The clinical and cost-effectiveness of point-of-care tests (CoaguChek system, INRatio2 PT/INR monitor and ProTime Microcoagulation system) for the self-monitoring of the coagulation status of people receiving long-term vitamin K antagonist therapy compared with standard UK practice: systematic review and economic evaluation

Brief statement describing the health problem

People with certain clinical conditions such as atrial fibrillation or heart valve disease are at high risk of thrombosis (blood clot). Untreated, these may lead to thromboembolism affecting the brain (causing a stroke), the lungs (pulmonary embolism) or other parts of the body. Many people with these conditions are required to take lifelong blood-thinning drugs (called vitamin K antagonists) to avoid the risks associated with thrombosis. Treatment using blood-thinning drugs is termed anticoagulant therapy and it is estimated that 1.4% of the population in the UK require anticoagulant therapy. 1

Warfarin is the most common vitamin K antagonist drug given to prevent clot formation and stroke. However, serious side effects including bleeding or stroke can result from people being on the wrong dose of warfarin (over- or underdosing). Therefore, it is necessary to ensure that people taking warfarin have ongoing monitoring of their blood coaguability.

Epidemiology and prevalence

There are increasing numbers of people with atrial fibrillation, heart valve replacement or other clinical conditions requiring long-term oral anticoagulation therapy (OAT). 2 As up to 60% of people with atrial fibrillation might be undiagnosed, screening programs have the potential to increase diagnoses and associated use of OAT. 3 The prevalence of atrial fibrillation has recently been described as ‘approaching epidemic proportions’4 and it has been predicted that, by 2050, more than 5.6 million adults in the USA will be diagnosed with atrial fibrillation, compared with 2.3 million in 2001. 5 Increased use of OAT has intensified pressure upon resources, with some haematology services becoming unable to cope. 6

Impact of health problem: significance for the NHS and burden of disease

The blood coaguability of people taking warfarin is monitored by the use of the international normalised ratio (INR), which is a standardised unit for measuring the time it takes for blood to clot. INR monitoring can be delivered using various options in the NHS. The options include INR monitoring managed by health-care professionals in anticoagulant clinics based in hospitals using laboratory testing or managed in primary care (with or without the use of laboratory services). The use of a personal INR testing machine at home (known as a point-of-care test) allows people to perform self-testing (when people perform the test themselves and the results of the test are managed by health-care professionals) or self-management (when people perform the test and alter the dose of anticoagulation therapy themselves according to a personalised protocol). Self-testing and self-management are together referred to as self-monitoring. Self-monitoring is considered as one of the options for INR monitoring in the NHS, but there is limited evidence on the clinical effectiveness compared with other ways of delivering services.

The use of point-of-care coagulometers for self-monitoring may avoid unnecessary visits to hospitals while allowing regular INR monitoring and timely adjustment of warfarin dosing to avoid adverse events. For people requiring monitoring of their coagulation status, this may result in better quality of life. 17

Measurement of disease

The goal of anticoagulant therapy is to establish a balance between bleeding and clotting18 and it is desirable for people on warfarin to remain within a narrow INR therapeutic range, generally between 2.0 and 3.0. 19,20 If the dose of anticoagulation therapy is too low (underanticoagulation), the risk of thromboembolism increases, while if it is too high (overanticoagulation), the risk of haemorrhage increases. Individuals’ reactions to warfarin vary according to modifiable (e.g. diet) and non-modifiable factors (e.g. age, concomitant diseases). Adequate control of INR is necessary to avoid serious complications such as stroke. Therefore, repeated and regular measurements of INR are required to allow adjustments to size and/or frequency of dosage. 21

Summary of point-of-care tests

Point-of-care devices for measuring coagulation status in people receiving long-term vitamin K antagonist therapy allow both self-testing and self-management, defined as follows:

• Self-testing: point-of-care test carried out by the patient with test results managed by their health-care provider [e.g. general practitioner (GP), nurse, specialised clinic].

• Self-management: point-of-care test carried out by trained patient, followed by interpretation of test result and adjustment of dosage of anticoagulant according to a predefined protocol.

Self-testing and self-management are together referred to as self-monitoring for the purposes of this report.

The purpose of this assessment was to appraise the current evidence for the clinical effectiveness and cost-effectiveness of self-monitoring (self-testing and self-management) using either the CoaguChek^® system (Roche Diagnostics, Basel, Switzerland), the INRatio2^® PT/INR monitor, (Alere Inc., San Diego, CA, USA) or the ProTime Microcoagulation system^® (International Technidyne Corporation, Nexus Dx, Edison, NJ, USA), compared with standard clinical monitoring in people with atrial fibrillation or heart valve disease for whom long-term vitamin K antagonist therapy is indicated.

All of these point-of-care devices, which are currently available for use in the NHS, are CE marked and Food and Drug Administration (FDA) approved. Point-of-care instruments work basically in the same way: a drop of capillary whole blood is obtained by a finger puncture device, applied to a test strip and inserted into a coagulometer. However, they differ in terms of methods of clot detection and general operational functions.

Summary of CoaguChek system

The CoaguChek system is a point-of-care testing device developed by Roche Diagnostics and measures prothrombin time and INR (the globally recommended unit for measuring thromboplastin time) in people on oral anticoagulation (vitamin K antagonist) therapy. A low INR indicates an increased risk of blood clots, while a high INR indicates an increased risk of bleeding events. CoaguChek S and CoaguChek XS devices are intended for patient self-monitoring. The CoaguChek XS model comprises a meter and specifically designed test strips for blood sample analysis (fresh capillary or untreated whole venous blood). The CoaguChek XS system purports to have the following advantages over the CoaguChek S: (1) the thromboplastin used in the prothrombin time test strips is a human recombinant thromboplastin, which is more sensitive and has a lower International Sensitivity Index (ISI) of 1.0 compared with 1.6; (2) test strips have onboard quality control that is automatically run with every test, rather than having to perform external quality control; (3) test strips do not have to be refrigerated; (4) a smaller blood sample can be used; and (5) the meter is smaller and lighter. The CoaguChek XS Plus model is aimed primarily at health-care professionals and possesses additional features to the XS system, including increased storage and connectivity for data management.

Summary of INRatio2 PT/INR monitor

The INRatio2 PT/INR monitor performs a modified version of the one-stage prothrombin time test using a recombinant human thromboplastin reagent. The clot formed in the reaction is detected by the change in the electrical impedance of the sample during the coagulation process. The system consists of a monitor and disposable test strips and the results for prothrombin time and INR are reported.

Summary of ProTime Microcoagulation system

The ProTime Microcoagulation system is designed for measuring prothrombin time and INR. The test is performed in a cuvette which contains the reagents. Two different cuvettes are available depending on the amount of blood that needs to be collected and tested: the standard ProTime cuvette and the ProTime3 cuvette.

Identification of important subgroups

There are a number of clinical conditions which require long-term vitamin K antagonist therapy to reduce the risk of thrombosis. These conditions include atrial fibrillation and heart valve disease.

Current usage in the NHS

The National Institute for Health and Care Excellence (NICE) clinical guideline on atrial fibrillation24 recommends that self-monitoring of INR should be considered for people with atrial fibrillation receiving long-term anticoagulation, if they prefer this form of testing and if the following criteria are met:

• the patient (or a designated carer) is both physically and cognitively able to perform the self-monitoring test

• an adequate supportive educational programme is in place to train participants and/or carers

• the patient’s ability to self-manage is regularly reviewed

• the equipment for self-monitoring is regularly checked via a quality control programme.

Clinical outcomes

• Frequency of bleeds or blood clots.

• Morbidity (e.g. thromboembolic and cerebrovascular events) and mortality from INR testing and vitamin K antagonist therapy.

• Adverse events from INR testing, false test results, vitamin K antagonist therapy and sequelae.

Patient-reported outcomes

• People‘s anxiety associated with waiting time for results and not knowing their current coagulation status and risk.

• Acceptability of the tests.

• Health-related quality of life.

Intermediate outcomes

• Time and values in therapeutic range.

• INR values.

• Test failure rate.

• Time to receive test result.

• Patient compliance with testing and treatment.

• Frequency of testing.

• Frequency of visits to primary or secondary care clinics.

Identification of studies

Comprehensive electronic searches were undertaken to identify relevant reports of published studies. Highly sensitive search strategies were designed using both appropriate subject headings and relevant text word terms, to retrieve randomised controlled trials (RCTs) evaluating the point-of-care tests under consideration for the self-monitoring of anticoagulation therapy. A 2007 systematic review with similar objectives to those of the current assessment was identified in The Cochrane Library. 31 As extensive literature searches had already been undertaken for the preparation of this systematic review, the literature searches for the current assessment were run in May 2013 for the period ‘2007 to date’ to identify newly published reports. All RCTs included in the Cochrane review were obtained and included for full-text assessment. Searches were restricted to publications in English. MEDLINE, MEDLINE In Process & Other Non-Indexed Citations, EMBASE, Bioscience Information Service, Science Citation Index and Cochrane Central Register of Controlled Trials (CENTRAL) were searched for primary studies, while the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects and the Health Technology Assessment (HTA) database were searched for reports of evidence syntheses.

Reference lists of all included studies were perused in order to identify additional potentially relevant reports. The expert panel provided details of any additional potentially relevant reports.

Searches for recent conference abstracts (2011–13) were also undertaken and included the annual conferences of the American Society of Haematology, the European Haematology Association and the International Society on Thrombosis and Haemostasis, as well as the proceedings of the 12th National Conference on Anticoagulant Therapy. Ongoing studies were identified through searching Current Controlled Trials, Clinical Trials, World Health Organization, International Clinical Trials Registry and NationaI Institutes of Health Reporter. Websites of professional organisations and health technology agencies were checked to identify additional reports. Full details of the search strategies used are presented in Appendix 1.

Inclusion and exclusion criteria

The initial scoping searches performed for this assessment identified a Cochrane review31 and a few technology assessment reports21,32,33 assessing different models of managing OAT. These publications focused on several RCTs, which reported relevant clinical outcomes. In particular, the Cochrane review included both the CoaguChek S and the CoaguChek XS devices. The CoaguChek XS system is the upgraded version of CoaguChek S and uses the same technology as its precursor. Details of the performance of the two CoaguChek models compared with standard INR monitoring are provided below (see Performance of point-of-care devices).

The studies fulfilling the following criteria were included in this assessment.

Study design

We identified relevant RCTs assessing the effectiveness of the CoaguChek system, the INRatio2 PT/INR monitor and the ProTime Microcoagulation system. Therefore, non-randomised studies (including observational studies) were not considered for this assessment. Systematic reviews were used as source for identifying additional relevant studies.

Studies were excluded if they did not meet the prespecified inclusion criteria, and, in particular, the following types of report were not deemed suitable for inclusion:

• biological studies

• reviews, editorials and opinions

• case reports

• non-English-language reports

• conference abstracts published before 2012.

Data extraction strategy

Two reviewers (PS and MB) independently screened the titles and abstracts of all citations identified by the search strategies. Full-text copies of all studies deemed to be potentially relevant were obtained and assessed independently by two reviewers for inclusion (PS and MC). Any disagreements were resolved by discussion or arbitration by a third reviewer (MB).

A data extraction form was designed and piloted for the purpose of this assessment (see Appendix 2). One reviewer (PS) extracted information on study design, characteristics of participants, settings, characteristics of interventions and comparators, and relevant outcome measures. A second reviewer (MC) cross-checked the details extracted by the first reviewer. There was no disagreement between reviewers.

Assessment of risk of bias in included studies

A single reviewer (PS) assessed the risk of bias of the included studies and findings were cross-checked by a second reviewer (MC). There were a few disagreements which were resolved by consensus or arbitration by a third reviewer (MB). The reviewers were not blinded to the names of studies’ investigators, institutions and journals. Studies were not included or excluded purely on the basis of their methodological quality. The risk of bias assessment for all included RCTs was performed using the Cochrane risk of bias tool (see Appendix 3). 28 Critical assessments were made separately for all main domains: selection bias (‘random sequence generation’, ‘allocation concealment’), detection bias (‘blinding of outcome assessor’), attrition bias (‘incomplete outcome data’) and reporting bias (‘selective reporting’). The ‘blinding of participants and personnel’ was not considered relevant for this assessment due to the nature of intervention being studied (i.e. patient performing the test themselves or under supervision of health-care professionals). However, we collected information related to the blinding of outcome assessors, which was considered relevant to the assessment of risk of bias.

