results that are the main interest, but rather how the results change in response to sensitivity analyses; this will identify the key drivers of cost-effectiveness that would require detailed measurement and consideration in any subsequent work.

Sensitivity analyses

Both one-way sensitivity analyses and PSAs were conducted. One-way sensitivity analyses were used to assess the sensitivity of the base-case results to key uncertainties in the model to identify the key drivers of cost-effectiveness. Tables 8 and 9 describe the key sensitivity analyses that were conducted for each model. Threshold analyses were also conducted around the costs of CMR, and angiography and pressure wire to establish the cost of the test at which the costs associated with alternative strategies were the same.

To quantify the uncertainty around the one-way sensitivity analyses, parameters were assigned distributions (rather than fixed values) so that SEs and CIs could be estimated; this was done in the same way as the PSAs that were conducted. PSAs were used to investigate the joint impact of results of all uncertain parameters in the model simultaneously.

Model parameters were assigned probability distributions to describe the range of plausible values that they could take based on the precision of the estimates available. The type of distribution assigned was matched to the nature of the parameter; for example, a beta distribution lies between zero and one and is, therefore, appropriate for parameterising the probability of two mutually exclusive events. The type of distribution assigned to each parameter was described with the model inputs above.

Randomly selected values from each distribution for each model parameter were simultaneously generated using Monte Carlo simulation; this was repeated 1000 times and the results were calculated for each run of the model, generating a distribution of costs and effects that were used to calculate 95% CIs around the cost and QALY differences between testing strategies, enabling the effect of the parameter uncertainty on results to be investigated.

Feasibility prospective cohort study

There were two main changes to the study design, summarised below:

1. We included the option of postal consent to the recruitment protocol, mainly to be able to catch patients who were repatriated quickly to local hospitals after the index procedure.

2. We followed up patients through routinely collected hospital episode data (HES for English hospitals and PEDW for Welsh hospitals). We originally intended to obtain follow-up data for 12 months after the index procedure from each hospital. However, this was not feasible for many patients, given that they were repatriated to/followed up at a different hospital from where they had their index procedure.

Formal consensus study

There were four main changes related to the formal consensus study, summarised below:

1. We had intended to include different stakeholders [e.g. cardiologists, general practitioners (GPs) and patient representatives] in the consensus process. However, as the consensus statements developed it became evident that these were highly specialised and it would have been unreasonable to expect those without cardiology expertise to rate and discuss them. Therefore, the consensus process included only cardiologists.

2. We did not originally intend to give the final survey to cardiologists who did not participate in the consensus process. However, we had fewer than anticipated cardiologists in the consensus panel; therefore, to avoid any criticism of the number of panel members in our study and to prevent the possibility of introducing bias (given the self-selected nature of the panel), we decided to extend the survey to UK cardiologists who did not participate in the formal consensus process.

3. We had originally intended to implement our primary outcome using follow-up data from local HISs. However, given the difficulties of obtaining local follow-up data (highlighted in Feasibility prospective cohort study, point 2), we used HES follow-up data instead.

4. We designed and gave a questionnaire to all cardiologists who referred patients for CMR (in our two centres with dedicated CMR facilities), which requested information about how CMR changed the management of those patients. We determined whether or not changes in management described in the questionnaire were adequately reflected in hospital episode data (see Chapter 6, Implementation of a primary outcome based on the changes in management for which consensus was achieved).

Health economic study

We intended to use data collected as part of the PIPA study, particularly patient episode data from HES and the PEDW. However, because the HES data were not available in time to do this, an alternative approach was required. Secondary sources of data were sought and used as inputs into economic decision models used to identify the key drivers of cost-effectiveness for CMR imaging in patients who activate the PPCI pathway, that is, the model parameters that have the most influence.

Identifying the eligible population

Patients who activated the PPCI pathway and received PCI were easy to identify from catheter laboratory databases, since consistent terminology was used to define these patients. A proportion of these patients recruited in the study were identified independently of these databases. Patients who activated the PPCI pathway but did not receive PPCI were not consistently defined in catheter laboratory databases; these patients required further manual checks by catheter laboratory staff who assisted the research nurses in identifying eligible patients. Some of these patients were screened out, possibly because of (1) misunderstanding by catheter laboratory staff or research nurses over eligibility (especially at the beginning of the study) and (2) the lack of consistent terminology used to describe these patients in catheter laboratory databases, leading to confusion about whether or not to include them.

Implementing consent

We asked all participating hospitals to implement a conventional system of written consent so that we could establish a consent rate and ensure that we had consent to obtain follow-up data from HES and PEDW. We met the feasibility objective of implementing conventional prospective consent at all hospitals. Consent rates ranged from 59% to 74%27 with respect to numbers considered eligible to participate, and from 22% to 86% with respect to approximate BCIS volume in the period of recruitment; these percentages were lower for some hospitals than we had anticipated for a study in which participants have no active involvement. The main reason for the lower than expected consent rate, and the variation between hospitals, was that many patients identified as eligible were missed; PPCI patients are asleep immediately following the procedure, rarely confined to bed after the first 4–6 hours and many are discharged within 24 hours or transferred to referring hospitals. Some of the missed patients activated the PPCI pathway at the weekend, when research nurses were not always available. In some centres, patients did not get admitted to the critical care ward or general ward after the PPCI procedure. We implemented postal consent to invite identified eligible patients who were not approached in hospital, but return rates were low. Some patients were unable to cope with the emergency nature of their condition and the large amount of new information they were given as part of their care; for some, the request to participate in a research study seemed to be yet another thing to think about and it was easier for them to decline to take part.

Data linkage and extraction across hospitals

We did not meet the feasibility criterion for data linkage and extraction from HISs for all of the requested data sets. We prespecified that, for each participating hospital, the data items requested should be available (i.e. extracted from sources and linked) for > 90% of patients. Only one hospital provided all of the requested data sets (anonymised, clinical, ECHO, biochemistry and medication on discharge) for > 80% of eligible (for the anonymised data set) and consented patients. Some hospitals had entire data sets missing: medications on discharge were not available from hospitals B, C and D; information about ECHO tests was not available from hospital B; and biochemistry data were not available from hospital D. There were several reasons for missing data, for example:

• Some hospitals were unable to extract data from local stand-alone databases that were designed for a specific purpose, for example logging biochemistry requests or storing and presenting test results. 27 Such local databases were typically old and did not have an interface for data extraction; data would have had to be retrieved manually for each patient in turn, rather than submitting a ‘batch’ query. The study was not resourced for this activity. Moreover, obtaining data in this way was considered infeasible for a national registry (as well as time consuming) and hospitals were not requested to do so.

• Some data (e.g. medications on discharge, bedside ECHO) were not recorded electronically and were available only in paper form (sometimes scanned but not coded/searchable).

• Non-bedside imaging tests (ECHO and CMR) were available electronically but as free-text reports rather than coded data; variables of interest were impossible to extract from these reports by conventional text-searching methods because of the variability with which the information was recorded. 27

Cardiovascular magnetic resonance exposure

We were able to obtain CMR reports for all consented patients, although imaging reports were largely in free-text format, so individual CMR parameters would have had to be extracted manually. We concluded that this would not be feasible for an extended registry.

Follow-up/outcome data

It would be feasible to obtain 12 months’ follow-up data after the index admission through HES in an extended registry. Admissions (inpatient data set), outpatient appointments and A&E data could be identified for all patients for whom a NHS number was available and who had a date recorded for their index admission. Because we obtained hospital episode data only for patients who we believed to be eligible, we cannot comment on the risk that eligible patients might not have been identified.

Compiling the registry from Hospital Episode Statistics

It is not currently feasible to compile the registry from HES data rather than multiple HISs. Although we could identify the eligible population and obtain follow-up data, we could not identify the CMR exposure within 10 weeks of the index admission. This was mainly because the CMR is often performed at an outpatient clinic and the HES outpatient data quality, particularly for clinical information, is poor (i.e. lack of data on diagnoses or procedures).

Multivessel disease

The expected costs and QALYs gained under each of the ischaemia testing options are shown in Table 18 together with the cost-effectiveness results for CMR and pressure wire, each compared with stress ECHO. Comparisons are made with stress ECHO because this was the ischaemia test in use at the time of the study (and is considered the ‘existing’ comparator) and also because CMR and pressure wire were assumed to be equally effective; these strategies differ only in terms of their costs. Based on the point estimates, CMR was slightly less costly and more effective than stress ECHO and, therefore, dominated stress ECHO. Pressure wire was more costly and more effective than stress ECHO: the ICER is considerably higher than the accepted cost-effectiveness threshold of £20,000–30,000 per QALY, and, therefore, would not be considered cost-effective relative to stress ECHO. Pressure wire is convenient for cardiologists if the test identifies the fact that a patient needs revascularisation, as the patient can be treated immediately while in the catheter laboratory, and the cost of the pressure wire in addition to the revascularisation is modest. In the base-case analysis, 35% of patients having ischaemia testing were assumed to truly have ischaemia; the majority of patients are, therefore, having an expensive test without needing further treatment. If a greater proportion of patients undergoing ischaemia testing required revascularisation, it is likely that the estimated cost of a pressure-wire strategy would reduce. It would be possible to use a pressure wire on other vessels at the time of the index angiography and PCI, but it was beyond the scope of this work to consider the likely costs and consequences of such a strategy. Based on the point estimates, pressure wire is dominated by CMR, because CMR is less costly and equally as effective. Cost and QALY differences are small relative to the mean values under each strategy, and none of the differences in costs or QALYs is close to being statistically significant; indeed, the differences in QALYs were tiny. There is considerable uncertainty around these base-case results; however, as is clear from the plots of 1000 simulated cost and QALY differences from the PSA for CMR and pressure wire (each compared with stress ECHO) in Figures 10 and 11. In each figure, there are a large number of points in two or three quadrants of the cost-effectiveness plane. The black dot is the point estimate of the cost and QALY difference, and, in each figure, it is close to the origin.

