Evaluation of triage methods used to select patients with suspected pandemic influenza for hospital admission: cohort study

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Chapter 1 Introduction

Influenza pandemics have occurred at least three times in the last century. Their severity ranges from similar to seasonal influenza to a major international threat to health, with millions becoming ill and a proportion dying. A pandemic thus has the potential to place a huge strain upon health services, particularly the emergency care services, which may be exacerbated by staff sickness absence due to influenza.

The timing, course and severity of a pandemic are difficult to predict, but estimates of the number of cases and the burden upon health services are necessary to assist planning. The UK influenza pandemic contingency plan published in 2007 predicted around 750,000 excess emergency department attendances and 82,500 excess hospitalisations during a pandemic. 1 Under these circumstances it would be impractical for all patients fully to be assessed by a senior clinician. We therefore need methods of triage and resource allocation that are fair, robust and reproducible. 2

The term triage is often used to describe a brief initial assessment in the emergency department to determine patient order of priority in the queue to be seen. However, it can be used more broadly to include the full process of emergency assessment, including investigations such as blood tests and radiography, and can be applied to decision-making regarding whether the patient should be admitted to hospital and whether he/she should be referred for high-dependency or intensive care.

Emergency department triage methods need to accurately predict the individual patient’s risk of death or severe illness. The predicted risk can then guide decision-making. Patients with a low risk may be discharged home, those with a high risk admitted to hospital, and those with a very high risk referred for high-dependency or intensive care. The level of risk used to trigger these decisions need not necessarily be fixed or determined in advance. Indeed, decision-making thresholds could change during the course of a pandemic as the balance between resource availability and demand changes. Triage methods that use a risk prediction score to determine the need for hospital care may therefore be more useful than a triage rule that classifies patients into admission and discharge categories.

In April 2009, a new strain of the A/H1N1 influenza virus (known as swine flu) was detected in Mexico and started to spread globally. In June, the World Health Organization declared the outbreak to be a pandemic. The virus spread to the UK, leading to a first wave of cases in July 2009 and a second wave in October and November 2009. The initial waves of the pandemic provided an opportunity to undertake research that could then guide patient management in subsequent waves or future pandemics.

Prior to the emergence of the 2009 H1N1 pandemic, Health Protection Agency (HPA) guidance, supported by the British Thoracic Society and British Infection Society, recommended the use of the CURB-65 pneumonia score3 in adults, shown in Appendix 1. This score uses five variables (confusion, urea level, respiratory rate, blood pressure and age) to generate a score between zero and five. Department of Health guidelines on surge capacity in a pandemic also considered use of a physiological–social score [Pandemic Modified Early Warning Score (PMEWS)]4 for adults, shown in Appendix 2. This score uses physiological variables, age, social factors, chronic disease and performance status to generate a score between zero and 20. National guidance produced in response to the emergence of H1N1 influenza included a new swine flu hospital pathway for emergency department management with seven criteria. This was based upon a Community Assessment Tool (CAT) consisting of seven criteria, any one of which predicts increased risk and the need for hospital assessment5 in adults and children. This is shown in Appendices 3 (adults) and 4 (children).

Existing literature shows CURB-65 to perform reasonably well as a mortality predictor in an emergency department population with community-acquired pneumonia {AUROC [area under the receiver–operator characteristic curve (C-statistic): a measure of the discriminant value of a risk prediction score] 0.76},6 but less well in predicting the need for high-level care (AUROC 0.697 and 0.648). The physiological–social score considered by the Department of Health (PMEWS) is not a particularly good predictor of death in community-acquired pneumonia (used as a proxy for pandemic influenza), with an AUROC score of 0.66, but performed much better when predicting a requirement for higher-level care (AUROC 0.83)8 and has shown promise when used in the prehospital setting to determine need for emergency department attendance (AUROC 0.719 and 0.810). The national guidelines produced for the H1N1 pandemic appear to have been developed by expert consensus without validation in the appropriate patient populations.

To our knowledge there have been no studies evaluating any of these triage methods in patients with suspected pandemic influenza, and no studies to develop a risk-prediction score in the emergency department population with suspected pandemic influenza. We therefore aimed to use the initial waves of the H1N1 pandemic to evaluate existing emergency department triage methods for predicting severe illness or death in patients presenting with suspected pandemic influenza, and determine whether an improved triage method could be developed.

Our specific objectives were to determine:

1. the discriminant value of the CURB-65 score, PMEWS and the swine flu hospital pathway for predicting severe illness or death in adults presenting with suspected pandemic influenza, and of the swine flu hospital pathway for predicting severe illness or death in children

2. the independent predictive value of presenting clinical characteristics and routine tests for severe illness or death in patients presenting with suspected pandemic influenza

3. whether the discriminant value of emergency department triage can be improved by developing two new triage methods based upon (1) presenting clinical characteristics alone and (2) presenting clinical characteristics, electrocardiogram (ECG), chest radiograph and routine blood test results.