We judged each included study as ‘low risk of bias’, ‘high risk of bias’ or as ‘unclear risk of bias’ according to the criteria for making judgments about risk of bias described in the Cochrane Handbook for Systematic Reviews of Interventions. 28 Adequate sequence generation, allocation concealment and blinding of outcome assessor were identified as key domains for the assessment of the risk of bias of the included trials.

Data analysis

For dichotomous data (e.g. bleeding events, thromboembolic events, mortality), relative risk (RR) was calculated. For continuous data [e.g. time in therapeutic range (TTR)], weighted mean difference (WMD) was calculated. Where standard deviations (SDs) were not given, we calculated them using test statistics wherever possible. The RR and WMD effect sizes were meta-analysed as pooled summary effect sizes using the Mantel–Haenszel (M–H) method and the inverse-variance (IV) method, respectively. We also calculated 95% confidence intervals (CIs). To estimate the summary effect sizes, both fixed-effect and random-effect models were used with RR and WMD. In the absence of clinical and/or statistical heterogeneity, the fixed-effects model was selected as the model of choice, while the random-effects model was used to cross-check the robustness of the fixed-effects model. However, in the presence of either clinical or statistical heterogeneity, the random-effects model was chosen as the preferred method for pooling the effect sizes, as in this latter situation, the fixed-effects method is not considered appropriate for combining the results of included studies. 28 Heterogeneity across studies was measured by means of the chi-squared statistic and also by the I² statistic, which describes the percentage of variability in study effects that is explained by real heterogeneity rather than chance. It is worth noting that, for bleeding and thromboembolic events, we used the total number of participants who were actually analysed as denominator in the analyses. In contrast, for mortality, we used the total number of participants randomised as denominator because participants could have died due to any causes after randomisation but before entering the self-monitoring programme.

Apart from the prespecified subgroups analysis, according to the type of anticoagulation therapy management (self-testing and self-management), we performed a post-hoc subgroup analysis according to the type of the target clinical condition (i.e. atrial fibrillation, heart valve disease and mixed clinical indication) and one according to the type of service provision for anticoagulation management (i.e. primary care, secondary care and shared provision). Where trials had multiple arms contributing to different subgroups, the control group was subdivided into two groups to avoid a unit of analysis error.

Sensitivity analyses were planned in relation to some of the study design characteristics. The methodological quality (low/high risk of bias) and the different models of the CoaguChek system were identified at protocol stage as relevant aspects to explore in sensitivity analyses. In addition to those prespecified in the protocol, we performed a sensitivity analysis by excluding the studies conducted in the UK.

Review Manager software (Review Manager 5.2, 2012, The Cochrane Collaboration, The Nordic Cochrane Centre, Copenhagen, Denmark) was used for data management and all relevant statistical analyses for this assessment. Where it proved unfeasible to perform a quantative synthesis of the results of the included studies, outcomes were tabulated and described in a narrative way.

Performance of point-of-care devices

A formal evaluation of the performance of the CoaguChek, INRatio and ProTime point-of-care systems with regard to INR measurement was outside the scope of this assessment. An objective ‘true’ INR remains to be defined and usually the calculation of INR measurement is based on different assumptions. INR determined in the laboratory is regarded as the gold standard with which all other measurement methods should be compared. 34 Information on the precision and accuracy of these point-of-care devices was gathered from the available literature. Normally, the precision or reproducibility of point-of-care devices is expressed by means of the coefficient of variation (CV) of the variability, while the accuracy is the level of agreement between the result of one measurement and the true value and is expressed as correlation coefficient. 35 Table 1 summarises the performance of the target point-of-care devices according to the FDA self-test documentation and relevant published papers.

A systematic review published by Christensen and Larsen in 201235 assessed the precision and accuracy of current available point-of-care coagulometers including CoaguChek XS, INRatio and ProTime/ProTime3. The authors found that the precision of CoaguChek XS varied from a CV of 1.4% to one of 5.9% based on data from 14 studies, while the precision of INRatio and ProTime varied from 5.4% to 8.4% based on data from six studies. The coefficient of correlation for CoaguChek XS varied from 0.81 to 0.98, while that for INRatio and ProTime varied from 0.73 to 0.95. They concluded that the precision and accuracy of point-of-care coagulometers were generally acceptable, compared with conventional laboratory-based clinical testing. The same conclusions were drawn by the Canadian Agency for Drugs and Technologies in Health report published in 2012 on point-of-care testing. 41 Similarly, the international guidelines prepared in 2005 by the International Self-Monitoring Association for oral Anticoagulation stated that ‘Point-of-care instruments have been tested in a number of different clinical settings and their accuracy and precision are considered to be more than adequate for the monitoring of OAT in both adults and children’ (p. 40). 42

CoaguChek XS versus CoaguChek S

The CoaguChek S monitor was replaced in 2006 by the XS monitor, which offers a number of new technical features such as the use of a recombinant human thromboplastin with a lower ISI and internal quality control included on the test strip. The safety and reliability of CoaguChek S and CoaguChek XS have been demonstrated in several studies in both adults and children. 43–50 A number of studies have also compared the performance of CoaguChek S with that of CoaguChek XS in relation to conventional INR measurement. Even though a good agreement between the two CoaguChek models and conventional laboratory-based results has been demonstrated, CoaguChek XS has shown more accurate and precise results than its precursor in both adults and children, especially for higher INR values (> 3.5). 34,36,51–54

Quantity of available evidence

A total of 658 records were retrieved for the assessment of the clinical effectiveness of the point-of-care tests under investigation. After screening titles and abstracts, 563 were excluded and full-text reports of 120 potentially relevant articles were obtained for further assessment, including 25 full-text papers from the 18 trials included in the Cochrane systematic review published by Garcia-Alamino and colleagues. 31 In total, 26 RCTs (published in 45 papers) met the inclusion criteria and were included in the clinical effectiveness section of this assessment. Three of the 26 included studies were randomised crossover trials,32–34 while the remaining studies were parallel-group RCTs.

We based the primary analyses on data from 21 out of the 26 included studies relevant to the comparisons and outcomes of interest (Table 2 provides further details).

Of these 21 trials which provided data for statistical analyses, 15 trials were the same as those included in the Cochrane systematic review by Garcia-Alamino and colleagues31 and six were newly identified trials, published in or after 2008.

Figure 1 shows a flow diagram of the study selection process. The list of 26 included RCTs (and other linked reports) is given in Appendix 4.

FIGURE 1.

Flow diagram outlining the selection process.

Quality of research available

Figure 2 illustrates a summary of the risk of bias assessment for all included studies. The majority of trials were judged at ‘unclear’ or ‘high’ risk of bias. One trial was only reported in abstract and hence did not allow for an adequate assessment of the risk of bias. 59 Similarly, one trial was discontinued before the end of the prespecified follow-up due to difficulties in the recruitment process. 60 Overall, only four trials were assessed to have adequate sequence generation, concealed allocation and blinded outcome assessment and, therefore, were judged at low risk of bias. 55,61–63 Three of these trials used either the CoaguChek model ‘S’61,63 or the model ‘XS’62 for INR measurement, while the other trial used CoaguChek XS to measure INR in children receiving anticoagulation therapy. 55 Appendix 6 provides details of the risk of bias assessment for each individual study. Main findings of the risk of bias assessment for all included studies are described in detail below.

FIGURE 2.

Summary of risk of bias of all included studies.

Characteristics of the included studies

Table 3 summarises the main characteristics of the 26 included RCTs. The baseline characteristics of all included trials are described below and tabulated in Appendix 7 (see Tables 31 and 32).

Clinical effectiveness results

Clinical outcomes

Bleeding

Twenty-one trials reported a total of 1472 major and minor bleeding events involving 8394 participants. 59–79 Two trials reported that there were no bleeding events and hence did not contribute to the overall effect size in the related meta-analysis. 66,75 Twenty-one trials reported 476 major bleeding events in a total of 8202 participants,59–65,67–74,77–79 while 13 trials reported 994 estimable minor bleeding events in a total of 5425 participants. 61,63,64,67,69,71–74,76,77 No statistically significant differences were observed between self-monitoring participants (self-testing and self-management) and those in standard care for any bleeding events (RR 0.95, 95% CI 0.74 to 1.21; p = 0.66) (Figure 3), major bleeding events (RR 1.02, 95% CI 0.86 to 1.22; p = 0.80) (Figure 4) and minor bleeding events (RR 0.94, 95% CI 0.65 to 1.34; p = 0.73) (Figure 5). The results were not affected by the removal of the UK-based trials (see Appendix 8) or by the removal of the trials assessing ProTime and/or INRatio (see Table 6 and Appendix 8). Similarly, sensitivity analyses restricted to CoaguChek XS trials demonstrated no differences from the all-trials results (see Table 6 and Appendix 8). A sensitivity analysis restricted to trials at low risk of bias slightly changed the estimate of effect but did not significantly impact on the findings (RR 0.59, 95% CI 0.27 to 1.30; p = 0.19) (see Appendix 8).

FIGURE 3.

Forest plot of comparison: any bleeding events. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus; PSM, patient self-management; PST, patient self-testing.

FIGURE 4.

Forest plot of comparison: major bleeding events. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus; PSM, patient self-management; PST, patient self-testing.

FIGURE 5.

Forest plot of comparison: minor bleeding events. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

The subgroup analysis by type of anticoagulant management therapy did not show any difference between self-management and standard care for any bleeding events (RR 0.94, 95% CI 0.68 to 1.30; p = 0.69) but revealed a significant higher risk in self-testing participants than in those receiving standard care (RR 1.15, 95% CI 1.03 to 1.28; p = 0.02) (see Figure 3). When trials assessing ProTime and INRatio were removed from the analysis, a non-significant trend was observed in favour of self-testing (0.58, 95% CI 0.22 to 1.47; p = 0.25) (see Table 6 and Appendix 8). No significant differences in the risk of major bleeding were observed between self-management (RR 1.09, 95% CI 0.81 to 1.46; p = 0.58) and self-testing (RR 0.99, 95% CI 0.80 to 1.23) versus standard care (see Figure 4). When only minor bleeding events were assessed (see Figure 5), a significant increased risk was observed in self-testing participants (23%), compared with those in standard care (RR 1.23, 95% CI 1.06 to 1.42; p = 0.005), but not in those who were self-managed (RR 0.84, 95% CI 0.53 to 1.35; p = 0.47). Two trials enrolled participants with atrial fibrillation, six trials enrolled participants with AHVs and 13 trials enrolled participants with mixed indication. No statistically significant subgroup differences were found for bleeding events according to the type of clinical indication (Figure 6). Similarly, for bleeding events, no significant differences were detected when trials were grouped according to the type of control care (anticoagulant clinic care RR 0.84, 95% CI 0.50 to 1.42; p = 0.52; GP/physician RR 1.09, 95% CI 0.79 to 1.50; p = 0.60; mixed care RR 0.94, 95% CI 0.79 to 1.13; p = 0.54) (Figure 7).

FIGURE 6.

Forest plot of comparison: any bleeding events – clinical indication. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

FIGURE 7.

Forest plot of comparison: any bleeding events – type of standard care. AC, anticoagulant; C, CoaguChek; CS, CoaguCchek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

Thromboembolic events

Twenty-one trials reported 351 major and minor thromboembolic events in a total of 8394 participants. 59–79 Six of these trials did not contribute to the overall estimate of effect as they reported ‘zero’ events in both groups. 66–69,74,75 Self-monitoring (self-testing and self-management) showed a statistically significant reduction in the risk of thromboembolic events by 42% (RR 0.58, 95% CI 0.40 to 0.84; p = 0.004), compared with standard care (Figure 8). The risk reduction further increased to 48% when only major thromboembolic events were considered (RR 0.52, 95% CI 0.34 to 0.80; p = 0.003) (Figure 9). The risk of thromboembolic events significantly decreased when the analyses were restricted to non-UK trials (RR 0.50, 95% CI 0.32 to 0.76; p = 0.001); to CoaguChek trials (RR 0.52, 95% CI 0.38 to 0.71; p < 0.0001); and to trials at low risk of bias (RR 0.38, 95% CI 0.16 to 0.92; p = 0.03) (see Appendix 8).

FIGURE 8.

Forest plot of comparison: thromboembolic events. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus; PSM, patient self-management; PST, patient self-testing.

FIGURE 9.

Forest plot of comparison: major thromboembolic events. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus; PSM, patient self-management.