FIGURE 10.

A plot of the 1000 simulated cost and QALY differences from the PSA for CMR vs. stress ECHO.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

FIGURE 11.

A plot of the 1000 simulated cost and QALY differences from the PSA for pressure wire vs. stress ECHO.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

For this feasibility study, it is not the cost-effectiveness results per se that are of interest, but rather the way in which they change in response to sensitivity analyses that helps us to determine the key drivers of cost-effectiveness. Table 19 reports the results of the sensitivity analyses around this model. Sensitivity analysis one, around the probabilities of ischaemia testing or a decision to revascularise a second vessel (or not) directly from the index angiography, affects the mean costs and QALYs under each strategy but does not alter the cost-effectiveness results, because these probabilities are the same under each strategy. Sensitivity analyses two and three, around the diagnostic accuracy of CMR and pressure wire, had the most effect on results. When the sensitivity and specificity of CMR were reduced to 80%, CMR became more costly than stress ECHO, rather than dominant, and, at accepted cost-effectiveness thresholds, would not be considered cost-effective relative to stress ECHO. When the sensitivity and specificity of pressure wire were reduced to 80%, conclusions did not alter: pressure wire was still more costly and slightly more effective than stress ECHO, but the ICER quadrupled. The diagnostic accuracy of the ischaemia tests, in particular CMR, are key drivers of cost-effectiveness. Varying the costs of CMR, angiography and pressure wire, and varying QALYs for patients with MACE and those without MACE, did cause cost-effectiveness results to vary, but did not alter cost-effectiveness conclusions.

An alternative way of assessing the sensitivity of results to the cost of an ischaemia test is to consider how the cost of the test would need to alter for the costs of alternative strategies to be estimated to be equal. In the base-case analyses, the cost of CMR was £264, and a CMR strategy was less costly than a stress ECHO strategy. Threshold analyses around the cost of CMR identified that if the cost rose to £371, there was no difference in the costs between a CMR and a stress ECHO strategy. In the base-case analyses, the cost of angiography and pressure wire was £1340, and a pressure-wire strategy was more expensive than a CMR or stress ECHO strategy. Threshold analyses identified that the cost of angiography and pressure wire would need to reduce to just £250 for there to be no difference in costs between a CMR and a pressure-wire strategy, and to £415 for there to be no difference between the cost of a stress ECHO and a pressure-wire strategy.

Normal (unobstructed) coronary arteries

The expected costs, QALYs and cost-effectiveness results under standard ECHO and CMR compared with standard ECHO alone for patients with normal coronaries are shown in Table 20. The reduction in costs associated with treating fewer patients for MI with the introduction of CMR testing compensated only partially for the additional cost of the CMR test. Mean costs and QALYs gained were higher with standard ECHO and CMR than with standard ECHO alone, and the ICER would not be considered cost-effective at accepted thresholds. There is considerable uncertainty around these base-case results, however, as is clear from the plot of the simulated cost and QALY differences from the PSA in Figure 12. There are a large number of points in three quadrants of the cost-effectiveness plane.

FIGURE 12.

A plot of the 1000 simulated cost and QALY differences from the PSA for standard ECHO and CMR vs. standard ECHO.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Reproduced with permission from Stokes et al. 36 © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

Table 21 reports the results of the sensitivity analyses around this model, designed to help identify the key drivers of cost-effectiveness. Sensitivity analysis one (reducing the diagnostic accuracy of CMR) and sensitivity analysis six (increasing the cost of CMR) had the greatest impact on the mean cost difference between standard ECHO and CMR and standard ECHO alone. Threshold analyses around the cost of CMR (£264 in the base-case analyses) identified that, if the cost reduced to £166, there was no difference in the costs between standard ECHO and CMR and a standard ECHO alone strategy. Sensitivity analyses 8–10, which all directly varied QALYs, had the greatest impact on the difference in QALYs between the arms. However, in all cases, standard ECHO and CMR remained both more costly and more effective than standard ECHO alone, and in none of the sensitivity analyses would standard ECHO and CMR be considered cost-effective at accepted thresholds. These sensitivity analyses suggest that the diagnostic accuracy of CMR is again a key driver of cost-effectiveness. Results are also influenced by the cost of CMR, and the difference between utility values assigned to MACE and no MACE, and the proportion of patients who have MACE, who are assumed to die.

Cohort characteristics and follow-up

Baseline characteristics were reasonably well balanced between participants who consented and those who did not consent in the study, but suggested that patients living in less deprived residential areas were, on average, less willing to take part. Characteristics were better balanced across participants recruited by the four hospitals, and participants who did and those who did not receive a CMR scan. Data completeness was good for most variables. Follow-up data were available for the entire follow-up period (12 months) for only 729 out of 1035 (70%) participants in England and 287 out of 635 (45%) participants in Wales. This is because data were requested soon after recruitment finished and the Welsh data were provided rapidly. The data for English hospitals took much longer to be released, resulting in a longer follow-up period. The limited follow-up was a result of this process being part of the testing feasibility for a full registry; in the context of a registry, the request for these data would be made at an appropriate stage of the study to achieve the desired duration of follow-up.

The proportion of the target population that gets cardiovascular magnetic resonance after activating the primary percutaneous coronary intervention pathway

Although > 10% of patients in hospitals with a dedicated CMR facility were referred for CMR, many of these referrals were for research (patient participating in a research study) rather than clinical purposes. It is not clear whether or not this reflects practice in other hospitals with dedicated CMR facilities. Referral for non-clinical reasons (unless the findings are used to change clinical management) would dilute any observed effect of CMR on outcomes in an extended registry. It was not possible to determine how many CMRs that were done for research purposes were subsequently reviewed by the treating clinician and resulted in changes in patient management.

Cost-effectiveness models

For each of the base-case models, the differences in QALYs between strategies were tiny; therefore, the results are driven largely by the differences in costs, although these were also modest. Sensitivity analyses around the two models identified the diagnostic accuracy of the ischaemia tests as the key driver of cost-effectiveness. In the model for patients with multivessel disease, the costs of ischaemia testing and the QALYs associated with MACE and no MACE exerted an influence on cost-effectiveness results, but not to the same extent as the diagnostic accuracy of CMR and pressure wire. Similarly, in the model for patients with normal coronaries, the cost of CMR, the difference between QALYs associated with MACE and no MACE, and the proportion of patients who have MACE who are assumed to die, exerted an influence on the results.

Patient subgroups

We identified five patient subgroups (Table 24) for which there was consensus that CMR changed management in a clinically important way, either from HISs or from HES/PEDW.

• Patients who underwent PPCI could be identified both from HISs and from HES/PEDW.

• Patients with unobstructed arteries could not be directly identified from either HIS or HES, but they were indirectly identified from HIS (consented into study and underwent angiography but had no data in BCIS) and from HES/PEDW (had a record for angiography but no record for PCI).

• Patients with multivessel disease could only be identified reliably from HISs; the presence of disease in multiple vessels is not coded by ICD-10 but might be inferred from multiple procedure codes in HES/PEDW for coronary revascularisation (PCI or CABG).

• Patients who experienced OHCA, and PPCI patients who went on to develop LV thrombus, could be identified only from HES/PEDW.

The proportions of patients in each subgroup (across all hospitals and by hospital) are shown in Table 25. Across all hospitals, 89% of patients underwent PPCI, 11% were found to have unobstructed coronary arteries and 44% of patients had multivessel disease. The proportions of patients in each subgroup were similar between hospitals, except for patients with multivessel disease (19% in hospital D vs. 48%, 51% and 53% in hospitals A, B and C, respectively).

Manual review of patient notes

We conducted a manual review of patient notes because of the uncertainty regarding the identification of the unobstructed coronary arteries subgroup from HISs. Only 38 out of 47 (81%) patients were identified as having unobstructed coronary arteries in the manual review of patient notes (see Appendix 9). Of the remaining nine patients, six had a culprit lesion identified (therefore, the likely reason for their emergency angiography was expected to be MI), and the reason for the coronary angiography in the other three patients was unknown.