The first new triage method would use only variables available at initial patient assessment, i.e. history and examination, including simple technologies such as automated blood pressure measurement and pulse oximetry. This triage method could be used to assess patients for the need for hospital investigation and identify patients that could be discharged without further assessment.

The second new triage method would be based upon all available emergency department data, including routine blood tests, ECG and chest radiograph findings. This triage method could be used for two potential purposes: (1) identification of patients with a low risk of adverse outcome, who can be discharged home after emergency department assessment, and (2) identification of high-risk patients who are likely to need high-dependency or intensive care.

Chapter 2 Methods

We undertook a prospective cohort study of patients presenting to the emergency department of the participating hospitals with suspected pandemic influenza during the second wave of the 2009 H1N1 pandemic. Patients were eligible for inclusion if they met the clinical diagnostic criteria of (1) fever (pyrexia ≥ 38°C) or a history of fever and (2) influenza-like illness (two or more of cough, sore throat, rhinorrhoea, limb or joint pain, headache, vomiting or diarrhoea) or severe and/or life-threatening illness suggestive of an infectious process.

Emergency department staff identified eligible patients and then completed a standardised assessment form that doubled as a clinical notes and study data collection form [referred to hereinafter as the clinical assessment form (CAF) and prepared in adult and paediatric variants – see Appendix 5]. It included the elements of the CURB-65 score, PMEWS, the swine flu hospital pathway and any other measures that could be routinely recorded in the emergency department (comorbidities, physiological observations, routine blood tests, ECG and chest radiograph). Details of any prepresentation antiviral medication, antibiotics and immunisation status were also recorded. The study did not involve any change to patient management, so patients were treated and then discharged home or admitted to hospital according to normal emergency department practices.

Patients were informed of the study by means of posters displayed in the emergency department, and leaflets distributed from reception and the pandemic influenza assessment area. They were informed that they could withdraw their data from the study but were not asked to consent to participate in the study. We did not seek patient consent to participate on the basis that the study was limited to collection of routinely available data and any delays in patient assessment could have risked compromising patient care.

Research staff followed patients up until 30 days after attendance by hospital record review and, if appropriate, general practitioner contact to identify patient outcomes. Data were abstracted from the CAF and hospital notes by researchers working with an honorary contract from the hospital Trust or researcher passport recognised by the Trust. The researcher kept a record of any patients who withdrew from the project. He/she entered anonymised data on to a secure online database provided by the Clinical Trials Unit at the University of Sheffield, Sheffield, UK. Other members of the research team had access only to anonymised data on the secure database.

The CAF constituted the clinical notes and was kept in each hospital according to normal practice. A copy of the CAF was retained by the researcher in a secure location in each hospital, to be destroyed 6 months after the end of the project. The Clinical Trials Unit will maintain an anonymised database until 10 years after the end of the project.

Chapter 3 Ethical and governance arrangements

The North West Research Ethics Committee (REC) and the National Information Governance Board (NIGB) reviewed and approved the study protocol. The University of Sheffield was the study sponsor. The Project Management Group (PMG), consisting of the coapplicants and the appointed research staff, managed the study. A steering group was appointed, consisting of the chief investigator, project manager, an independent clinician (Chairperson), statistician and layperson to provide independent oversight.

Chapter 4 Results

As insufficient cases presented to the participating hospitals to complete our initial analysis plan, we have restricted our analysis to the ability of the various existing triage tools to predict hospital admission and poor outcome.

Cases were identified and data collected at the Northern General Hospital between 29 September 2009 and 10 January 2010, Sheffield Children’s Hospital between 10 October 2009 and 31 December 2009 and South Manchester between 24 September 2009 and 7 February 2010. We identified and collected data from a total of 492 cases, 11 of whom asked for their data to be withdrawn, leaving 481 for analysis. There were 77 cases at the Northern General Hospital, 226 at the Sheffield Children’s Hospital and 178 at South Manchester. Ages ranged from infant to 96 years. Most of the cases were children, with 347 out of 481 (72%) aged 16 years or less. The modal age group was 1–2 years, accounting for 69 out of 481 (14%). There were 237 females (49%) and 244 males (51%). Most patients self-referred (399/481, 83%), while only 41 (8%) were referred via their GP and 15 (3%) were referred via NHS Direct.

Symptom duration was recorded for 379 patients. Mean duration was 3.1 days, median was 2 days and most patients (213 out of 379, 56%) had 1–2 days of symptoms. Prior to their index hospital attendance, 30 (6%) had attended hospital with the same complaint, eight patients (2%) had received vaccination against H1N1, 39 (8%) had been given oseltamivir, and 46 (10%) had been given antibiotics, although not always specifically for their presenting complaint.