Self-management compared with standard care halved the risk of thromboembolic events (RR 0.51, 95% CI 0.37 to 0.69; p < 0.0001). In contrast, for self-testing participants59,62,71,77 no significant risk reduction was observed compared with those in standard care (RR 0.99, 95% CI 0.75 to 1.31; p = 0.95) (see Figure 8). The subgroup difference between self-management and self-testing was statistically significant (p = 0.002). When trials assessing the ProTime system were removed from the analysis, the risk reduction increased from 1% to 45% but the summary estimate of effect was not statistically different from the all-trials summary estimate (RR 0.55, 95% CI 0.13 to 2.31; p = 0.41) (see Table 6 and Appendix 8). Self-monitoring participants with AHVs showed a significant reduction in the number of thromboembolic events, compared with those in standard care (RR 0.56, 95% CI 0.38 to 0.82; p = 0.003). Among participants with mixed clinical indication (atrial fibrillation, AHVs or other conditions), the effect was larger but not statistically significant than that observed in participants receiving standard care (RR 0.57, 95% CI 0.30 to 1.09; p = 0.09) (Figure 10). The risk of thromboembolic events reduced in self-monitoring participants by 55% when routine anticoagulation control was managed by a GP or physician (RR 0.45, 95% CI 0.29 to 0.68; p = 0.0002). In contrast, even though fewer thromboembolic events were observed in participants who self-monitored their therapy than in those managed in specialised anticoagulation clinics (RR 0.65, 95% CI 0.30 to 1.42; p = 0.28) or those in mixed provision managed by either a physician/GP or a specialist (RR 0.66, 95% CI 0.31 to 1.38; p = 0.27), no significant subgroup differences were detected (Figure 11).

FIGURE 10.

Forest plot of comparison: any thromboembolic events – clinical indication. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

FIGURE 11.

Forest plot of comparison: any thromboembolic events – type of standard care. AC, anticoagulation; C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

Mortality

Thirteen trials reported 422 deaths due to all-cause mortality in a total of 6537 participants. 61,63,64,66,67,70–75,77,79 Two trials with zero fatal cases did not contribute to the overall estimate of effect. 66,74 One trial of 1200 participants70 reported overall mortality data without separating the results for participants self-managed and for those receiving standard care. We contacted the corresponding author of this trial for further information but we did not receive any reply. Therefore, for mortality data, for this particular trial, we relied on the estimates published in the previous meta-analysis by Garcia-Alamino and colleagues31 and in the HTA by Connock and colleagues,21 which were based on individual patients’ data.

The risk reduction for all-cause mortality was not statistically significant different between self-monitoring (self-testing and self-management) and standard care (RR 0.83, 95% CI 0.63 to 1.10; p = 0.20) (Figure 12). The results were not affected by the removal of the UK-based trials or by the removal of trials at high or unclear risk of bias. When the analysis was restricted to trials that used the CoaguChek system, the summary estimate for self-monitoring was not different from the all-trials estimate (RR 0.68, 95% CI 0.46 to 1.01, p = 0.06) (see Table 6 and Appendix 8). Two trials reported six deaths out of a total of 932 participants, related to vitamin K antagonist therapy. 61,63

FIGURE 12.

Forest plot of comparison: mortality. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

Risk of death reduced by 32% through self-management (RR 0.68, 95% CI 0.46 to 1.01; p = 0.06) but not through self-testing (RR 0.97, 95% CI 0.78 to 1.19; p = 0.74), even though the test for subgroup differences was not statistically significant (p = 0.13) (see Figure 12). Self-monitoring halved the risk of mortality in participants with AHVs (RR 0.54, 95% CI 0.32 to 0.92; p = 0.02) but not in those with mixed clinical indication for AOT (RR 0.95, 95% CI 0.78 to 1.16; p = 0.61) (Figure 13). The subgroup difference between participants with AHVs and those with mixed indication with regard to the number of deaths was statistically significant (p = 0.05). No data were available from trials that enrolled participants with atrial fibrillation. Significantly fewer deaths were recorded among participants who self-monitored their therapy than among those who were routinely managed by their GP/physician (RR 0.52, 95% CI 0.30 to 0.90; p = 0.02) (Figure 14).

FIGURE 13.

Forest plot of comparison: mortality – clinical indication. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

FIGURE 14.

Forest plot of comparison: mortality – type of standard care. AC, anticoagulation; C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’; CPlus, CoaguChek Plus.

Heterogeneity among trials

A significant statistical heterogeneity was observed for any bleeding outcomes (I² = 66%, p < 0.0001). In contrast, there was no statistically significant heterogeneity across trials for thromboembolic outcomes (I² = 36%, p = 0.08) or for mortality (I² = 11%, p = 0.34). The summary estimates of effect were influenced considerably by five large trials: Eitz and colleagues,78 Körtke and colleagues,70 Fitzmaurice and colleagues64 and Menéndez-Jándula and colleagues61 for self-management, and Matchar and colleagues71 for self-testing. The trial by Matchar and colleagues,71 which was the largest trial on self-testing, did not show any significant difference between self-testing and standard care with regard to the incidence of major events. Standard care was provided by means of high-quality clinic testing in this trial (a designated, trained staff responsible for participants’ visits and follow-up; the use of a standard local procedure at each site for anticoagulation management; and the performance of regular INR testing about once a month). The estimated effect of self-testing versus standard care in the subgroup analysis was dominated by this large trial, and, therefore, interpretation of this finding requires caution.

Adverse events

No other adverse events from INR testing, false test results, vitamin K antagonist therapy and sequelae were reported in the included trials.

Intermediate results

Anticoagulation control: target range

Anticoagulation control can be measured as the time that INR is in the therapeutic range or as INR values in therapeutic range. Data on INR TTR were available from 18 trials. 55,56,58,60–69,71,73–75,77 However, there was variation in the measures used for reporting TTR. Seven trials comparing self-monitoring with standard care reported TTR as mean percentage;61,64,65,69,71,74,77 three as median percentage,58,62,75 five as overall percentage63,66–68,73 and one as cumulative number of days. 60 The two remaining trials, which compared patient self-management with patient self-testing, reported the TTR as mean percentage time (one trial)55 and overall percentage time (the other trial). 56 It proved impossible to convert median values into mean values due to the lack of information on the maximum or minimum value required by the conversion formula. Therefore, we were unable to pool the TTR results from the 18 trials which provided this information. The results of these trials are shown in Table 7.

TABLE 7 - International normalised ratio results (TTR and INR values in target range)

IQR, interquartile range; NR, not reported; NS, not significant; OW, once weekly; PSM, patient self-management; PST, patient self-testing; TW, twice weekly.

SD was calculated from 95% CI values given in the trials.

Note

Gadisseur 2003:68 TTR for untrained control arm was 63.5% (95% CI 59.7% to 67.3%) (SD 24.6%).

Type of point-of-care test	Study ID and country	Measure	INR TTR				INR value in target range
			PSM/PST	Control	Difference	p-value	Measure	PSM/PST	Control	Difference	p-value
CoaguChek XS	Bauman 2010,55 Canada (children only)	Mean % (95% CI)	PSM: 83	PST: 83.9	1 (–7.7 to 9.7)		NR
	Christensen 2011,66 Denmark	aOverall days % (95% CI) (SD)	PST-OW: 79.7 (79 to 80.3) (2.3) PST-TW: 80.2 (79.4 to 80.9) (2.3)	72.7 (71.9 to 73.4) (2.6)	7 (6 to 7.9) (73% to 80%)	< 0.001	% of INR values (95% CI)	PST-OW: 78.3 (76.5 to 80.1) PST-TW: 80.8 (79.3 to 82.1)	67.2 (64.1 to 70.2)		< 0.001
	Ryan 2009,62 Ireland	Median % (IQR)	74 (64.6–81)	58.6 (45.6–73.1)		< 0.001	NR
	Verret 2012,74 Canada	Mean % (SD)	80 (13.5)	75.5 (24.7)		0.79	NR
CoaguChek S or CoaguChek	Christensen 2006,75 Denmark	Median % (95% CI)	78.7 (69.2 to 81.0)	68.9 (59.3 to 78.2)		0.14	NR
	Cromheecke 2000,76 Netherlands	Values NR				NS	% of INR values	55	49	OR 1.2 (95% CI 1.0 to 1.6)	0.06
	Eitz 2008,78 Germany	NR					% of INR values	79	65		< 0.001
	Fitzmaurice 2002,67 UK	% (95% CI) (SD)a	74 (67 to 81) (16.2)	77 (67 to 86) (23.5)		NS	% of INR values (95% CI)	66 (61 to 71)	72 (65 to 80)		NS
	Fitzmaurice 2005,64 UK	Mean % (95% CI) (SD)a	70 (68.1 to 72.4) (20.1)	68 (65.2 to 70.6) (23.0)	2.4 (–1.2 to 6.0)	0.18	Mean % of individual (95% CI)	70 (64.8 to 74.8)	72 (66.3 to 77.1)		NS
	Gadisseur 2003,68 Netherlands	% (95% CI) (SD)a	PSM: 68.6 (63.7 to 73.6) (16.8) PST: 66.9 (62.7 to 71.0) (14.9)	67.9 (62.9 to 73.0) (19.5)	PST: 3.4 (–2.7 to 8.9) PSM: 5.1 (–1.1 to 11.3), p < 0.5	0.33	% (95% CI)	66.3 (61–71.5)/63.9 (59.8–68)	61.3 (55–62.4)/58.7	PST: + 5.2 (–1.7 to 12.1) PSM: + 7.6 (0.1 to 14), p < 0.5	0.14
	Gardiner 2006,56 UK	% (95% CI) (SD)	PSM: 69.9 (60.8 to 76.7) (23.1)	PST: 71.8 (64.9 to 80.1) (22.1)	PSM + PST: (n = 77): 71 (64.7 to 76.4) (22.5)	0.46	NR
	Khan 2004,69 UK	Mean % (SD)	71.1 (14.5)	70.4 (24.5)		NS	NR
	Horstkotte 1996,59 Germany	NR					% of INR values	43.2	22.3		< 0.001
	Menéndez-Jándula 2005,61 Spain	Mean % (SD)	64.3 (14.3)	64.9 (19.9)		0.2	Mean % of individual (SD)	58.6 (14.3)	55.6 (19.6)	95% CI 0.4 to 5.4	0.02
	Rasmussen 2012,58 Denmark	Median (25th–75th percentile) %	52 (33–65)	55 (49–66)			NR
	Sawicki 1999,72 Germany	NR					% of individual	53	43.2		0.22
	Sidhu 2001,73 UK	%	76.5	63.8		< 0.0001	NR
	Siebenhofer 2008,63 Austria	% (IQR) 6/12 months	70.6 (60.9–83.9)/75.4 (9.4–85.0)	57.5 (34.2–80.3)/66.5 (47.1–81.5)		< 0.001/0.029	NR
	Soliman Hamad 2009,79 Netherlands	NR					Mean % per patient (SD)	72.9 (11)	53.9 (14)		0.01
	Völler 2005,60 Germany	Mean cumulative days (SD)	178.8 (126)	155.9 (118.4)		NS	Mean % of INR values (SD)	67.8 (17.6)	58.5 (19.8)		0.0061
CoaguChek Plus	Körtke 2001,70 Germany	NR					% of INR values	79.2	64.9		< 0.001
CoaguChek/INRatio	Azarnoush 2011,77 France	Mean % (SD)	61.5 (19.3)	55.5 (19.9)		0.0343	NR
	Hemkens 2008, Germany57	NR					NR
ProTime	Matchar 2010,71 USA	Mean % (SD)	66.2 (14.2)	62.4 (17.1)	3.8 (95% CI 2.7 to 5.0)	< 0.001	NR
	Sunderji 2004,65 Canada	Mean % (SD)	71.8 (45.69)	63.2 (48.53)		0.14	NR

Time in therapeutic range ranged from 52%58 to 80%66,74 for self-monitoring and from 55%58 to 77%67 for standard care. In all but three trials,58,61,67 TTR was higher in self-monitoring participants than in those receiving standard care and, in five of these trials, the difference between intervention groups was statistically significant. 62,66,71,73,77 Three of the UK-based trials reported no significant differences between self-monitoring and standard care. 64,67,69 Pooling of results was possible for 10 trials that provided suitable data. 61,64–69,71,74,77 No statistically significant differences were found between self-management and standard care (RR 0.47, 95% CI –1.40 to 2.34; p = 0.62). A modest but significantly higher proportion of TTR was, however, found for participants assigned to self-testing than for those in control care (WMD 4.44, 95% CI 1.71 to 7.18; p = 0.001) (Figure 15). It is worth noting that the overall estimate of effect was dominated by the largest included trial on self-testing, THINRS. 71 In two trials, one using CoaguChek XS66 and the other using ProTime,71 the WMD between self-testing and standard care for TTR was significantly higher, indicating better anticoagulation control among self-testing participants.