We compared the diagnoses from the patient notes with the primary diagnoses recorded in HES for the index admission (see Appendix 9). Four of the 38 patients identified as having unobstructed coronary arteries in their medical notes had ‘myocardial infraction’ as a primary diagnosis in HES. Of the six patients who had a culprit lesion identified in their medical notes, only two had ‘myocardial infraction’ recorded as their primary diagnosis in HES; two had a diagnosis of ‘chronic ischaemic heart disease’, one had a diagnosis of ‘pain in throat and chest’ and one had a diagnosis of ‘atrial fibrillation and flutter’.

Identifying changes in management in hospital episode data

The important changes in management identified from the statements in consensus, the patient subgroups these changes in management refer to, the HES/PEDW data sources used to identify them and the events identified from hospital episode data sources in the 12 months of follow-up after the index admission are shown in Appendix 10. Medication data are not available in hospital episode data; therefore, any changes in medication during follow-up could not be identified. This was particularly relevant for the LV thrombus after PPCI subgroup, for which the start of anticoagulation therapy was identified as the important change in management. All relevant non-ischaemic diagnoses were identifiable in hospital episode admissions data, as were procedures, such as implantation of devices, repeat revascularisation and additional diagnostic tests. In contrast, we could not identify these from the outpatient data set because ICD-10 and OPCS codes associated with each outpatient episode were missing for a large proportion of patients (25% of patients had missing diagnoses codes, 74% of patients had missing procedure codes and 74% of patients had ‘unknown and unspecified causes of morbidity’ as diagnosis). This will lead to an underestimation of diagnostic tests in the cohort, because most diagnostic tests are carried out on an outpatient basis. However, we could identify the frequency of outpatient appointments under the cardiology specialty.

Implementation of the consensus statements

The number of events representing changes in management in the relevant patient subgroups, identified in HES/PEDW up to 12 months after the index admission by CMR status are shown in Table 26. The frequency of events in patients who did and in those who did not have CMR was similar across all subgroups. Patients who had CMR had, on average, more outpatient appointments across all subgroups.

Table 27 shows the time to first event for each change in management and for all subgroups, by CMR status. Except for patients with unobstructed coronary arteries, patients in other subgroups who did not have CMR had an admission for an additional diagnostic test earlier than patients who had CMR. In the subgroup of patients with unobstructed coronary arteries, patients who did not have CMR had an admission related to a new non-ischaemic diagnosis earlier, and an admission for an additional diagnostic test and implantation of a device later than patients who did get CMR. In the PPCI/multivessel disease subgroups, time to first cardiology outpatient appointment was earlier for patients who did not have CMR than for patients who had CMR.

The frequency of events in CMR versus non-CMR hospitals is shown in Table 28. On average, patients in CMR centres had more outpatient appointments than patients in non-CMR centres. New diagnoses, additional diagnostic tests, implantation of devices and additional revascularisation in patients with multivessel disease were more frequent in CMR hospitals than in non-CMR hospitals. Time to the first event in CMR versus non-CMR centres for all events and by patient subgroup are shown in Table 29. In CMR centres, patients in most subgroups had an admission for an additional diagnostic test and implantation of a device later than patients in non-CMR centres. Time to first cardiology outpatient appointment was earlier for patients in CMR centres across all subgroups.

Feasibility of setting up a registry using hospital information system data

The outcome measures of the feasibility prospective cohort study were feasibility parameters to establish if (1) consent can be implemented at all four hospitals, (2) data linkage and extraction from multiple HISs can be achieved for > 90% of consented patients at all hospitals, (3) local data can be successfully linked with hospital episode data for > 90% of consented patients at all hospitals and (4) the proportion of patients activating the PPCI pathway who get a CMR scan is ≥ 10% in hospitals with dedicated CMR facilities.

The results suggest that the registry is feasible with respect to points 1, 3 and 4, albeit with some caveats, but not point 2. We found that recruitment and consenting of the patient population (using conventional consent), and linkage of local HIS data with hospital episode/vital status data are feasible. However, we experienced problems with identifying and approaching all eligible patients across all sites and we could not obtain all of the requested data from all hospitals. We had envisaged that it would be relatively straightforward to identify patients meeting the eligibility criteria (i.e. patients who activated the PPCI pathway, including those who had an emergency angiogram but did not receive PCI) consecutively, given the emergency nature of PPCI pathway activation. Indeed, we chose this population for a national CMR registry using routinely collected data because, at the outset, we expected that this population would be easy to identify from hospital databases. Although patients who received PPCI were easy to identify from catheter laboratory databases, not all eligible patients were identified in this way and many were identified manually by the research nurses.

The numbers of PPCI patients identified as eligible and approached at each hospital were lower than the numbers of PPCIs reported to BCIS by each hospital over the recruiting period (varying from 22% to 86% across the four hospitals; see Table 10). Some PPCI patients were missed because they were quickly repatriated to their local referring hospital. Other patients who arrived at the PPCI centre via A&E were not always classified as PPCI (e.g. patients who experienced out-of-hospital cardiac arrest), resulting in many of them being missed.

Patients who activated the PPCI pathway but had an emergency angiogram without PPCI (i.e. those with unobstructed coronary arteries) were difficult to identify from catheter laboratory databases. Contrary to expectation, there was no data field in any database capturing emergency admission to the catheter laboratory. These patients were also not described consistently in HIS databases and there was misunderstanding over eligibility (especially at the beginning of the study) by catheter laboratory staff and research nurses. Therefore, it is unclear what proportion of these patients was identified at each site during the recruiting period and how the proportion may have changed over time.

We met the feasibility objective of implementing conventional prospective consent at all hospitals, although consent rates were lower than anticipated for a study in which participants have no active involvement. The main reason for this was that many patients identified as eligible were missed (because of short hospital stay, no nurse cover at weekends, quick repatriation to referring hospitals, mobile inpatient population, etc.). There was also the issue of competing studies in the hospitals, including the perception that interventional studies should be given priority over observational studies. We relied on postal consent to invite patients identified as eligible and who were not approached in hospital, but return rates were low and we did not have the resources to send reminders or make telephone calls.

We explored alternative methods of obtaining consent as part of the feasibility study. We considered applying for approval to recruit patients without consent [‘presumed consent’, section 251 (of the NHS Act 200676) exemption] but concluded that this proposal would be unacceptable legally, given that, in principle, it is possible for patients to be asked to give consent for their data to be used for the study. 73 Exemptions are granted only on a temporary basis (pending implementation of consent) or when consent is impossible, not just infeasible. We considered the option of requesting consent at the same time as requesting procedural consent (e.g. by adding a paragraph to the standard NHS form, either opt-in or opt-out consent), although the emergency nature of the procedure means that written procedural consent is not always obtained and verbal assent is often recorded by hand in the patient notes. 27 Furthermore, we were unsuccessful previously when trying to introduce a research consent step into usual care; at the lead hospital, we could not get permission to alter the standard NHS consent form for one specialty and it was impossible to coordinate a change in the written procedural consent across all specialties. Although we were aware of at least one instance when a lead consultant had been able to integrate an opt-out consent model (to allow the use of routinely collected data for research) in the procedural consent form, this instance related to a diagnostic rather than an interventional context. The former setting has a more structured environment with fewer consultants and possibly less time pressure and we considered that an analogous process would be difficult to replicate in the PPCI environment, in which patients are critically ill and staff are under constant pressure to free beds for incoming patients.

Most prospective registries in the UK require explicit patient consent (e.g. National Joint Registry,74 Inflammatory Bowel Disease registry75), using stand-alone consent forms either with/without an approved ‘short text’ for obtaining consent that can be added to local consent forms. Participating hospitals are responsible for obtaining and recording patient consent with their uploads. However, the issue of missing information on consent is recognised and the National Joint Registry, for example, has dispensation [section 251 (of the NHS Act 200676) exemption] to record patient details when the indication for patient consent is ‘not recorded’, although this dispensation is granted on an annual review basis. Other registries, such as the cardiac audits under the umbrella of the National Institute for Cardiovascular Outcomes Research (NICOR) [which includes the Myocardial Ischaemia National Audit Project (MINAP) and the BCIS National Audit of Percutaneous Coronary Intervention (NAPCI)], have exemption under section 251 of the NHS Act 200676 to use patient information for medical research without consent. We cannot discern a rationale for the inconsistency in the consent arrangements for existing registries. Therefore, we are uncertain whether or not a future registry would be granted a section 251 (of the NHS Act 200676) exemption, as it is manifestly possible to implement prospective consent, as evidenced by some but not all existing registries.

A possible solution to the challenge of obtaining consent in the usual care setting would be to implement changes in NHS information technology (IT) administrative systems (ideally at the national level) to allow a record of verbal patient consent to be recorded in patients’ electronic notes (e.g. a ‘consent module’). The use of a system embedded within the NHS administrative systems could be applied more broadly to all registry or similar projects seeking to exploit routinely collected data, so that patients could be easily identified as having given or not given consent, having withdrawn consent, or not having been asked for consent. It could also capture whether or not the patient consents to all data being used and if not, which data are specifically excluded from the consent. However, we are uncertain whether or not verbal patient consent recorded in this way would comply with NHS Digital’s requirement for ‘explicit’ patient consent (the legal basis for accessing HES/ONS data). 27,77

Although patients and the public are broadly supportive of the use of electronic patient data for research,78–81 there are concerns about potential breaches of privacy and the misuse of health data. 82 The public response to the care.data public consulation83 highlighted important challenges in terms of patient confidence and trust in how electronic health records are used in medical research. 84 Newer consent models have been proposed, including a dynamic consent model, which would allow patients to control consent electronically over time and receive information about the uses of their data and up-to-date lay summaries of research projects that have used their data. 85 However, such a model would require investment in and maintenance of an electronic infrastructure, which would be difficult in the current economic climate of the NHS. Others have called for more adaptive governance models depending on the particular circumstances of the proposed research. 86 It was beyond the scope of this study to explore these possibilities for obtaining consent. 27 The difficulties we experienced in identifying consecutive eligible patients and consenting those who were identified in the emergency setting of activating the PPCI pathway highlight that the conventional consent model that we successfully implemented would not be feasible for a national registry.

We did not meet the feasibility criterion for data linkage and extraction (the target was that this should be achieved for > 90% of patients at all four hospitals) from HIS for all the requested data sets. Reasons for missing data included the following: difficulties in extracting data from local stand-alone databases, some of which were old and did not have an interface for data extraction; some data (e.g. medications on discharge, bedside ECHO) were not recorded electronically; imaging data were available electronically but only as free-text reports, making individual parameters of interest difficult to extract. The main impacts for a registry of failing to obtain these HISs is the inability to characterise the registry population in detail, to adjust any comparisons for patients’ characteristics and to describe changes in these data over time (potential indicators of the consequences of important changes in management).

We had initially envisaged establishing a data linkage and extraction model in one hospital and using this to guide data extraction at the other hospitals. We also envisaged that this model would be automated, with minimal IT support beyond the set-up phase. However, this was not possible because HIS for each data set differed across hospitals and different hospitals provided different levels of start-up IT support for the research project. 27 We budgeted for only a small amount of funding (£5000) to each hospital to help with this aspect of the study. Data linkage requires expertise in several areas, including knowledge of the databases to be linked and skills in creating linkage programmes. This expertise was variable across hospitals and in-depth knowledge was sometimes lacking; this was perhaps not surprising given the diversity of the databases involved, and the fact that some of these were old and were being phased out. 27 We relied on each hospital identifying key individuals to help with this aspect of the study. In addition, even in hospital A, the highest priority for the IT team was to maintain the integrity and functionality of the clinical care systems; in this context, it was difficult for them to prioritise our request to support this research project, even with the funding that was available.

The complexity of data retrieval from multiple HISs in secondary care in the NHS has been highlighted previously. 61 The government’s vision for the NHS, outlined in the National Information Board’s complex Personalised Health and Care 2020 framework,87 is that it will be an entirely digital organisation by 2020. Although such a system, if properly implemented, should provide benefits for research as well as patient care, it is an ambitious goal given the current financial and operational pressures within NHS organisations. The earlier (and equally ambitious) National Programme for IT, which aimed to create a single electronic care record for patients, connect primary and secondary care IT systems and provide a single IT platform for health professionals, was dismantled, mainly because IT companies could not provide solutions to match the ambition. 27,88 Despite such programmes, our experience from this study has highlighted the poor-quality electronic infrastructure in hospitals and the multitude of clinical databases that are not amenable to ‘interoperability’ (i.e. not needing extensive customisation or permission from the database vendor to provide data flow). Full interoperability for HISs across the majority of hospitals, therefore, still appears to be a distant goal. 89

We met the feasibility criterion of having > 10% of our consented population being referred for CMR in hospitals with a dedicated CMR facility. However, many of these referrals were for research (patient participating in a research study) rather than clinical purposes. For example, in the lead hospital, 33% of all referrals were for research. It is not clear whether or not this reflects practice in other hospitals with dedicated CMR facilities. Referral for non-clinical reasons (unless the findings are used to change clinical management) would dilute any observed effect of CMR on outcomes in an extended registry. Another challenge was that imaging reports were largely in free-text format, so individual CMR parameters would have to be extracted manually, which would not be feasible for an extended registry. A registry assessing the prognostic value of CMR, including individual CMR parameters, such as contractile function, myocardial oedema, MVO, intracardiac thrombus, myocardial haemorrhage and myocardial scar, would allow for an assessment of the value added of each parameter on patient outcome. Similarly, ECHO reports were also in free-text format, and bedside ECHO reports were missing entirely from our data sets because these are not routinely uploaded to electronic databases.

We made the decision to obtain data about follow-up from hospital episode data rather than from the source data submitted by participating hospitals, which are used to compile the hospital episode data. Using hospital episode data has two advantages: (1) follow-up episodes of care can be linked to index admissions, wherever they occur (not just in the participating hospitals) and (2) the organisations compiling hospital episode data apply established data management processes to improve data quality. We met the feasibility objective with respect to linkage of index admission data with hospital episode data. Linkage was achieved for 99% of patients. We failed to achieve linkage for the remainder mainly because of missing identifiers (NHS numbers) in the data sets submitted by participating hospitals. We had originally envisaged collecting information from local HISs on outcomes in the 12 months following the index procedure. However, we would have been unable to capture these data on every patient included in the study because many patients were discharged to, and followed up in, different hospitals, and it would have been difficult to liaise with and obtain data from multiple sites.

Is it feasible to set up a national registry from hospital episode data?

We explored the feasibility of setting up the registry retrospectively entirely by using linked, routinely collected data sets [e.g. hospital episode data, NHS England’s Diagnostic Imaging Dataset (DID) and national audits, such as the BCIS’ NAPCI90]. Setting up the registry in this way would avoid the need to obtain patient consent. This would be deemed appropriate on the basis that all of the information made available would be anonymised.

We were able to identify patients who activate the PPCI pathway in hospital episode data using the relevant procedure codes. We identified 98% of patients in our cohort who underwent PPCI and 85% of patients who underwent emergency angiography without PPCI. We could not, however, identify the CMR exposure in hospital episode data for the majority of study participants who had a CMR scan. This is because CMR is carried out mainly as an outpatient investigation and the hospital episode outpatient data sets do not contain details of the exact nature of the outpatient visit (only the specialty), so the reason for outpatient visits coded under the cardiology specialty was unclear. One option to consider would be the use of NHS England’s DID to identify CMR exposure. DID is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients, extracted from local radiology information systems and submitted monthly to NHS England. 91 The main driver for the creation of the DID was to assess how the use of diagnostic imaging could contribute to the early diagnosis of cancer (in particular, GP direct access to these tests). As such, DID has already been linked to cancer registry data, but not other national audits, and there could be substantial difficulties/delays in applying for linkage permissions with, for example, the BCIS’ NAPCI data set. 90 Although DID captures information about referral source and patient type, details of the test (e.g. type of test and body site), demographic information (e.g. GP-registered practice, patient postcode, ethnicity, gender and date of birth), plus items about waiting times for each diagnostic imaging event (it does not store the images themselves, the individual parameters measured by the test or the outcomes/diagnoses related to the test). DID could not, therefore, be used to assess the prognostic value of individual components of a CMR scan, or the extent to which a CMR scan altered patient management. Nevertheless, it should allow ascertainment of CMR exposure.

Economic evaluation

We developed a series of economic decision models to identify the key drivers of cost-effectiveness. To our knowledge, this is the first time the key drivers of the cost-effectiveness of introducing CMR have been considered. We chose two patient subgroups for our models: patients with multivessel disease and patients with unobstructed coronary arteries. Both of these subgroups were identified as potentially benefiting from CMR in the formal consensus study.

Our results showed that, for each of the base-case models, the differences in QALYs between strategies were very small and, therefore, the results were largely driven by the differences in costs, although mean differences in cost for different strategies were modest. Sensitivity analyses around the two models identified the diagnostic accuracy of the ischaemia tests as the key driver of cost-effectiveness. In the model for patients with multivessel disease, the costs of ischaemia testing and the QALYs associated with MACEs and no MACEs exerted an influence on cost-effectiveness results, but not to the same extent as the diagnostic accuracy of CMR and pressure wire. Similarly, in the model for patients with unobstructed coronary arteries, the cost of CMR, the difference between QALYs associated with MACEs and no MACEs, and the proportion of patients who have MACEs who are assumed to die exerted an influence on the results; however, the better diagnostic accuracy of CMR and standard ECHO combined (the reference standard in the base case) compared with standard ECHO only was more important.

The key drivers of cost-effectiveness highlighted by each model are similar, and, therefore, it is likely that if CMR was introduced for our entire eligible population (patients who activate the PPCI pathway), it would be these parameters that would need to be measured accurately in any future study. This work highlights the importance of having accurate information about the diagnostic accuracy of different tests, as estimates for these parameters are such key determinants of cost-effectiveness. We have identified some of the key unknowns in terms of costs and outcomes in the population of patients who activate the PPCI pathway, which can inform future studies to ensure that important variables are captured in sufficient detail.