FIGURE 15.

Forest plot of comparison: TTR. C, CoaguChek; CS, CoaguChek ‘S’; CXS, CoaguChek ‘XS’.

The INR values in therapeutic range were reported in 12 trials. 59–61,64,66–68,70,72,76,78,79 There was great variation between trials in the measures used to assess INR values in therapeutic range and, therefore, the pooling of data across trials proved unfeasible. In eight trials which reported the proportion of INR measurements in the therapeutic range,59,60,66–68,70,76,78 the values ranged from 43.2%59 to 80.8%66 for self-monitoring and from 22.3%59 to 72%67 for standard care. In four trials that reported the proportion of participants in the therapeutic range instead,61,64,72,79 the values ranged from 53%72 to 72.9%79 for self-monitoring and from 43.2%79 to 72%64 for standard care. With the exception of two UK-based trials,64,67 all trials reported higher proportion of INR measurements or larger proportions of participants in therapeutic range for self-monitoring than for standard care. Significant differences between interventions were detected in six of these trials. 60,61,66,70,78,79 The INR values in therapeutic range are summarised in Table 7.

Among participants with AHVs, self-monitoring resulted in a significantly higher INR TTR73,77 or INR values in therapeutic range59,70,78,79 than standard care. In two trials that included participants with atrial fibrillation,60,69 no TTR differences were found between self-monitoring and standard care.

Patient-reported outcomes

Critique of the included studies

Gailly 200933

The objective of this study was to conduct a cost-effectiveness analysis of the use of point-of-care devices for GP-managed care, anticoagulation clinic-managed care, patient self-testing and patient self-management, compared with standard laboratory testing. The analysis focused on a cohort of patients on long-term anticoagulation therapy. A decision-tree model, with a 10-year time horizon, was constructed from the perspective of a Belgian health-care provider. The models input parameters were estimated from a meta-analysis of published studies for clinical effects and Belgian health-care databases for baseline risks and resource use.

As the meta-analysis of clinical effectiveness studies only identified evidence for a significant impact of point-of-care testing on mortality for patient self-management, the cost-effectiveness analysis focused on this modality of monitoring versus usual care (GP-managed testing with analysis of the blood sample in a laboratory). Furthermore, the outcome measure was restricted to the number of life-years gained as it was reported that no reliable quality-of-life data were identified. The annual number of point-of-care tests and the number of GP consultations due to INR tests in usual care and patient self-management were varied in a sensitivity analysis.

Applying the significant beneficial effects of self-management on mortality and thromboembolic events, the results showed self-management to be the dominant strategy compared with usual care, except when 100% of the GP consultations observed in usual care were assumed to be maintained with patient self-management and when the annual number of tests with self-management increased to 52 per year. The probabilistic sensitivity analysis showed patient self-management to have a high chance of being a dominant cost-saving strategy in comparison with usual care.

Medical Advisory Secretariat 200988

This Canadian study assessed the cost–utility of health service point-of-care testing, patient self-testing and patient self-management versus standard care for patients on long-term anticoagulation therapy. A Markov decision-analytic model was developed with a 5-year time horizon, and was analysed from the perspective of the Ministry of Health and Long-Term Care. The model was analysed for a hypothetical cohort of patients, and model inputs were derived from a systematic review of effectiveness, other published literature and expert opinion. Time spent within and outside the therapeutic range was used to estimate the likelihood of patients moving from one health state to another. The results indicated that all of the evaluated point-of-care strategies were cost-effective compared with usual care, and that patient self-management appeared to be the most cost-effective strategy.

Other studies

In addition to the above published evaluations, two abstracts were identified for potential relevance. Visnansky and colleagues90 conducted a rapid HTA to explore the cost-effectiveness of patient self-testing using CoaguChek compared with standard care (laboratory testing). A Markov model was constructed and analysed for hypothetical cohorts (mean age 63 years) on long-term anticoagulation therapy for different indications, applying a lifetime horizon. The authors concluded that patient self-testing was a cost-effective (dominant) strategy compared with usual care for all diagnosis subgroups.

Schmidt and colleagues91 conducted a cost–utility analysis of patient self-management compared with standard monitoring among long-term OAT patients in an Austrian setting. A Markov model was constructed adopting a lifetime horizon with an average baseline age of 67 years. This study found that although self-management incurred higher costs initially, throughout follow-up these costs reduced due to the lower number of health-care contacts over time. Adopting a lifetime perspective, it was found that self-management was the dominant strategy based on both a cost-per-life-year and a cost-per-QALY analysis.

Summary of findings from identified studies

The above overview of existing economic evaluations illustrates that the cost-effectiveness of patient self-testing and self-management versus usual care is uncertain and largely dependent on a number of key factors.

The adopted perspective appears to have a significant impact on estimated cost-effectiveness. Existing studies have estimated costs from different perspectives, including those of health service providers, society as a whole, health-care payers and health insurance funds. When a wider societal perspective has been adopted, self-management and self-testing strategies have generally compared favourably with standard clinic-based testing, as a result of lower time costs associated with fewer health service contacts. The initial costs associated with patient self-management and self-testing also appear to be important determinants of cost-effectiveness.

Variation between the studies in terms of the estimated or assumed effects of self-monitoring (on thromboembolic and bleeding events) also helps to account for the variable findings and conclusions. The two UK-based evaluations of greatest relevance to the scope of this diagnostic assessment report (DAR)21,87 estimated or applied effect estimates consistent with small or negligible differences between self-management and usual care with respect to TTR and adverse thromboembolic and haemorrhagic events. They subsequently found there to be a low probability of patient self-management being cost-effective. Contrary to this, several studies applied large effect estimates favouring self-monitoring in terms of TTR, thromboembolic events and/or mortality, and, subsequently, found self-monitoring strategies to be cost-saving or cost-effective. 33,86,88

In relation to the scope of this assessment, the two most relevant studies are those reported by Jowett and colleagues87 and Connock and colleagues. 21 These economic evaluations were largely based on the same trial conducted in the UK. Jowett and colleagues adopted a NHS and wider societal perspective and Connock and colleagues21 adopted a health service and personal social services perspective, which was in line with the NICE reference case. Key outcomes were measured directly within the trial-based evaluation, including utility values and complications experienced. Self-testing and self-management strategies do appear to increase the costs of INR monitoring in the short run, as demonstrated by these studies and others. However, other studies have shown that these costs can be offset by future cost-saving and quality-of-life gains, depending on the relative effectiveness of self-monitoring versus usual care in reducing the incidence of mainly thromboembolic events.

The two UK-based economic evaluations suggest that for effect estimates consistent with those observed in the largest UK-based trial of patient self-management, self-monitoring of INR is unlikely to be cost-effective. However, no UK-based trials have been sufficiently powered to detect a significant difference between standard INR monitoring and patient self-monitoring in terms of major thromboembolic or haemorrhagic events. Meta-analysis of similar trials, given their rarity, provides a more powerful means of estimating the true effect of self-monitoring on these clinical outcomes. An updated meta-analysis was described and presented in Chapter 2, and included randomised evidence from a number of recent European trials where standard care is similar to that provided in the UK in terms of approach, frequency and the level of INR control achieved. Therefore, the following section describes the construction and analysis of a new economic model that builds on those described above, and which incorporates all of the available evidence on the clinical effectiveness of self-testing and self-monitoring.

Relevant patient population(s)

The model compared the alternative monitoring strategies for a hypothetical cohort of people with atrial fibrillation or an AHV. These two groups represent the majority of people on long-term vitamin K antagonist therapy. While self-monitoring of INR is relevant to other patient groups, including those with venous thrombotic embolism, there were insufficient data to explicitly model cost-effectiveness for all groups individually. Furthermore, the majority of studies informing the relative effects of alternative monitoring strategies were derived from trials including predominantly people with atrial fibrillation and/or an AHV. Therefore, the base-case modelling exercise was carried out for a mixed cohort consisting of people with one or other of these two conditions. In the base-case analysis, 60% of the cohort was modelled to have atrial fibrillation, with the remaining 40% having an AHV, in line with the observed proportions of patients with these conditions in self-monitoring trials.

Monitoring strategies to be evaluated

The economic model incorporated the pathways of care that individuals currently follow under standard practice in the NHS, as well as proposed new pathways for self-testing and self-management (informed by a review of current guidelines and expert opinion). Current practice was dichotomised in the model as standard monitoring in primary care and standard monitoring in secondary care. In the base-case analysis, the proportional split between standard primary and secondary care INR monitoring was taken from the manufacturer’s submission for technology appraisal 256 (TA256). 96 Based on a survey of providers in England and Wales carried out in 2011, it was estimated that 66.45% and 33.55% of warfarin monitoring appointments were managed in a primary and secondary care setting, respectively. These figures were accepted by the independent evidence review group (ERG) and appraisal committee for NICE TA256. 97

In terms of self-monitoring, the model incorporated both self-testing and self-management strategies using the alternative devices identified in the scope. However, the cost-effectiveness of self-monitoring was assessed as a whole, and it was assumed in the base-case analysis that 50% of people would self-test while 50% would self-manage. Self-testing and self-management strategies were costed separately for each device based on the assumption that self-testing people telephone in their results from all tests undertaken, while self-managing people manage their dosing independently. In reality, some self-monitoring people are likely to fall somewhere in between these two strategies, and the potential impact of this was addressed in sensitivity analysis by varying the proportional split between self-management and self-monitoring.

Framework (method of synthesis)

The alternative monitoring pathways, informed by review of previous guidance and expert opinion, were embedded in a Markov model simulating the occurrence of adverse events over time (Figure 16). The adverse events that constituted the model were ischaemic stroke (minor, non-disabling, and major, disabling or fatal), systemic embolism, minor haemorrhage and major haemorrhage [intracranial haemorrhage (ICH), including haemorrhagic stroke, gastrointestinal bleed, and others]. Systemic embolism was treated as a transient event within the model, such that people surviving this event returned to baseline levels of quality of life and did not incur ongoing costs and morbidity. Minor haemorrhage was handled in the same way. Ischaemic stroke and ICH were assigned post-event states associated with additional costs and quality-of-life decrements.

FIGURE 16.

Schematic of the model structure. AF, atrial fibrillation; HS, haemorrhagic stroke; M, Markov process.

The model simulated transitions between the discrete health states, and accumulated costs and QALYs on a quarterly (3-month) cycle. Within each 3-month cycle, the simulated cohort was exposed to a risk of the aforementioned events as well as death from other causes. A constraint was applied whereby simulated people could experience only one event per cycle. A further simplifying structural assumption was applied, such that, following a major ischaemic stroke or ICH, no further events were explicitly modelled. However, all-cause mortality was inflated following these events to account for the increased risk of death.

Baseline risks for the modelled events were derived from the observed event rates in cohorts of people being managed under current standard models of care. RRs of these events resulting from improved/reduced INR control, conferred by self-monitoring, were derived from the meta-analysis of RCTs of self-monitoring versus standard practice. Appropriate costs and quality-of-life weights were attached to modelled events and health states, allowing cumulative health and social care costs and QALYs to be modelled over time. Further details of the event risks, transitions, costs and quality-of-life weights applied in the model are provided in the following sections.

Modelled baseline risks for people with atrial fibrillation

Previous economic models relied on a variety of sources to inform the underlying baseline risks of adverse events, ranging from single-centre trials to data pooled from a number of trials. The unpublished model provided by Roche made use of event rates reported by TTR,98–100 based on data from the control arms of large multinational trials comparing new anticoagulant drugs with standard treatment with warfarin for people with atrial fibrillation.

The Randomised Evaluation of Long-term Anticoagulation Therapy (RE-LY) trial of dabigatran etexilate versus warfarin provides a detailed source of event-rate data by centre-level quartiles of mean TTR. 99,101 The advantage of these data is that they allow underlying risks to be modelled by the level of anticoagulation control achieved, but there is a question surrounding their generalisability to the atrial fibrillation population on warfarin therapy in the UK. However, a previous study assessed the representativeness of the RE-LY clinical trial population to real-world atrial fibrillation patients in the UK,102 and found that the majority of patients in the UK (65–74%) would have met the inclusion criteria. Furthermore, to assess the generalisability of the annual risks of stroke derived from RE-LY data, these were compared with those derived from a large cohort study of atrial fibrillation patients on warfarin in the UK. Gallagher and colleagues103 analysed longitudinal data from the General Practice Research Database on 27,458 warfarin users with atrial fibrillation, and provided a Kaplan–Meier plot of the probability of being stroke free by different levels of TTR. Points on these plots were extracted using DigitizeIt software (DigitizeIt, Braunschweig, Germany: www.digitizeit.de), and used to estimate the annual risks of stroke by TTR groupings.