Identifying important changes in management resulting from cardiovascular magnetic resonance and the subgroups of patients to which these refer

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A key objective of our feasibility study was to define a primary composite outcome, acceptable to cardiologists and other stakeholders (e.g. clinical commissioners) as representing a clinically important change in management (e.g. expected to prevent future MACEs) as a result of an eligible patient having had CMR, that could be used for the registry in the medium term. Although such a primary outcome is not typical, it would allow evidence to be obtained more quickly to inform decisions about implementing CMR than would be possible for an evaluation based on MACEs. In the long term, we envisaged that the primary outcome for the registry would be MACEs, but addressing relevant research questions using the registry would need data for a large number of patients. For example, in order to have 90% power to detect a 1.2% difference in MACEs (4.8% vs. 6.0%) between those having CMR and those not, at the 5% level of statistical significance, and assuming 10% of patients who activate the PPCI pathway receive CMR and 90% do not, the registry would require > 37,000 patients. This represents 20 hospitals, with an average of 500 patients giving consent per year per site for > 3.5 years. We expected that important changes in management would be much more frequent than MACEs, allowing the effectiveness of CMR with respect to this outcome to be evaluated more quickly with data from fewer patients.

We used a formal consensus approach because there were no studies in the literature that reported the impact of CMR in our study population, reflecting the fact that changes in clinical management are difficult to use as end points in trials. This is because some important changes in clinical management are not easily observed92 and the causal relationship between changes in management and improved outcomes is not well defined. Imaging has been identified as one of the key drivers of increased health-care costs; therefore, the adoption of an imaging modality in a new clinical context should be supported by evidence of its benefit to specific patient groups or clinical scenarios. Such evidence is difficult to obtain in practice.

We identified five subgroups of ACS patients who activate the PPCI pathway for whom there was consensus that CMR changes patient management in a clinically important way: (1) patients who have an OHCA (≈7% of those who activate the PPCI pathway),69 (2) patients who have a ‘normal’ (unobstructed) coronary angiogram (≈10% of those who activate the PPCI pathway),15,16 (3) patients who develop LV thrombus (3% overall and 9% in patients with anterior STEMI),70 (4) patients who have multivessel disease (between 40% and 65% of those who undergo PPCI)7,10,71 and (5) patients in whom CMR markers indicate a poor prognosis (up to 60% of patients after STEMI). 2,72 There was consensus about the first three subgroups in both rounds of the survey, whereas consensus was reached about the other two subgroups in the modified survey. These results suggest that CMR benefits a large proportion of patients who activate the PPCI pathway.

Cardiologists who participated in our research agreed that CMR is superior to ECHO in establishing a diagnosis in patients who survive an OHCA, which has implications for treatment and prognosis. Despite this view, there are few studies that have reported the role of CMR in managing these patients. A retrospective case series of 54 OHCA survivors showed that CMR diagnosed the cause (ischaemic or non-ischaemic cardiomyopathy) in 40 (74%) of the survivors. 93

The cardiologists also agreed that CMR can provide a definitive diagnosis (e.g. acute MI, acute myocarditis and cardiomyopathy, especially Takotsubo cardiomyopathy) in patients with ACS and unobstructed coronary arteries. Evidence for the role of CMR in patients with ACS and unobstructed coronary arteries also comes from small retrospective case series. These suggest that CMR provides a definitive diagnosis in 80–90% of these patients. 20,23,94 Without access to CMR, the management of these patients is variable in clinical practice, which may have long-term implications. A 2015 systematic review50 showed that the overall all-cause mortality in patients presenting with suspected MI and unobstructed coronary arteries was ≈5% at 12 months.

The cardiologists agreed that CMR identifies LV thrombus better than ECHO, which allows more patients to be identified and treated appropriately. Accurate identification of a LV thrombus is important because it often directs subsequent anticoagulation therapy to prevent embolic events. A 2015 systematic review,95 published after we conducted the formal consensus study, showed that late gadolinium enhancement (LGE) CMR is the most accurate modality for detecting LV thrombus, with 88% sensitivity and 99% specificity (compared with standard ECHO, which had 24–33% sensitivity and 94–95% specificity, and contrast ECHO, which had 23–61% sensitivity and 96–99% specificity), although most of the included studies in this review did not use a pathological or surgical gold standard for the detection of LV thrombus.

There was agreement among cardiologists that CMR-based testing of ischaemia after PPCI would be likely to optimise the revascularisation strategy for patients with multivessel coronary disease, although they acknowledged that there is no evidence to support the view that a CMR-based revascularisation strategy would improve outcomes. Patients with multivessel disease have a two-fold increase in MACEs compared with patients with single-vessel disease. 96 There is continuing debate about the benefits of complete versus single lesion revascularisation during the index PPCI admission. 97 Despite recent evidence from randomised controlled trials (RCTs) showing improved clinical outcomes when complete revascularisation is undertaken at PPCI,98 current American99 and European100 revascularisation guidelines for acute MI recommend revascularisation only of the infarct-related artery at PPCI in patients with multivessel disease.

There was considerable debate about using CMR to identify patients with a poor prognosis after PPCI. Although there was agreement that CMR parameters of cardiac function (e.g. impaired LV function, large infarct size, MVO) are useful for risk stratification after STEMI, there was disagreement about whether or not this would lead to a management change. Some cardiologists felt that there would be no changes to prescribing for secondary prevention because all patients should receive aggressive secondary prevention in accordance with guidelines. It was acknowledged, however, that CMR markers are prognostic for outcome;4 two recent meta-analyses of prognostic studies (conducted after this formal consensus study) showed an increased risk of MACE, by 13–15% for every 10% decrease in EF assessed by CMR,2 and in patients with MVO assessed by CMR [odds ratio (OR) 2.60, 95% CI 1.68 to 4.02 and OR 4.30, 95% CI 2.19 to 8.43, depending on the method of MVO assessment]. 3

We could not identify all patient subgroups from HIS data sets. Patients with LV thrombus and OHCA were identifiable only in hospital episode data, from ICD-10 codes for these diagnoses assigned to the index admission. Patients with unobstructed arteries could not be positively identified from HIS; we identified them by their exclusion from the hospital BCIS’ NAPCI data sets (as only patients who have a stent inserted, i.e. have PPCI, or have had an attempt to have a stent inserted, are included in BCIS’ NAPCI). Given the uncertainty we faced when trying to identify this population prospectively (see Feasibility of setting up a registry using hospital information system data) and the initial confusion over eligibility at the beginning of our recruitment period, we decided to review the medical notes (representing the reference standard) for a subset of these patients to determine the proportion classified correctly according to our definition of ‘unobstructed coronary arteries’ (exclusion from BCIS’ NAPCI). Although we classified 81% of patients correctly, some patients we identified as having unobstructed arteries were identified as having a culprit lesion in the manual review of patients’ notes. Furthermore, there was also a degree of disagreement between medical notes and the primary diagnosis recorded in hospital episode data for the index admission of these patients, with 11% of patients who were identified as having unobstructed coronary arteries in their medical notes having a primary diagnosis of MI in HES. Our investigation using medical notes highlights a degree of misclassification of this subgroup of patients, although this is not surprising given the uncertainty in diagnosis during the index admission.

Implementation of a primary outcome based on the changes in management for which consensus was achieved using hospital episode data

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There was consensus that CMR changes patient management in a clinically important way for five subgroups of ACS patients. For each group, we listed ‘activities’ (e.g. new diagnoses or procedures, additional diagnostic tests, changes in medication, outpatient appointments) that reflected a change in management. Activities were identified from the relevant consensus statements and by looking at care pathways, when available, to confirm that we captured all aspects of patient management in these activities. We then compiled a list of the relevant ICD-10 diagnosis codes and OPCS procedure codes (with help from clinicians on the study team and a dedicated HES coder). We identified these codes in hospital episode data for participants up to 12 months following the index procedure.

We were able to identify all of the activities that we identified as representing changes in management in the hospital episode data. Therefore, we conclude that it would be feasible to look for differences between patients who have and those who do not have CMR within ACS subgroups in terms of frequency and time to first event for some of the activities. We recognise that some events that were listed as representing a change in management (e.g. cardiology outpatient appointment, changes in medication) are not immediately amenable to inclusion in a composite outcome for a time-to-event model.

This approach to constructing a proxy outcome reflects our original concept and appears to be feasible when the important changes in management are hypothesised to cause a change in the same direction in all events/activities included in a composite outcome for a subgroup. However, through the consensus process, it became apparent that some of the hypothesised changes in management arising from CMR will not necessarily result in a unidirectional change in the frequency or rate of an activity. In several instances, an important change in management was more appropriate targeting of a treatment activity to the patients who were likely to benefit most, without necessarily any change in the overall frequency of the activity. For example, in patients with multivessel disease, CMR may result in better (i.e. more cost-effective) targeting of additional revascularisation. In these situations, a simple evaluation of the frequency or rate of a proxy outcome would not detect any value of CMR to patients and the NHS.

We were unable to identify some important changes in management that could have arisen from CMR. There are no medication records in hospital episode data, so changes in drug therapy could not be identified. Initiation, withdrawal or changes in medication represented an important change in management for all of our patient subgroups and, in particular, patients with LV thrombus, for whom anticoagulation therapy is the main change in management. A potential solution to this problem is the linkage of HIS and hospital episode data with data from the Clinical Practice Research Datalink (CPRD), a primary care data set that has detailed prescription information. However, because not all general practices contribute data to CPRD, medications data would be available for only a proportion of patients in a registry.

We were also unable to identify additional tests or procedures carried out in the outpatient department because most outpatient episodes in the HES outpatient data set do not have diagnostic/procedure codes assigned to them. We are uncertain about the extent to which DID, recently added to the HES portfolio of data sets, may improve this situation. Finally, hospital episode data alone may not capture all activity related to changes in management as some patients will be seen multiple times by their GP or in private facilities. However, as the extent of after care in private facilities in the UK is minimal, it is unlikely to invalidate a future comparison in a registry.

Feasibility prospective cohort study

We established a successful collaboration among four hospitals and exceeded the recruitment target for the study of 1600 patients. We also identified the main challenges to establishing a prospective registry in the way we had envisaged: identifying the eligible population is difficult to do from local HISs; the conventional consent model fails to capture a sizeable proportion of the eligible population and would be costly to implement; and, most importantly, the required local data from HISs are not uniformly available/exportable. 27

We did not identify or test a more efficient method to obtain consent. The methods used by hospitals for the study are almost certainly not feasible to implement in the long term for a registry, and are likely to result in varying completeness across hospitals. However, the barriers to any form of ‘presumed consent’ or ‘opt out’ seemed insurmountable without a long-term plan for implementing consent.

We are aware that CMR may benefit other groups of ACS patients (e.g. those with NSTEMI). However, we considered at the outset that it would be impossible to identify consecutive patients presenting with a broader diagnosis of ACS but not requiring emergency coronary angiography; being able to define and ascertain the entire eligible population is a key requirement of a high-quality database. This was a particularly important consideration given that the proposed registry was based on the premise of linking information collected in the course of usual care, avoiding the time and cost burden of primary data collection. 27 Even so, we still encountered difficulties in identifying from electronic records the group of patients with unobstructed coronary arteries in our eligible population.

We concluded that we could identify our eligible population in hospital episode data, based on the fact that we identified the index admission for most patients recruited into our study. However, because we had only hospital episode data for patients in the cohort, we cannot comment on or quantify the risk that an algorithm designed to identify such patients from hospital episode data could also identify some patients who would have been ineligible for the registry. Our impression from exploring codes assigned to hospital episode data for index admissions is that this risk is low and is likely to reduce as the quality of hospital episode data improves. The only way to quantify the risk would have been to request anonymised linkage of all HISs and hospital episode data for all cardiology admissions during the study period, with identification of the study cohort participants as a subgroup.

A major limitation is the length of time it took to complete the study. The main reason for this was because our application for HES data coincided with the moratorium on data requests by NHS Digital (formerly known as the Health and Social Care Information Centre), which resulted in a delay of 23 months in processing our application. This meant that the study took > 4.5 years to complete, whereas we had projected 1.5 years for completion. During this time, it is likely that data availability and the quality of the data will have changed/improved, given the rapidly evolving NHS IT systems/platforms. During this time, new diagnostic tests for detecting ischaemia (e.g. pressure wire), directly competing with CMR, were introduced and rapidly adopted by some cardiologists. These changes highlight the need for new research questions; therefore, it is likely that the aims and objectives for a national registry now would be different from those we proposed when we set up this study. For example, it may be more relevant to compare ischaemia testing using pressure wire with CMR, rather than to compare CMR with no CMR, which we proposed for our registry. We have tried to adapt the study to the current circumstances, for example with respect to the cost-effectiveness models, but have been unable to do anything about the changes in practice or the historic nature of the data collected.

Economic evaluation

There is considerable uncertainty around the majority of parameter estimates in both models. In the model for patients with unobstructed coronary arteries, the probability of patients truly having a MI was obtained from the results of a systematic review,50 but all other probability estimates in both models are based on single papers, often on small or modest sample sizes. There was only sparse evidence found in the literature for many of the probability estimates required. This was particularly true for the probabilities of MACEs for patients who truly did and those who did not have a MI (and had appropriate treatment or not), in the model for patients with unobstructed coronary arteries. Members of the study team were concerned that the estimated ratio of MACEs in MI patients compared with non-MI patients (in patients with unobstructed coronary arteries) found in Pathik et al. 52 was very high; this is why we investigated this parameter in the sensitivity analyses.

Even when papers reported on the correct patient group and outcomes, studies may have been conducted in other countries, where practices may differ from those in the UK. For example, Smits et al. 38 specifically reported numbers that could be used to estimate the probability of a MACE by 1 year for those truly with ischaemia separately for those who were and those who were not revascularised (see Figures 1 and 2, and Chapter 5, Cost-effectiveness models); however, this study was conducted in Europe and Asia, with no UK centres included. Occasionally, estimates from a different patient group were the only estimates available. For example, in the model for patients with multivessel disease, the diagnostic accuracy of stress ECHO (its sensitivity and specificity) was taken from a paper considering the diagnosis of significant coronary artery disease, rather than a PPCI population, and estimates are based on the number of vessels rather than the number of patients. 39

In the base-case analyses for multivessel disease, CMR and pressure wire were treated as reference standards, and both were assumed to have 100% sensitivity and specificity. There are suggestions in the literature that this is reasonable; for example, Emrich et al. 103 found that CMR correctly identified all 20 out of 125 patients with MI in their study. The assumption of equal diagnostic accuracy is critical, because the sensitivity analyses showed how diagnostic accuracy of the ischaemia tests was the key driver of cost-effectiveness. Similarly for the unobstructed coronary arteries model, reducing the diagnostic accuracy of CMR plus standard ECHO compared with standard ECHO alone (sensitivity analysis 1) doubled the ICER.

Individual patient data on resource use were not available from hospital episode data in time to be used in these analyses; resource use associated with each of the patient pathways was, therefore, estimated based on discussions with the PIPA study team and costs were largely obtained from NHS Reference Costs. 40 These methods have allowed standard care pathways to be costed but will have underestimated the variability between individual patients and their actual patient pathways.

As well as the costs of initial ischaemia testing (if applicable), and any revascularisation, we included the costs of follow-up outpatient appointments, medications and rehabilitation over the first year. We also included additional health-care costs to 1 year based on the Office of Health Economics’ estimate of the annual cost per person of NHS care. 43 It is recognised that there will be an element of double counting here, as the costs of ischaemia testing, revascularisation and subsequent costs will overlap with these annual costs, but this will be the same for all strategies and should not affect the comparison of costs between strategies. These annual costs were included to account for differences in costs for patients who died; these costs were halved for patients with a MACE who died by 1 year.

Utility estimates for a PPCI population were found in the literature, but there was uncertainty around the utility decrement for patients with MACEs compared with those without MACEs. A decrement of 0.05 was assumed for patients with a MACE compared with those without a MACE. 48 Although this decrement has been used by others, the original reference for this decrement implies that it was a sensitivity analysis, and was not based on any primary data. 104 Utility estimates for patients with unobstructed coronary arteries were not found in the Tufts Cost-effectiveness Analysis Registry,46 and a recent paper measuring quality of life in patients with MI with unobstructed coronary arteries reported that no studies measuring quality of life in these patients have previously been conducted. 105 This study measured quality of life using the SF-36 at 3 months post acute event and found similar findings in patients with unobstructed coronary arteries and a control group of MI patients with coronary heart disease, suggesting that the estimates used in our model for patients with unobstructed coronary arteries are reasonable.

Formal consensus study

Parts of this text have been reproduced from Pufulete et al. 4 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.

A key strength of the study was the use of a formal consensus approach, based on a systematic search of the literature as well as expert opinion. The nominal group technique is one of the four well-established methodologies for formal consensus [the other three are (1) the Delphi method, (2) the Research ANd Development Corporation (RAND)/University of California, Los Angeles Appropriateness Method (RAM) and (3) the National Institutes’ of Health consensus development conference methodology]. 4,106,107 Our literature review included all study designs to determine the size and nature of the evidence base and highlight research gaps. The search was systematic and extensive, including five databases and published abstracts. Four cardiologists contributed expert opinion. Two are UK/European CMR experts. The process provided evidence for the content validity of each statement. All cardiologists who drafted the statements proposed the same patient subgroups as potentially benefiting from CMR. There was generally good agreement between draft statements generated by cardiologists and the non-clinical reviewer.

There were no disagreements in the group meeting regarding the potential management change in each of the patient subgroups identified. Similarly, the studies identified in our literature review (both prognostic and diagnostic) were consistent in their findings. The majority of prognostic studies reported that CMR markers such as EF, MVO and haemorrhage were associated with MACEs, although most did not evaluate the prognostic value of CMR indices in addition to indices available from alternative imaging modalities. The evidence supporting many of the statements came from small, retrospective case series. There were no prospective studies in the literature that reported the impact of CMR on clinical management in our population or whether or not any changes in management affected patient outcomes.