These stroke risks were found to be very similar to those for people in the corresponding TTR quartiles of the RE-LY trial control arm. Therefore, the control arm of the RE-LY trial was considered to be an appropriate source for estimating baseline risks by level of TTR in the economic model. The study by Gallagher and colleagues103 also estimated a mean TTR (INR 2–3) of 63% for the UK cohort of people with atrial fibrillation on warfarin, and so the baseline risks in the model were set to those observed in RE-LY trial centres that achieved a mean TTR between 57.1% and 65.5%.

The analysis of RE-LY trial data by TTR quartiles99 provided estimated annual event rates for non-haemorrhagic stroke and systemic embolism, major haemorrhage (including intracranial bleed, haemorrhagic stroke and major gastrointestinal bleeds) and minor haemorrhage. These rates were entered in the model, where they were converted into annual risks (Table 14). Following further adjustment, where appropriate, with RRs, the annual risks were converted into quarterly risks using the following equation:

The events were modelled within each cycle of the model, and were further disaggregated based on the observed numbers of different types of event observed within each composite outcome in the RE-LY trial99,101 (Table 15).

Further adjustments were applied to the risk of stroke in atrial fibrillation patients, to reflect the importance of age as a risk factor. For this purpose, the same approach as used in the model for NICE TA256 (rivaroxaban for the prevention of stroke and systemic embolism in people with atrial fibrillation) was applied. 96 RRs of stroke by age, compared with a 70- to 74-year-old cohort (the average age of participants in RE-LY trial), were derived from a Framingham-based risk score calculator for patients with atrial fibrillation,106 and applied to adjust the risk of stroke and systemic embolism by 5-year age bands. 96 A similar approach was also used to inflate the risk of bleeding with increasing age, using data from Hobbs and colleagues. 107

Death following stroke was estimated by applying case fatality rates to these modelled events. Death following stroke utilised the same approach as used in the model of dabigatran versus warfarin for NICE TA249. 105 Based on Hylek,104 the hospital case fatality rate was first applied, followed by the reported 30-day mortality by severity of stroke (Rankin score 0–2; 3–5) post discharge (see Table 15).

Modelled baseline risks for people with an artificial heart valve

Less extensive data were identified describing the baseline risk of adverse events for people with AHVs by level of INR control. Previous economic models have tended to use overall event risks for mixed cohorts rather than explicit event risks for individual patient groups included in the modelled cohort. However, the model provided by Roche used a dichotomised cohort with event risks estimated separately for people with atrial fibrillation and an AHV. This approach is useful for modelling subgroups and cohorts with varying proportions of people with the two conditions. Therefore, the same general approach was adopted.

As per the model provided by Roche (J Craig, York Health Economics Consortium, 2013, personal communication provided by Roche through NICE), a recent meta-analysis of individual patient-level data from 11 RCTs of self-monitoring versus standard care provided the source of event data. 108 Heneghan and colleagues108 presented a subgroup analysis where they presented the estimated pooled hazard ratio and number needed to treat to prevent one major thromboembolic event (ischaemic stroke and systemic embolism) and one major haemorrhagic event by year of follow-up (up to 5 years) based on 2243 people with an AHV. The formula used by Heneghan and colleagues108 to estimate the number needed to treat was:

Sc(t) is the survival probability in the control group (standard monitoring) at time t, Sc(t)^h is the corresponding survival probability in the active treatment group (self-monitoring), and h is the hazard ratio. The 5-year probability of experiencing a thromboembolic (0.089) and major haemorrhagic event (0.169) in the control group were back calculated for people with an AHV, and converted into annual probabilities (Table 16). These were incorporated in the model for subsequent adjustment and conversion into quarterly probabilities for use as baseline risks.

A focused search was undertaken to identify alternative sources of data to inform the baseline risk of thromboembolic events in people with an AHV. A previous meta-analysis estimated a pooled annual linearised risk of 1.6% for people with a mechanical aortic valve. A further large Canadian series (including 1622 people with a mechanical heart valve) estimated linearised embolic stroke risks of 1.4% and 2.3% per year for people with an artificial aortic and a mitral valve, respectively. 109 These figures are generally consistent with the baseline estimates used in the model. However, a smaller series from a single centre in the south-west of England reported a lower rate of 1.15% per patient-year based on 2 years’ follow-up of 567 people with a Sorin Bileaflet third-generation prosthesis. 110 The impact of applying this lower baseline risk was assessed through sensitivity analysis.

In the absence of more detailed data for people with an AHV, the same proportional splits used to disaggregate thromboembolic and major haemorrhagic events for people with atrial fibrillation were applied (see Table 15). Furthermore, as data on minor bleeds were not available from Heneghan and colleagues108 for people with an AHV, the same baseline risk applied for people with atrial fibrillation was adopted. This was justified on the grounds of the two groups of people facing similar risks of a major bleed (0.405 and 0.363).

Further adjustments to baseline risks

Within the model, a number of simplifying structural assumptions were made. Following the occurrence of a major disabling ischaemic stroke or an ICH/haemorrhagic stroke, no further events were modelled. However, the risk of age-/sex-specific all-cause mortality was inflated following these events using RRs estimated by Sundberg and colleagues. 111 Deaths from other causes following minor stroke were also inflated in the model to account for the observed increased risk of death from all causes following this event. 111,112

The background risk of death from other causes also was increased for the atrial fibrillation and AHV cohorts using standardised mortality ratios reported by Friberg and colleagues113 and Kvidal and colleagues114 (Table 17).

Baseline rates of death from all and other causes were modelled by age and sex based on interim life tables. For other cause mortality, deaths due to stroke, systemic embolism and ICH were removed. 116,117

Incorporation of relative treatment effects

Pooled estimates of RR derived from the meta-analysis of RCTs of self-monitoring versus standard practice were used to adjust the baseline risks of events in the model (Table 18). Given the limitations of the available data, it was not possible to accurately estimate the relative clinical effectiveness of using the alternative self-monitoring devices. Therefore, in the first instance, equivalent effects were assumed on the basis of several studies showing reasonable correlation between the instruments in terms of precision and accuracy. However, it is worth noting that the majority of the clinical effectiveness evidence relates to CoaguChek S, with only one trial included in the systematic review using the INRatio2 PT/INR monitor (although not exclusively), and two trials using the ProTime Microcoagulation system (exclusively).

For the base-case analysis, relative effects were entered separately for the different types of event (any thromboembolic event, major bleed and minor bleed) by type of self-monitoring strategy (self-management and self-testing) (see Table 18). While not all effects were significant, the point estimates were applied in the model with appropriate distributions assigned to reflect the uncertainty surrounding them. These RRs, which represent pooled estimates obtained from trials with follow-up periods varying between 3 and 24 months, were assumed to apply directly to the 12-month risk of an event. Therefore, they were used to adjust the estimated annual baseline risk of events in the model, from which constant 3-month transition probabilities were derived, assuming constant proportional hazards over time. The RRs were applied only to people continuing on self-monitoring in the model.

Resource use estimation

Data on the resource use and costs associated with the alternative monitoring strategies were informed by published literature, existing guidance, expert opinion, manufacturers’ and suppliers’ prices, and other routine sources of unit cost data. 94,95 As noted above, certain costs were informed by expert opinion where suitable data from other sources were not available.

Costs of standard care

Resource use associated with standard monitoring was informed by a number of sources. The model provided by Roche used estimates of monitoring costs (under standard primary and secondary care) based on previous estimates calculated by the independent ERG for NICE technology appraisal TA249, Dabigatran etixilate for the prevention of stroke and systemic embolism in atrial fibrillation. 118 These estimates of monitoring costs in standard care, which were later applied in the NICE costing template for dabigatran,119 were derived by the ERG based on previous estimates used in the NICE costing report for clinical guideline CG36 on atrial fibrillation. 24 This report summarised the estimated annual resource use required for monitoring people in primary care, assuming 20 monitoring visits per year. These measures of resource use, per visit, are summarised in Table 19.

An alternative source of standard monitoring costs per visit was identified from the largest UK-based RCT of self-monitoring. 64 Jowett and colleagues carried out the economic analysis alongside the Self-Management of Anticoagulation, a Randomised Trial (SMART), where people in the control arm received a mix of standard primary and secondary care monitoring. 87 A unit cost per visit (accounting for staff time, equipment, consumables and overheads) was estimated for each care setting from a sample of NHS providers. The resultant cost estimates (per visit) for different types of standard care are presented in Table 20, inflated to 2011–12 prices.

Updated unit costs have been applied to provide a total cost per patient monitoring visit in 2011–12 GBP. When calculating the variable cost per patient associated with monitoring in a secondary care setting, the ERG in their report on dabigatran etexelate assumed that 33% of secondary care monitoring costs would be fixed and not influenced by changes in the number of people being monitored. This assumption was based on the observed proportional split between fixed and variable costs in the bottom-up calculation of the total cost of INR monitoring in primary care. 24 This same assumption was applied in our updated estimates.

When updating the unit costs for practice nurse time in primary care, we used an estimate per hour that incorporates allocated overhead costs (including management and administration) and use of practice space. Some of these allocated costs were not included in previous variable cost estimates for monitoring in primary care. It was considered appropriate to include them here to capture the opportunity cost associated with use of primary care facilities for INR monitoring. 120 However, as the allocated costs account for administration, additional administration time per patient visit was not costed separately as it was in previous estimates. 24,93,118

Given the slightly different approach to updating the unit costs for standard monitoring services, our cost estimates based on 20 monitoring visits (£235.20 and £306.94 for primary and secondary care monitoring, respectively) differ somewhat from those used in the NICE costing template for dabigatran (£220.90 and £303.43, respectively, for monitoring in primary and secondary care in 2009–10 prices) and also from those applied in the model provided by Roche (£231.33 and £317.90, respectively, for primary and secondary care monitoring in 2012–13 prices).

For primary care monitoring, these unit costs are somewhat higher than those presented in Table 19. However, the cost estimate for monitoring in a secondary care (hospital clinic) is substantially lower. Furthermore, while the proportional mix of standard care service use was not reported in the study by Jowett and colleagues,87 a total mean standard care monitoring cost of only £89.89 (£120.18 in 2011–12 prices) was reported at 12 months. The actual annual monitoring frequency observed in the control arm of the SMART trial was 37.9 days. 64

This suggests that an annual number of only ≈ 10 monitoring visits per year was required to achieve the level of control reported for the standard-care arm of this pragmatic UK-based RCT.

The assumption of 20 visits being the average number of monitoring visits required for people on long-term vitamin K antagonist therapy comes from the NICE costing report for the clinical guideline on the management of atrial fibrillation. 24,119 This was estimated based on the ratio of second to first attendances at anticoagulation clinics (≈ 19 from reported activity in the 2004–5 NHS reference costs) and a previous study by Jones and colleagues,121 which reported a median frequency of INR testing of 16 days for people receiving warfarin (equating to ≈ 22 tests per year). A repeat of the calculation based on reference costs activity data for 2011–12 yielded a ratio of only 9.5. However, this lower value may merely reflect a trend for more people to be followed up in primary care following initiation of therapy.

Given the uncertainty surrounding the average number of monitoring visits for people under standard primary and secondary care, the DAR specialist committee members were consulted on this parameter. Opinion on the frequency of monitoring suggested that 10–12 visits would be required on average in primary and secondary care, but that the number of visits would be highly variable across participants. It was also noted by one member that more monitoring visits may be required for people managed in secondary care, as it tends to be the people with poorer control who are managed in this setting. A further question was raised about the nursing time requirements for routine monitoring visits used in the previous cost estimates informing TA249 (15 minutes of band 5 nurse time per patient visit). One source suggested that 10 minutes would suffice for this.

Based on consideration of the all of the above evidence, it was assumed in the base-case analysis that, on average, 12 monitoring visits would be required per year for people under standard primary and secondary care monitoring. To retain consistency with previous analyses used to inform NICE guidance, we applied the unit costs per visit based on the figures in Table 19.

The impact of altering the number of standard care monitoring visits per year was also assessed through sensitivity analysis. We also conducted sensitivity analyses where the updated unit costs in Table 20 were applied to cost monitoring visits, and where we assumed only 10 minutes of nurse time per standard care monitoring visit.