The main features of a formal consensus method are anonymity (i.e. statements were rated free from peer-group pressure) and iterative feedback (i.e. participants could adjust their initial rating based on the feedback of the group rating). We described the supporting evidence for each statement, identified via the literature review, and fed back qualitative (i.e. panel members’ comments) and quantitative (i.e. group median ratings of each statement) information at the face-to-face meeting. We also included a varied group of cardiologists from different specialties in order to encompass diverse perspectives. 4

A potential limitation of this study is that we did not perform any formal quality assessment of primary studies. We made this decision for two reasons. First, the purpose of the review was to identify the evidence base in order to inform the consensus process, not to use the literature to quantify the effectiveness of CMR or its diagnostic or prognostic value. Second, no single assessment tool would have been appropriate for the diverse studies that were identified, hence assessments would not have been comparable. Therefore, we have not referred to our overall review as being ‘systematic’ (only the search), although its conduct closely reflected established guidelines for systematic reviews and we believe that other researchers following the same steps would replicate its findings.

The number of panel members in our study was lower than the recommended 8–12 members for a consensus panel. 107 Nevertheless, smaller groups are preferable to larger ones because, although having more group members increases the reliability of group judgement, large groups reduce the ability to elicit potentially important contributions from every member of the panel. 107 To overcome any criticism of the number of panel members in the study and to prevent the possibility of introducing bias (given the self-selected nature of the panel), we extended the survey to UK cardiologists who did not participate in the formal consensus process. Apart from one statement (statement 1, relating to the ability of CMR to identify patients with a poor prognosis after PPCI), the same statements were in consensus when the survey was completed by cardiologists external to the consensus process. This indicates that the process was robust and the survey statements were clear and unambiguous.

A further limitation of the study was that we did not include other stakeholders (e.g. GPs and patient representatives) in the consensus process. 108 Although we had originally planned to do so (see Patient and public involvement and engagement), we decided that the specialised nature of the statements would not be easily understood by those without cardiology expertise. 4 We did, however, hold a separate meeting (outside the consensus process) with a GP commissioner and the director of the Southwest Cardiac and Stroke Network. The conclusion of the meeting was that, given that the cohort is an emergency population and the consensus process identified that a large proportion of the population would potentially benefit from scanning, it would be better to offer CMR to the whole population rather than complicating the patient management strategy by scanning only subsets of patients.

Search strategy

1. exp Myocardial Infarction/ (142,973)

2. myocardial infarct$.tw. (134,547)

3. heart attack$.tw. (3601)

4. MI.tw. (25,881)

5. ami.tw. (12,166)

6. stemi.tw. (3663)

7. heart infarct$.tw. (705)

8. Acute Coronary Syndrome/ (6784)

9. acute coronary syndrome$.tw. (15,521)

10. ACS.tw. (9320)

11. or/1-10 (210,783)

12. exp angioplasty/ (53,568)

13. angioplasty.tw. (34,003)

14. percutaneous coronary intervention$.tw. (14,406)

15. exp Percutaneous Coronary Intervention/ (34,668)

16. PCI.tw. (11,157)

17. PPCI.tw. (366)

18. exp Stents/ (50,784)

19. stent$.tw. (56,427)

20. Myocardial Revascularization/ (9079)

21. revasculari$.tw. (37,853)

22. reperfused.tw. (5688)

23. reperfusion.tw. (55,775)

24. or/12-23 (191,056)

25. exp Magnetic Resonance Imaging/ (295,774)

26. Magnetic resonance imag$.tw. (122,420)

27. Cardiovascular magnetic resonance.tw. (1765)

28. cardiac magnetic resonance.tw. (3179)

29. MRI.tw. (121,901)

30. CMR.tw. (3035)

31. or/25-30 (344,849)

32. 24 and 31 (5713)

33. 11 and 32 (1318)

34. exp animals/ not humans/ (3,863,199)

35. 33 not 34 (1051)

Search strategy

1. exp heart infarction/ (261,299)

2. myocardial infarct$.tw. (185,732)

3. heart attack$.tw. (4991)

4. MI.tw. (45,694)

5. ami.tw. (19,697)

6. stemi.tw. (10,463)

7. heart infarct$.tw. (1450)

8. exp Acute Coronary Syndrome/ (26,244)

9. acute coronary syndrome$.tw. (28,004)

10. ACS.tw. (17,708)

11. or/1-10 (337,561)

12. exp angioplasty/ (67,762)

13. angioplasty.tw. (47,179)

14. percutaneous coronary intervention$.tw. (27,203)

15. exp Percutaneous Coronary Intervention/ (59,160)

16. PCI.tw. (26,664)

17. PPCI.tw. (1281)

18. Stent/ (64,591)

19. exp cardiovascular stent/ (34,380)

20. stent$.tw. (94,492)

21. heart muscle revascularization/ (21,848)

22. revasculari$.tw. (56,445)

23. reperfused.tw. (7304)

24. reperfusion.tw. (77,453)

25. or/12-24 (298,341)

26. exp nuclear magnetic resonance imaging/ (524,106)

27. Magnetic resonance imag$.tw. (164,672)

28. Cardiovascular magnetic resonance.tw. (3414)

29. cardiac magnetic resonance.tw. (6341)

30. MRI.tw. (203,793)

31. CMR.tw. (7673)

32. or/26-31 (563,445)

33. 25 and 32 (13,408)

34. 11 and 33 (3451)

35. (exp animal/ or nonhuman/) not exp humans/ (5,173,875)

36. 34 not 35 (3048)

37. limit 36 to embase (2864)

Search strategy

1. #1 MeSH descriptor: [Myocardial Infarction] explode all trees

2. #2 MeSH descriptor: [Acute Coronary Syndrome] explode all trees

3. #3 myocardial next infarct*

4. #4 heart next attack*

5. #5 MI

6. #6 AMI

7. #7 STEMI

8. #8 heart next infarct*

9. #9 ‘acute coronary syndrome*’

10. #10 #1 or #2 or #3 or #4 or #5 or #6 or #7 or #8 or #9

11. #11 MeSH descriptor: [Angioplasty] explode all trees

12. #12 MeSH descriptor: [Percutaneous Coronary Intervention] explode all trees

13. #13 MeSH descriptor: [Stents] explode all trees

14. #14 MeSH descriptor: [Myocardial Revascularization] this term only

15. #15 angioplasty

16. #16 ‘percutaneous coronary intervention*’

17. #17 PCI or PPCI

18. #18 stent*

19. #19 revasculari*

20. #20 reperfused

21. #21 reperfusion

22. #22 #11 or #12 or #13 or #14 or #15 or #16 or #17 or #18 or #19 or #20 or #21

23. #23 MeSH descriptor: [Magnetic Resonance Imaging] explode all trees

24. #24 ‘Magnetic resonance imag*’

25. #25 ‘Cardiovascular magnetic resonance’

26. #26 ‘cardiac magnetic resonance’

27. #27 MRI or CMR

28. #28 #23 or #24 or #25 or #26 or #27

29. #29 #11 and #22 and #28

Search strategy

1. # 4 1,351 #3 AND #2 AND #1

2. # 3 218,389 TOPIC: (Angioplasty or “percutaneous coronary intervention*” or PCI or PPCI or stent* or revasulari* or reperfused or reperfusion)

3. # 2 257,465 TOPIC: (“Magnetic Resonance Imag*” or “Cardiovascular magnetic resonance” or “ cardiac magnetic resonance” or MRI or CMR)

4. # 1 273,481 TOPIC: (“myocardial infarct*” or “heart attack*” or “heart infarct*” or “acute coronary syndrome*” or MI or AMI or STEMI or ACS)

Search strategy

1. # 6 873 #5 AND #4

2. # 5 TAXONOMIC DATA: (human not (animal* not human))

3. # 4 #3 AND #2 AND #1

4. # 3 TOPIC: (Angioplasty or “percutaneous coronary intervention*” or PCI or PPCI or stent* or revasulari* or reperfused or reperfusion)

5. # 2 TOPIC: (“Magnetic Resonance Imag*” or “Cardiovascular magnetic resonance” or “ cardiac magnetic resonance” or MRI or CMR)

6. # 1 TOPIC: (“myocardial infarct*” or “heart attack*” or “heart infarct*” or “acute coronary syndrome*” or MI or AMI or STEMI or ACS)

Statement 1. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary a intervention allows patients with cardiovascular magnetic resonance markers that indicate a poor prognosis (e.g. impaired left ventricular function, large infarct size, microvascular obstruction) to be followed up more appropriately and undergo more aggressive medical therapy for secondary prevention

Accurate assessment of infarct characteristics is important for risk stratification after PPCI. CMR can quantify in a single scan all cardiac markers relevant to PPCI outcomes, with high reproducibility and accuracy. CMR has high spatial and temporal resolution and is superior to ECHO for measuring LV volumes and EF. 112 Additionally, CMR markers, such as infarct size, MVO and myocardial salvage, have been shown to have long-term prognostic value. 113–115 LGE CMR has added prognostic value over ECHO. 116

Statement 2. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary intervention allows patients with cardiovascular magnetic resonance markers that indicate a good prognosis (e.g. normal left ventricular function, high myocardial salvage, no microvascular obstruction, no residual ischaemia) to be discharged earlier and followed up less frequently

Cardiovascular magnetic resonance measures several markers (i.e. infarct size, myocardial salvage index, MVO, myocardial oedema and haemorrhage) that cannot be measured by ECHO. These markers can be used to predict cardiac remodelling and prognosis after MI,113–115 determine the optimal therapeutic pathway for each patient and prevent over- and under-treatment.

Statement 3. Compared with echocardiography, cardiovascular magnetic resonance better identifies the cause of out-of-hospital cardiac arrest (e.g. large myocardial infarction, arrhythmogenic right ventricular cardiomyopathy, aberrant coronary arteries, hypertrophic cardiomyopathy) to optimise further treatment for the patient (e.g. defibrillator for primary arrhythmia or percutaneous coronary intervention) or family members

Out-of-hospital cardiac arrest affects about 60,000 people in the UK each year. Currently, hospital survival is about 32%. Early identification of the cause is essential to improve survival. Causes of OHCA include MI (40–90%) and inherited cardiomyopathies, such as arrhythmogenic right ventricular cardiomyopathy (ARVC) and hypertrophic cardiomyopathy (HCM). Unlike ECHO, CMR allows in vivo tissue characterisation, which differentiates scarring resulting from MI from other causes of focal fibrosis (e.g. observed in non-ischaemic cardiomyopathies such as ARVC and HCM). 117 LGE on CMR can predict serious cardiac complications in patients with HCM (e.g. all-cause death, cardiac death and death from heart failure),118 and identifies patients who may need more aggressive medical and device therapy (e.g. renin-aldosterone system inhibition for prevention of heart failure or ICD placement for primary prevention of sudden cardiac death).

Statement 4. Cardiovascular magnetic resonance after primary percutaneous coronary intervention identifies patients at high risk of having ventricular septal defect or impending cardiac rupture, who may require ventricular patch or other urgent cardiac surgery, and guides the optimal management of these patients

Ventricular septal defect (VSD) and LV free wall rupture are rare complications of MI, occurring in < 1% of PPCI patients, usually within 1 week of the infarct. Mortality ranges between 50% and 80%. Diagnosis can be difficult to make and usually requires multimodality imaging (e.g. ECHO, ventriculography, CT, CMR) before surgical repair. Because of its high spatial resolution, CMR can be used to clarify the detailed structure of these lesions. CMR accurately identifies the location, size and tissue margins of VSD and is useful for detecting apical defects, which are not easily identified by ECHO. CMR measurements may also be used to determine the size of the ventricular patch required to close the VSD, which avoids the inflation of a sizing balloon in friable infarcted tissue. 119 These features, together with other CMR markers of damage (e.g. infarct size, MVO, LV dysfunction) are useful for guiding optimal management of patients with post-infarct VSD. Similarly, CMR can differentiate between the different types of impending free wall rupture (i.e. acute, subacute and chronic), which may be helpful in planning surgical management. 119

Statement 5. In patients with a ‘normal’ (unobstructed) coronary angiogram, cardiovascular magnetic resonance can differentiate patients who have had a myocardial infarction with spontaneous reperfusion or distal embolisation from patients with a non-ischaemic diagnosis (e.g. myocarditis, Takotsubo cardiomyopathy, aortic dissection), resulting in a patient treatment plan appropriate for the definitive diagnosis

The incidence of unobstructed coronary angiogram in patients who activate the PPCI pathway is between 5% and 12%. In these patients, the lack of an accurate diagnosis may result in inappropriate or unnecessary treatment and/or follow-up and a poorer prognosis. 19 CMR facilitates differential diagnosis in the context of an unobstructed coronary angiogram, providing a definitive diagnosis (e.g. myocardial infarction, myocarditis, Takotsubo cardiomyopathy) in 65–90% of these patients. 20,23 In patients with myocarditis, LGE on CMR may predict long-term adverse outcomes. 26 In the context of MI without an angiographic lesion, CMR can locate the culprit infarct-related artery in patients with spontaneous reperfusion or with distal embolisation.

Statement 6. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary intervention better identifies patients at high risk of sudden cardiac death who would benefit most from an implantable cardiac device (e.g. implantable cardiac defibrillator, cardiac resynchronisation therapy)

About 6% of heart attack patients subsequently die suddenly from a presumed cardiac cause. Current guidelines recommend the use of ICD to prevent sudden cardiac death in patients who have a low LV EF after a heart attack. EF is used in clinical practice to make decisions about ICD implantation, but it has a low predictive value and many patients with an ICD will never benefit from it. 120 EF is most commonly measured by ECHO, but CMR is now considered the gold standard for EF measurement because it is more reproducible than ECHO. 112 CMR has been shown to be better than ECHO for selecting patients for ICD implantation when strict EF thresholds are used to guide implantation. 121 The extent of myocardial scar characterised by LGE CMR may also be used to predict whether or not ICD implantation is appropriate in this patient group. 122 Furthermore, LGE CMR can guide placement of the LV lead away from scarred myocardium, which results in a better clinical outcome after cardiac resynchronisation therapy (CRT). 123

Statement 7. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary intervention better identifies patients who would not benefit from cardiac resynchronisation therapy

Cardiac resynchronisation therapy (or biventricular pacing) uses a specialised pacemaker to resynchronise the beating of the two ventricles by pacing both simultaneously, which improves the contraction of the left ventricle and the overall efficiency of the heart. It is used in patients with systolic ventricular dysfunction and heart failure. However, about 30% of patients who meet the inclusion criteria for CRT do not respond to it. 124 CMR assessment of mechanical dyssynchrony and myocardial scar provides additional value over ECHO for identifying responsive patients. 125,126 Myocardial scar is an important feature of non-response to CRT127,128 and LGE CMR accurately differentiates between transmural, mid-myocardial, epicardial and subendocardial scar.

Statement 8. Compared with stress echocardiography or single-photon emission computed tomography, cardiovascular magnetic resonance after primary percutaneous coronary intervention in patients with multivessel disease better assesses ischaemia and viability of the myocardium to optimise the revascularisation strategy for the patient and avoid additional diagnostic tests

Between 40% and 65% of the patients who activate the PPCI pathway have multivessel disease. Adequate assessment of residual ischaemia in non-culprit arteries post PPCI is important for effective management because this patient group has an adverse prognosis. Various techniques are currently used to assess the need for additional revascularisation, including stress ECHO, SPECT and stress-perfusion CMR. Stress CMR has excellent prognostic value129 and better diagnostic accuracy than SPECT130,131 or stress ECHO132 for detecting angiographically significant coronary artery disease.

Statement 9. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary intervention better identifies patients with left ventricular thrombus for treatment with anticoagulation therapy

Left ventricular thrombus is a serious complication of acute MI. It increases the risk of thromboembolic events, particularly stroke. LV thrombus develops in up to 10% of patients with anterior wall infarctions after PPCI. Although ECHO is most commonly used to detect LV thrombus and to assess its shape and size, between 10% and 46% of echocardiograms are inconclusive. 133 LGE CMR is considered the gold standard for detecting LV thrombus, because it detects thrombus based on tissue characteristics rather than anatomic appearance. Fewer thrombi are detected by contrast-ECHO than by LGE CMR. 134 CMR has a higher sensitivity (88%) than contrast (61%) and non-contrast (< 33%) ECHO. 134,135

Statement 10. Compared with other imaging modalities, in patients with multivessel disease, cardiovascular magnetic resonance after primary percutaneous coronary intervention better identifies the artery that caused the myocardial infarction and guides the subsequent treatment plan in relation to additional revascularisation (percutaneous coronary intervention or coronary artery bypass graft surgery)

In patients with multivessel disease presenting with acute MI, the culprit artery is not always easy to identify as more than one artery could be responsible. CMR has higher spatial resolution than SPECT, allowing easy identification of the infarct-related artery and non-culprit territory stenosis in PPCI patients,136 and, hence, appropriate risk-stratification of patients with multivessel disease for planning their future treatment. 137

Statement 11. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary intervention better identifies incidental cardiac findings that may need further investigation and treatment

Compared with ECHO, CMR provides versatile imaging planes, superior tissue contrast and advanced tissue characterisation, allowing a comprehensive assessment of cardiac anatomy, function and flow, and imaging of the great vessels (including venous return), the pericardium and suspected cardiac tumours. 138 As such, CMR can identify congenital coronary anomalies, cardiac masses, coronary artery aneurysms, valvular heart disease, thoracic aortic disease, and so forth. CMR can adequately differentiate benign from malignant tumours in the heart. 139

Statement 12. Compared with echocardiography, cardiovascular magnetic resonance after primary percutaneous coronary intervention identifies significant incidental non-cardiac findings (e.g. oesophageal and lung tumours, pulmonary embolus, aortic aneurysm) that may need further investigation and treatment

Unlike ECHO, which provides limited imaging of mediastinal and extra-cardiac structures, CMR images a substantial part of the thorax and abdomen in the field of view, which may potentially contain non-cardiac abnormalities. The prevalence of non-cardiac findings on CMR is up to 80% (with up to 30% of these representing potentially significant findings), depending on the characteristics of the population examined. 140–142