Finally, given the reliance of some people on NHS transport for attending secondary care monitoring visits, a cost of transport was applied for a percentage of people modelled to receive this form of monitoring. The percentage of 8.55% was taken from a previous survey of patient pathways used to inform the manufacturer’s model for NICE TA25696 and the return transport cost was taken from the NHS reference costs (£30.96). 94

Costs of self-monitoring

An average testing frequency of 35 tests per year (every 10.42 days) was assumed for self-monitoring in the base-case analysis. This number was chosen to be consistent with the trials from which the relative effect estimates for self-monitoring were obtained. In a recent meta-analysis of patient-level data,108 11 of the self-monitoring trials included in our review reported the mean increase in the number of tests performed with self-monitoring versus control. There was an average of 24 additional tests by 12 months for people with atrial fibrillation and 22 additional tests for people with an AHV. The average of these two values was added to the estimated 12 tests per year for standard care, to give an estimate of 35 tests per year for self-monitoring. The impact of altering the difference in testing frequency between standard care and self-monitoring, through the 95% CIs reported by Heneghan and colleagues (13–30 per year), was assessed through sensitivity analysis. 108 Furthermore, we assessed scenarios where self-monitoring was not used to increase the frequency of monitoring as a means to improve INR control, but simply used to replace primary and secondary care testing. Under this scenario, we assumed no relative effects of self-monitoring on outcomes. The sections below provide further details on the cost of self-monitoring, with a summary of cost elements provided in Table 21.

Equipment

Self-monitoring device costs were obtained from the manufacturers. However, no up-to-date cost could be obtained for ProTime Microcoagulation System. The UK distributor of this device [International Technidyne Corporation (ITC), NJ, USA] was contacted for information, but stated that the device was not marketed for patient self-monitoring in the UK, and that the device was being superseded by the ProTime InRhythm™ System, ELITech (Berkhamsled, Herts, UK), which is being marketed in the UK for professional use only. For completeness, a self-monitoring strategy using the ProTime Microcoagulation System was included in the economic model, by applying a NHS list price from 2008. 122 Finally, a new promotional price (of £195) was provided for INRatio2. The impact of using this price was assessed in a sensitivity analysis.

Device costs were treated in the same way that capital investments are normally dealt with in economic evaluation. It was assumed that the NHS would pay for these and loan them out to patients. As such, they were annuitised over their expected use life to provide an equivalent annual/quarterly cost of use. While these devices have a potentially long life span based on the advice of manufacturers, their costs were annuitised over a 5-year period in the base-case analysis to account for the potential for loss and accidental damage.

There was also a degree of uncertainty about the suitability of the devices for reuse following discontinuation of self-monitoring by participants. In the base-case analysis, the same assumption that was used in a previous UK-based economic modelling study21 was applied, i.e. three-quarters of devices are reused by another patient in situations where a patient discontinues self-monitoring (see Training, for details on assumptions about discontinuation).

Consumables

The cost of test strips were provided by the manufacturers, and it was assumed in the base-case analysis that the annual cost of test strips would be equal to the number of tests performed annually multiplied by the cost per strip (i.e. that there would be no wastage). It was further assumed that two more test strips would be used annually to cross-check each device against a quality assured clinic-based machine. This was modelled to take place during biannual assessments for self-monitoring participants (see Biannual routine assessments).

NHS staff time

The staff time input required to oversee self-monitoring relied on expert opinion. People who are self-monitoring can require varying degrees of input from clinical staff to check readings and respond to queries. In the base case, it was assumed that all self-testing people would call in each and every test result on a dedicated telephone line, and that a nurse would later check and enter each patient’s result, and then telephone the patient back with instructions to either maintain or alter their warfarin dose. This was assumed to incur 5 minutes of band 5 nurse time per patient (based on the opinion of the specialist advisory committee), which was valued using nationally available unit costs. 95 It was assumed that self-managing people would not require any further support from nursing staff other than biannual routine assessments (see next section).

Biannual routine assessments

It was assumed that quality control of self-monitoring devices would take place at biannual clinic appointments, at the local anticoagulant clinic or practice from where self-monitoring was initiated. It was assumed that this would involve checking the patient’s instrument against an externally validated one, and that it would incur 15 minutes of direct face-to-face contact time with a practice nurse (£45 per hour) or hospital clinic nurse (£85 per hour). 95 In line with the base-case assumption that 34% of people are monitored in secondary care under standard practice, it was assumed that 34% of self-monitoring people would return to this setting for routine assessments, while the remainder would return to primary care clinics.

Training

Based on existing literature,123 as well as consultation with members of expert advisory committee, it was assumed that self-testing people would require 2 hours of one-to-one training, while those progressing to self-management would receive 4 hours of one-to-one training prior to initiation. These assumptions are consistent with those applied in the model that was provided by Roche (J Craig, York Health Economics Consortium, 2013, personal communication provided by Roche through NICE) and the literature on training requirements from RCTs of self-monitoring. Training time was costed using hourly unit costs for direct patient contact time (£45 per hour for practice nurse time and £85 per hour for hospital clinic nurse time).

The RCT literature64 and the expert advisory committee were also consulted with respect to training success rates and ongoing adherence to self-monitoring. In light of this, we incorporated a training failure rate of 15% – the mid-point between 5%, suggested by members of the expert advisory committee, and 24%, a pragmatic UK-trial-based estimate64 – and assumed that these people would incur the cost of training but return to standard care without incurring the cost of a monitoring device.

In addition to including a training failure rate in the model, it was considered unrealistic to assume that 100% of participants would continue to self-monitor after initiation. Therefore, we incorporated a discontinuation rate of 10% by 12 months in the model, based on consideration of the views of the expert advisory committee (≈ 5%) and a rate of 14% reported in the largest UK-based trial. 64 Beyond 12 months, it was assumed that self-monitoring people would continue to do so unless they experienced a fatal or disabling adverse event.

Warfarin costs

In line with previous evaluations, it was assumed that the quantity and cost of vitamin K antagonist drugs would not vary significantly between self-monitoring and standard monitoring. Therefore, these costs were excluded from the model.

Costs of adverse events

The cost of minor bleed was based on the NHS reference cost for VB07Z: Accident and emergency services, category 2 with category 2 treatment (weighted average). A major non-intracranial bleed was taken as the weighted average reference cost for the Healthcare Resource Group (HRG) codes related to non-elective admissions for gastro-intestinal bleeds (Table 22).

For the cost of a systemic embolism, a weighted average of the reference costs for non-elective admissions relating to the HRG for non-surgical peripheral vascular disease (QZ17A, QZ17B, QZ17C) was applied.

The initial cost of a minor stroke was taken as the weighted average of the 2011–12 non-elective reference costs for the HRG codes AA22A and AA22B (non-transient stroke or cerebrovascular accident, nervous system infections or encephalopathy, with and without complications and comorbidities). This equates to a cost of £3082.

For major stroke, the cost used in the rivaroxaban submission was also updated, whereby the initial treatment cost was taken as the weighted average of AA22A and AA22B (£3082), with the addition of 10.97 additional bed-days costed using the weighted average excess bed-day cost (£236.16 per day) for AA22A and AA22B. The excess bed-days were estimated by subtracting the length of stay accounted for in the reference costs for AA22A and AA22B – up to 24.43 days94 – from the average length of stay in hospital for people suffering a major stroke (34.4 days based on Saka and colleagues124). In addition, 14 days’ rehabilitation was added at a cost per day of £313.41 – based on the HRG VC04Z (rehabilitation for stroke) – to estimate the total cost of a major stroke to 3 months (£10,061). This estimate is lower than that used in the model for NICE TA256 (updated cost of £13,547), as excess bed-day costs were applied only to days above the costing trim-point for AA22A and AA22B, rather than days above the average length of stay for these codes. This is conservative in favour of standard care.

The costs associated with adverse events were adapted from those used in the model informing NICE TA256; rivaroxaban for the prevention of stroke and systemic embolism in people with atrial fibrillation. 96 These cost estimates were based largely on NHS reference costs, and were considered appropriate by the independent ERG in their critique of the manufacturer’s submission. 97 These costs were updated for the current analysis using the National Schedules of NHS Reference Cost, 2011–12,94 where possible, or were otherwise inflated from previously reported 2009–10 prices using the Hospital and Community Health Services (HCHS) pay and prices index. 95 These costs are presented in Table 22.

Further costs were applied on a quarterly basis in the years following ischaemic stroke. These costs were adapted from those applied in NICE clinical guideline CG92, which were initially based on costs reported by Wardlaw and colleagues125 of £11,292 per year for disabling stroke and £876 per year for non-disabling stroke (2001–2) prices. These costs were inflated to 2011–12 values using the HCHS pay and prices index. 95

For the acute treatment costs associated with an intracranial bleed, a weighted average of the non-elective reference costs for HRG AA23Z (haemorrhagic cerebrovascular disorders) was applied. In addition, the same rehabilitation costs as applied following major ischaemic stroke were applied following ICH, and the following quarterly health and social care costs were taken as the weighted average of those following minor (0.369) and major (0.631) ischaemic stroke. The cost of minor bleed was based on the NHS reference cost for VB07Z: accident and emergency services, category 2 with category 2 treatment (weighted average). A major non-intracranial bleed was taken as the weighted average reference cost for the HRG codes related to non-elective admissions for gastrointestinal bleeds (see Table 22).

Health measurement and valuation

Time spent in different states of the model was adjusted using utility weights reflecting the desirability of those states on a scale where 0 is equal to death and 1 is equal to full health. With the model structure similar to that of the model used to inform NICE TA256 (rivaroxaban for the prevention of stroke and systemic embolism in people with atrial fibrillation), a number of the utility values used in this previous model were applied (acute major and minor stroke, acute major haemorrhage and ICH). These values were considered appropriate by the independent ERG for NICE TA25697 and accepted by the appraisal committee. However, the utility values applied to the states ‘post minor’ and ‘post major stroke’ in TA256, were derived from a Norwegian study where values were elicited directly from participants and the general population. 127 Alternative values were identified for these states based on the EQ-5D responses of stroke people in the UK. Dorman and colleagues128 used the EQ-5D to measure the health status of 867 people enrolled in the International Stroke Trial. 129 The reported values of 0.31 for dependent health states and 0.71 for independent health states were considered more consistent with the NICE reference case than the directly elicited Norwegian values (0.482 and 0.719, respectively) used in TA256. Further, it was assumed that for people experiencing an ICH or a haemorrhagic stroke, the proportion of people returning to independent living would match that observed for ischaemic stroke, and that the same utilities for minor and major ischaemic stroke would apply to dependent and independent states following ICH. This approach was used as it was noted that the value used in the rivaroxaban submission92,96 was higher than the age-specific UK EQ-5D population norm for people ≥ 75 years of age. Finally, the baseline utility value for people with atrial fibrillation or mechanical heart valve who were stable was taken as the baseline EQ-5D value of patients enrolled in the SMART trial (0.738). 87

This value was applied to 65- to 70-year-old people. The difference between the UK EQ-5D population norm for 65- to 70-year-olds and the utility estimate from the SMART trial (0.042) was used to estimate age-specific baseline utilities in the model. The resultant utility values applied to events and health states are provided in Table 23.

Utilities associated with acute events were applied for the 3-month period following the event. For post-event states with associated ongoing morbidity, the appropriate health state utilities were applied for all subsequent cycles spent in these states. Half-cycle corrections were applied, by assuming that people experienced events on average at the mid-point of the cycle. Thus, a patient starting off in the well state and experiencing a major stroke in a given cycle of the model would accrue 6 weeks at the utility value for well and 6 weeks at the utility value for major stroke.

Time horizon, and discounting of costs and benefits

Both costs and benefits (QALYs) were discounted at 3.5% per annum, in line with the NICE reference case. 29 The model was initially analysed over a 10-year period, but the impact of adopting longer time horizons (including the patient’s lifetime) was explored in sensitivity analyses. It was anticipated that a 10-year time horizon would be sufficient to demonstrate the main health and cost impact of any identified differences in adverse event rates between the alternative monitoring strategies, while avoiding the uncertainty surrounding assumptions about event rates far into the future.

Analysis

The results of the model are presented in terms of a cost–utility analysis (i.e. costs for and number of QALYs generated by each monitoring strategy). Each strategy was compared incrementally with its next less costly, non-dominated comparator, to estimate its incremental cost per QALY gained. In addition, given the uncertainty surrounding the relative effectiveness of the alternative self-monitoring devices, self-monitoring using each device was also compared incrementally with the standard care monitoring strategy (mixed primary and secondary care monitoring).

Further analyses were undertaken to assess cost-effectiveness by age, indication for anticoagulation therapy (atrial fibrillation, AHV), the standard care comparator (primary care monitoring, secondary care monitoring), and the active intervention (self-monitoring, self-management). The impact of altering key parameter values and assumptions was also assessed through extensive sensitivity analysis.

Given the computational burden of running the model probabilistically for each scenario assessed, results of main analyses and sensitivity analyses are presented based on deterministic runs of the model using the point estimates for input parameters. To characterise the joint uncertainty surrounding point estimates of incremental costs and effects, probabilistic sensitivity analysis was also undertaken. 133 Each parameter was assigned an appropriate distribution as indicated in the preceding parameter tables. The model was then run iteratively 1000 times, with a value drawn randomly for each input parameter from its assigned distribution for each run. The estimated mean cost and effects for each strategy, based on these 1000 iterations, are presented for comparison with the deterministic results. The results of the probabilistic analysis are also presented in the form of incremental cost-effectiveness scatterplots and cost-effectiveness acceptability curves – for self-monitoring using each device compared with standard practice. As no direct evidence for the relative clinical effectiveness of the alternative monitoring devices could be identified, the strategies have not been compared simultaneously in the probabilistic analysis. Parameters excluded from the probabilistic analysis were self-monitoring training costs; in-hospital fatal stroke costs; post-stroke costs; the proportion of the cohort with atrial fibrillation; the proportion male; the proportional split between primary and secondary standard care monitoring; discontinuation rates; and unit costs of devices, consumables and staff time.

Base-case analysis

This section presents the results of the base-case analysis. The following assumptions were applied:

• 66.45% of standard care monitoring occurs in primary care with practice nurses. 96

• 60% of the cohort have atrial fibrillation, 40% have an AHV. 108

• Average age of the cohort is 65 years, and 55% are male. 108

• 50% of self-monitoring people self-test, 50% self-manage (assumption).

• The increase in the number of tests performed per year with self-monitoring is 23. 108

• Relative treatment effects are estimated and applied separately for self-testing and self-management (see Table 10).

• 15% of participants do not commence self-monitoring following training (see Training).

• 10% of participants discontinue self-monitoring within a year of commencing (see Training).

• Self-monitoring device costs are annuitised over 5 years (see Equipment).

• 75% of devices are reused by another patient when a patient discontinues self-monitoring (see Equipment).

Figure 17 indicates the modelled proportion of the cohort (under standard monitoring care) experiencing a stroke, thromboembolic event, major bleeding event, and death by time in years. Figure 18 presents the same outcomes under the self-monitoring strategy. Applying the base-case assumptions, the results indicate that over a 10-year period, the introduction of self-monitoring would reduce the proportion of people suffering a thromboembolic event by 2.5%, while slightly increasing the proportion suffering a major haemorrhagic event by 1.4% (Table 24).

FIGURE 17.

Modelled cumulative probability of a first thromboembolic and major haemorrhagic event, and death from all causes (standard-care cohort).

FIGURE 18.

Modelled cumulative probability of a first thromboembolic and major haemorrhagic event, and death from all causes (self-monitoring cohort).

While the predicted monitoring costs are higher with self-monitoring (see Table 24), the total health and social care costs are similar and in some cases lower, and the QALY gains are greater. Thus, under the base-case scenario, the self-monitoring strategies compare favourably with standard care, except for with ProTime, where the incremental cost per QALY gained is £47,640 (Table 25, Figure 19). Furthermore, due to the lower cost of the INRatio2 device and testing strips, coupled with the assumption of equivalent clinical effectiveness of the alternative self-monitoring devices, INRatio2 dominates CoaguChek XS. However, it should be noted that no direct evidence of clinical effectiveness was identified exclusively for INRatio2 from the systematic review.

FIGURE 19.

Cost-effectiveness frontier (base case).

Incremental analysis of alternative scenarios

Table 26 shows the results of further scenario analyses. For exclusive self-testing and self-management versus mixed primary/secondary care standard monitoring, and for mixed self-monitoring versus exclusive primary and secondary care clinic testing. Exclusive self-management with INRatio2 and CoaguChek XS was cost saving under the base-case assumptions, whereas self-testing was not cost-effective. The results also showed the mixed self-monitoring strategy (50% self-testing, 50% self-management) to be cost saving with CoaguChek XS and INRatio2 in comparison with exclusive secondary care testing. When applying the pooled RR for adverse events (derived from all self-monitoring studies) to both self-testing and self-managing participants, the cost savings and QALY gains associated with self-monitoring increased (see Table 26, scenario 5). This is because under this scenario self-testing becomes independently more effective. The same pattern of results was identified when self-monitoring was compared with exclusive secondary care anticoagulation clinic testing (see Table 26, scenario 6) using the point estimates of RRs derived only from trials making this comparison (see Figures 6 and 14). Finally, scenario 7 (see Table 26) shows the results when restricting the comparison to CoaguChek XS versus standard monitoring, using the pooled point estimates of RR derived only from trials of CoaguChek versus standard practice.

Table 27 presents the results of alternative non-base-case scenarios, assessing the impact of using self-monitoring not to increase the number of tests performed annually, but to replace standard monitoring tests (average 12 per year). For these analyses it was assumed that no difference in clinical effectiveness exists between self-management, self-testing and standard care. Under most of these scenarios, standard monitoring was found to be less costly than self-monitoring. However, self-testing and self-management with INRatio2 and CoaguChek XS remained cost saving in comparison with exclusive secondary care anticoagulation clinic monitoring.

Differential results for subgroups

Table 28 presents the results for self-monitoring versus standard care by indication (atrial fibrillation and AHVs) and cohort age. Compared with standard monitoring, self-monitoring in a 65-year-old cohort with atrial fibrillation was estimated to cost £2574 and £4160 per QALY gained with INRatio2 and CoaguChek XS, respectively. Self-monitoring with ProTime was estimated to cost £58,584 per QALY gained. For a 65-year-old AHV cohort, self-monitoring with INRatio2 and CoaguChek XS was found to be more effective and less costly (dominant) than standard monitoring.

A further analysis was carried out for the atrial fibrillation cohort using the baseline risks observed for participants with better INR control in standard care, assuming a constant RR reduction for thromboembolic events associated with self-monitoring. As the INR TTR increased in the control group, and the baseline risk of thromboembolic events consequently dropped, the cost-effectiveness of self-monitoring also decreased. However, the ICERs for CoaguChek XS and INRatio2 rose above £20,000 per QALY only when the baseline TTR was set at > 72.6%.

While cost-effectiveness was found to decrease slightly in a younger mixed cohort (due to the lower baseline risk of thromboembolic events), the ICERs for CoaguChek XS and INRatio2 remained below £20,000 per QALY gained. Self-monitoring was found to be most cost-effective in a 75-year-old cohort.

Further analysis of uncertainty (sensitivity analysis)

Deterministic sensitivity analysis was undertaken to test the robustness of the model, based findings to various parameter and structural assumptions (Table 29). The findings were found to be most sensitive to the baseline risk of thromboembolic events and the effectiveness of self-monitoring for preventing these events (see Table 29, scenarios 14–16). Applying a baseline risk of 1.15% coupled with the upper 95% confidence limit of the RR estimate for self-management (0.69), the ICERs for the mixed self-monitoring strategies rose above £30,000 per QALY gained (see Table 29, scenario 17). The same was found when the lower baseline risk (1.15%) was coupled with the upper confidence limit for the RR (for thromboembolic events) associated with self-monitoring as a whole (0.84 applied for self-testing and self-management). One hundred per cent self-management remained cost saving under the former combined scenario but not the latter.

The cost-effectiveness of self-monitoring improved further when the modelled time horizon was extended to 20 and 30 years, with both CoaguChek XS and INRatio2 dominating standard primary/secondary care-based monitoring. The incremental cost per QALY gained for self-monitoring with CoaguChek XS and INRatio2 also remained below £20,000 when higher training failure and discontinuation rates were applied, and when higher self-monitoring testing frequencies were applied (with no change in effects). The cost-effectiveness findings were also robust to the number of tests performed annually in standard primary/secondary care-based clinic monitoring.

A final sensitivity analysis was conducted to approximate the cost-effectiveness of self-monitoring for a cohort of children with an AHV on long-term vitamin K antagonist therapy. For this analysis, the cohort age was set to 10 years, the baseline risk of thromboembolic events was reduced to 1.4%, and the annual risk of all-cause mortality following a stroke was set at 14.5. 134 Under this scenario, the ICERs for self-monitoring with CoaguChek XS and INRatio2 remained favourable. However, it should be noted that no good data were identified to appropriately adjust the risk of death from all causes in children with an AHV, and therefore the standardised mortality ratio estimated for an 18- to 55-year-old cohort of AHV participants was applied.

Probabilistic sensitivity analysis of the base case

Table 30 presents the mean costs and effects, and mean incremental cost-effectiveness results, for the four strategies based on 1000 probabilistic simulations. Compared with the deterministic analysis presented in Table 25, the results are very similar.

Figure 20 shows the scatterplot of the estimated mean incremental costs and effects of self-monitoring with CoaguChek XS compared with standard monitoring, derived from 1000 probabilistic iterations of the model. Approximately 50% of the points lie below zero on the cost axis and above zero on the effect axis, indicating a 50% chance of the self-monitoring strategy (50% self-testing, 50% self-managing) dominating standard care monitoring. The acceptability curve (Figure 21) indicates an 80% chance of self-monitoring with CoaguChek XS being cost-effective compared with standard monitoring at a willingness-to-pay threshold of £20,000 per QALY gained.

FIGURE 20.

Incremental cost-effectiveness scatterplot: self-monitoring with CoaguChek XS vs. standard monitoring.

FIGURE 21.

Cost-effectiveness acceptability curves: self-monitoring with CoaguChek XS vs. standard care. WTP, wllingness to pay.

Figures 22 and 23 show the corresponding incremental cost and effect scatterplot, and acceptability curve for self-monitoring with INRatio2 versus standard care. This analysis assumes equivalent effects for INRatio2 compared with CoaguChek XS. Self-monitoring with INRatio2 was estimated to have an 81% chance of being cost-effective at a threshold of £20,000 per QALY gained under these assumptions. However, it should be noted that no direct RCT evidence was identified for the effect of INRatio2 on long-term adverse outcomes, with the majority of RCT evidence relating to versions of CoaguChek.

FIGURE 22.

Incremental cost-effectiveness scatterplot: self-monitoring with INRatio2 vs. standard monitoring.

FIGURE 23.

Cost-effectiveness acceptability curves: self-monitoring with INRatio2 vs. standard care.

Figures 24 and 25 summarise the results of the probabilistic analysis for self-monitoring with ProTime versus standard monitoring. Owing to the higher cost of the device, this strategy was found to have a lower chance of being cost-effective in than standard practice.

FIGURE 24.

Incremental cost-effectiveness scatterplot: self-monitoring with ProTime vs. standard monitoring.

FIGURE 25.

Cost-effectiveness acceptability curves: self-monitoring with ProTime vs. standard care.

Finally, Figures 26 and 27 summarise the uncertainty surrounding the cost-effectiveness of self-monitoring with CoaguChek XS versus secondary care anticoagulation clinic testing (applying RR distributions based on the pooled estimates from trials making this comparison) and mixed (primary/secondary care) standard monitoring (using RRs derived from trials using only CoaguChek).

FIGURE 26.

Cost-effectiveness acceptability curves: self-monitoring with CoaguChek XS vs. standard monitoring (based on pooled RR estimates from CoaguChek studies only).

FIGURE 27.

Cost-effectiveness acceptability curves: self-monitoring with CoaguChek XS vs. secondary care anticoagulation clinic monitoring (applying RRs for self-monitoring vs. specialised anticoagulation clinic testing).

Clinical effectiveness

This assessment is based on 26 RCTs evaluating the use of point-of-care devices for the self-monitoring (self-testing and self-management) of people receiving AOT. The results of this assessment indicate that:

• Self-monitoring (self-testing or self-management) of anticoagulation therapy leads to significantly fewer thromboembolic events than standard primary care or anticoagulation control in specialised clinics (RR 0.58, 95% CI 0.40 to 0.84; p = 0.004).

• There is no evidence of a difference in bleeding events (RR 0.95, 95% CI 0.74 to 1.21; p = 0.66).

• Self-monitoring almost halved the risk of thromboembolic events in people with AHVs.

• A statistically significantly greater reduction in thromboembolic events was observed among self-managed people than among those in self-testing.

• Among people who self-monitored their therapy, there was a trend towards fewer thromboembolic events when compared with those who were managed by their GPs or physicians than those managed in specialised anticoagulation clinic. The subgroup analysis was not, however, statistically significant.

• Self-monitoring significantly reduced the risk of mortality among people with AHVs but not among those with mixed clinical indication. There was lower all-cause mortality through self-management but not through self-testing. In particular, significantly fewer deaths were observed among people who self-managed their AOT than those who received primary standard care (control care by a GP or a physician).

• Compared with standard care, self-monitoring (self-testing and self-management) did not demonstrate a significant reduction in the number of major and minor bleeding events.

• In the majority of included trials (23 out of 26), the INR TTR was higher in self-monitoring people than in people receiving standard anticoagulation control, and in five of these trials there was a statistically significant difference between intervention groups.

• The overall percentage of participants who completed self-monitoring was fairly high (at least 80%), and in the few trials that collected participant views, participants expressed high satisfaction and willingness to continue with the intervention at home.

• Six of the trials were conducted in the UK and there was no evidence that the UK trial populations were importantly different from the rest of the included studies.

• The majority of the trials (22 out of 26) investigated the use of the CoaguChek system, the results are, therefore, more robust for CoaguChek than for ProTime and INRatio.

• Four of the 22 trials investigating the CoaguChek system used the CoaguChek XS system. There was insufficient evidence to determine whether or not the CoaguChek XS outcomes differed from those for previous versions of CoaguChek systems.

• A brief overview of diagnostic performance of the various CoaguChek systems demonstrated that across several studies INR results were more accurate in adults and children when comparing CoaguChek XS with other CoaguChek models. We are of the opinion that this provides evidence that the clinical outcomes can be compared across different versions of the CoaguChek system.

Cost-effectiveness

The base-case model assessed the impact on costs and outcomes of using self-monitoring to increase the number of INR tests performed annually (by 23), so as to improve INR control and prevent adverse outcomes. The primary findings are detailed below.

• While self-monitoring (50% self-testing, 50% self-management) is likely to increase the INR monitoring cost compared with mixed primary/secondary care standard monitoring, it is likely to be cost-effective as a result of its impact on the incidence of thromboembolic events. This finding assumes that the pooled relative effects of self-testing and self-management, obtained from the meta-analysis of all RCTs, are applicable to the UK setting.

• Underlying this general observation is the finding that the pooled effect estimate for self-testing on thromboembolic events is small and non-significant (RR 0.99), while the effect estimate for self-management is large (RR 0.51) and significant. Thus, within the base-case model, self-management alone is highly cost-effective (or dominant), while self-testing is not cost-effective.

• In an alternative specification, the overall pooled effect estimates obtained from all self-testing and self-management trials were applied to both the self-testing and self-management strategies in the model. Under this scenario, both self-testing and self-management, with CoaguChek XS or INRatio2, were found to be dominant or highly cost-effective compared with standard monitoring.

• Two key parameters underpinning the above findings are the baseline risk of thromboembolic events, and the relative effect of self-monitoring on these events. The model findings were robust to individual changes in these parameters through feasible ranges. However, when the lower baseline risk of thromboembolic events was combined with the upper confidence limit for the RR for associated self-management (RR 0.69), the ICERs for self-monitoring as a whole rose above £30,000 per QALY. The same was found when the lower baseline risk of thromboembolic events was coupled with the upper confidence limit of the pooled RR for self-monitoring as whole (RR 0.89). It should be noted, however, that self-management on its own remained cost saving under the former combined scenario.

• Further uncertainty relates to the applicability of the pooled effect estimates to the UK setting. The few identified UK-based trials of self-monitoring versus standard practice did not demonstrate significant effects on thromboembolic or bleeding events. Applying these effect estimates, self-monitoring would not be cost-effective at the self-monitoring testing frequency observed in RCTs.

• Alternative scenarios assessed the potential for self-monitoring to be cost-effective if used to replace clinic-based testing without increasing the frequency of testing. Under these scenarios, it was assumed that there would be no effect on the number of thromboembolic or bleeding events and a cost-minimisation approach was adopted. This showed that when holding all other base-case parameters constant, self-monitoring (50% self-testing, 50% self-managing) was more costly than standard primary care monitoring, but less costly than standard secondary care monitoring. These findings were, however, sensitive to the unit costs applied to standard care monitoring visits. Applying the alternative standard monitoring unit costs estimated by Jowett and colleagues,87 the opposite was observed, with self-monitoring dominating secondary care monitoring but being dominated by primary care monitoring.

Clinical effectiveness

Although our assessment has been conducted according to current standards and recommendations, and is the most up-to-date review undertaken, we need to acknowledge some potential limitations and uncertainties. The areas of uncertainty were:

• The included trials varied considerably in terms of clinical indications for anticoagulation therapy, type of control care, reporting structure for the time and/or values in therapeutic range, type and structure of the pre-intervention training and education programme, length of follow-up and methodological study quality. While the meta-analysis results demonstrated low statistical heterogeneity (which makes it statistically reasonable to combine the studies), there remains uncertainty around the fact that clinical heterogeneity could have over- or underestimated the effects.

• Quantifying the impact of the potential risk of bias in the estimates was not possible. Only four trials55,61–63 were judged at low risk of bias. In some trials, outcomes were not assessed blinded, allocation of participants to intervention groups was not concealed, statistical analyses were not conducted according to an intention-to-treat principle, or many methodological details were lacking.

• All included trials enrolled highly selected samples of people requiring anticoagulation therapy, and so it was uncertain whether or not there was strong external validity (i.e. applicability of the study results to the entire population of eligible participants). To be enrolled in the trials, participants needed to demonstrate adequate cognitive and physical abilities, as well as dexterity and confidence in using the point-of-care device. In some of the included trials,61,64,67,68 a considerable proportion of eligible participants (up to 50%) ultimately were not considered suitable for inclusion.

• The frequency of testing was not consistently reported in the included studies. This hampered the possibility to conduct further analyses with regard to different monitoring strategies. The frequency of INR testing in the trials was generally weekly for self-monitoring participants and monthly in standard care. It was unclear what the optimal frequency might be, especially at long-term follow up where there was little evidence.

• There remains some uncertainty on the applicability of the pooled results to the UK population. In our view, the greatest uncertainty relates to the applicability of the standard-care comparators in the trials and not to the participants in the trial.

• The majority of the trials included participants with mixed clinical indications for anticoagulation therapy, which made it challenging to extrapolate the results to specific clinical populations. In particular, only limited data were available for people with atrial fibrillation and, consequently, no reliable conclusions could be drawn in relation to this patient population.

• The majority of trials investigated the use of the CoaguChek system (22 out of 26) for the self-monitoring of anticoagulation therapy and it proved unfeasible to conduct reliable comparisons according to the type of point-of-care device. While the CoaguChek device has the most robust evidence, ProTime and, particularly, INRatio do not. Given the broadly similar performance of all the devices compared with the gold-standard laboratory test, we are of the opinion that it is not unreasonable to consider pooled estimates of effect across all studies and devices. However, this is an assumption that currently has no direct comparative evidence available and so a degree of caution is necessary.

• The subgroup analysis according to the type of anticoagulation therapy management (self-management vs. self-testing) was limited due to the results being dominated by the largest trial published so far, THINRS,71 which enrolled 2922 people and assessed patient self-testing using the ProTime device versus routine clinical care. The trial results showed similar rates of main clinical outcomes between intervention groups with the exception of a small but significant improvement in the percentage of time in target range for self-testing people. It is worth pointing out that this trial had a highly specialised routine care and the longest follow-up period (mean 3 years). It is probable that the quality of the standard care in this trial exceeds current routine care for anticoagulation monitoring and the lack of significant differences between self-testing and routine monitoring could be explained by the rigorous criteria used to ensure high-standard care.

Cost-effectiveness

The model developed for this assessment has built upon previous models developed to assess the cost-effectiveness of INR self-monitoring strategies and new pharmaceuticals compared with warfarin therapy under standard INR monitoring arrangements. Where possible, the input parameter values used have previously been reviewed for NICE submissions by independent ERGs and accepted by appraisal committees. A further strength of the model comes through the dichotomisation of indication for warfarin use (atrial fibrillation/AHVs), mode of standard-care monitoring (primary/secondary care) and mode of self-monitoring (self-testing/self-management). This allowed assessment of cost-effectiveness by subgroups based on these indicators. Nevertheless, the main uncertainties are given below:

• A weakness of the modelling relates to the uncertainty surrounding the pooled clinical effectiveness estimates (for self-testing and self-management) and, in particular, their applicability to the NHS setting.

• A further weakness relates to the structural assumptions required to estimate cost-effectiveness in younger cohorts, i.e. those below the average age of cohorts used to inform the baseline risks of events and standardised mortality ratios associated with the clinical indications and adverse events. To reflect the importance of age as a risk factor for thromboembolic events, RRs by 5-year age bands were taken from a previous atrial fibrillation model92,96,97 and applied. Given a lack of similar evidence relating to people with a mechanical heart valve, the same RRs were also applied to this subgroup in the model. While this is not ideal, the model results were found to be robust to a range of alternative baseline risks when applied in isolation.

• Owing to data limitations, very young cohorts were not formally included in subgroup analyses for the economic modelling. A sensitivity analysis was conducted to approximate the results for a cohort of children, but the estimates of baseline risk and self-monitoring effects were not well informed.

• It should finally be noted that the perspective of the cost-effectiveness analysis was that of the NHS and personal social services. Therefore, our modelling does not capture any wider benefits or cost savings to patients and their families, such as a reduction in time spent travelling to and waiting in clinics.

EMBASE

Searched: 1980 to week 22 2013.

Ovid MEDLINE(R)

Searched: 1946 to week 5 May 2013.

Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations

Searched: 5 June 2013.

OVID Multifile searched

Searched: 5 June 2013.

URL: https://shibboleth.ovid.com/

Science Citation Index

Searched: 1970 to 5 June 2013.

BIOSIS

Searched: 1956 to 5 June 2013.

Conference Proceedings Citation Index – Science

Searched: 2012 to 5 June 2013.

ISI Web of Knowledge

Searched: 5 June 2013.

URL: http://wok.mimas.ac.uk/

The Cochrane Library, Issue 4, 2013 (Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Review of Effects, HTA database)

Searched: May 2013.

URL: www3.interscience.wiley.com/

Search strategy

1. MeSH DESCRIPTOR 4-Hydroxycoumarins EXPLODE ALL TREES

2. (warfarin) OR (vitamin k antagonist*)

3. MeSH DESCRIPTOR anticoagulants EXPLODE ALL TREES WITH QUALIFIER AD

4. #1 OR #2 OR #3

5. MeSH DESCRIPTOR self administration

6. MeSH DESCRIPTOR self care

7. MeSH DESCRIPTOR Point-of-Care Systems

8. (poc) OR (self NEAR3 (monitor* or manag* or measur*)) OR (patient* NEAR3 (monitor* or manag* or measur*))

9. #5 OR #6 OR #7 OR #8

10. #4 AND #9

Additional conference proceedings

ASH 2012, 54th ASH Annual Meeting and Exposition, Atlanta, GA, 8–11 December 2012.

EHA 2012, 17th Congress, Amsterdam, 14–17 June 2012.

ISTH 2011, XXIII Congress of the International Society on Thrombosis and Haemostasis 57th Annual SSC Meeting, ICC Kyoto, Kyoto, Japan, 23–28 July 2011.

Proceedings of the 12th National Conference on Anticoagulant Therapy, Phoenix, Arizona, 9–11 May 2013.

Clinical Trials

Searched: June 2013.

URL: http://clinicaltrials.gov/ct/gui/c/r

Search strategy

CoaguChek OR INRatio OR ProTime OR ((“point-of-care” or self) AND anticoagulant OR warfarin))

Searched: International Clinical Trials Registry Platform (ICTRP)

Date of search: June 2013.

World Health Organization

Searched: 5 June 2013.

URL: www.who.int/ictrp/en/

EMBASE

Searched: 1980 to week 23 2013.

Ovid MEDLINE(R) 

Searched: 1946 to week 5 May 2013.

Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations 

Searched: 7 June 2013.

OVID Multifile

Searched: 5 June 2013.

URL: https://shibboleth.ovid.com/

EMBASE

Searched: 1980 to week 22 2013.

Ovid MEDLINE(R)

Searched: 1946 to week 5 May 2013.

Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations

Searched: 5 June 2013.

OVID Multifile

URL: https://shibboleth.ovid.com/

Health Management Information Consortium

Searched: 1979 to March 2013.