Social isolation (defined as living alone or having no fixed abode) was reported by 12 (2%). Table 1 shows the proportion reporting different levels of performance status. This was not recorded for one-third of patients but most cases that did report it had unrestricted normal activity. Table 2 shows the proportion reporting chronic disease or medication use. The only chronic problem recorded with any frequency was asthma, in 13% of cases.

Influenza was thought by the physician to be the most likely diagnosis in 214 out of 368 cases (58%). The most common alternatives were upper respiratory tract infection (79 cases) and tonsillitis (23 cases).

Presenting physiological features were not recorded in all cases. Temperature (n = 425) ranged from 35.0°C to 40.7°C [mean 37.8, standard deviation (SD) 1.1] and peripheral oxygen saturation (n = 369) ranged from 79% to 100% (mean 97%, SD 6%). Some 19 out of 369 (5%) cases had peripheral oxygen saturation below 94%. Results for pulse rate, respiratory rate and blood pressure (Table 3) are categorised by age group to allow for age-related variation in normal values for these parameters. Tachycardia and tachypnoea were relatively common, whereas blood pressure was generally normal.

Variables that were relevant only to younger children were present as follows: 6 out of 207 (3%) had been managed on a special care baby unit, 8 out of 234 (3%) had not had their routine vaccinations, 51 out of 227 (22%) were not taking feeds and 47 out of 205 (23%) of clinicians reported parental anxiety as being a concern.

Blood tests were only ordered for 55 out of 481 cases (11%). The results are summarised in Table 4. Chest radiographs were ordered and were abnormal in 12 cases, normal in 19, not done in 284 and not recorded in 166. An ECG was ordered and abnormal in 10 cases, normal in 24, not done in 67 and not recorded in 380.

The clinical plan included oseltamivir for 58 cases and antibiotics for 56 (22 amoxicillin, nine augmentin, one cefotaxime, two ceftriaxone, three clarithromycin, one gentamycin and 18 penicillin). The attendance resulted in admission for 83 out of 481 cases (17%): 12 aged 0–1 years, 14 aged 2–5 years, 13 aged 6–16 years and 44 adults.

Tables 5 and 6 show the CURB-65 scores and PMEWS scores (adults only). The recommended threshold for admission4 for CURB-65 is a score of two or more. Table 5 suggests that 9 out of 134 (7%) of patients should have been admitted. Applying a similar threshold for PMEWS would have resulted in 81 out of 134 (60%) being admitted.

Patients with a higher CURB-65 score were more likely to be admitted (p = 0.001, chi-squared test for trend): 25 out of 101 (25%) with a score of zero, 11 out of 24 (46%) with a score of one, 7 out of 8 (88%) with a score of two and the patient with a score of three were admitted. Admitted patients had a higher mean PMEWS scores (4.6 vs 2.0, p < 0.001, t-test). The C-statistics for CURB-65, PMEWS and the swine flu hospital pathway in adults in terms of discriminating between those admitted and discharged were 0.65 (95% CI 0.54 to 0.76), 0.76 (95% CI 0.66 to 0.86) and 0.62 (95% CI 0.51 to 0.72), respectively.

Tables 7 and 8 show the results for adults and children (aged 16 or less), respectively, on the swine flu hospital pathway, along with the number and proportion with each criterion admitted. Among the adults, 16 out of 28 (57%) with a positive criterion were admitted, compared with 28 out of 106 (26%) with no positive criteria. Among the children, 14 out of 39 (36%) with a positive criterion were admitted, compared with 25 out of 308 (8%) with no positive criteria.

Only 5 out of 481 (1%) patients had a poor outcome according to our definition. Their details are as follows:

1. Female, aged 60, no chronic illnesses, presented with respiratory rate 30, heart rate 90, temperature 38.0, blood pressure 160/62, peripheral oxygen saturation 90%, Glasgow Coma Score (GCS) 15, haemoglobin 13.4, platelets 198.0, white cell count 12.7, sodium 119.0, potassium 4.4, urea 12.9, creatinine 102.0, chest radiograph abnormal, CURB-65 score 2, PMEWS score 6, positive for swine flu hospital pathway criterion C, died 5 days after admission.

2. Female, aged 43, known asthma, presented with respiratory rate 22, heart rate 95, temperature 39.2, blood pressure 188/111, peripheral oxygen saturation 95%, GCS 15, haemoglobin 15.3, platelets 275.0, white cell count 14.0, sodium 138.0, potassium 4.2, urea 3.4, creatinine 100.0, chest radiography performed but findings not recorded, CURB-65 score 0, PMEWS score 5, negative for all swine flu hospital pathway criteria, required non-invasive ventilation.

3. Male, aged 39, known renal failure, presented with respiratory rate 16, temperature 38.7, haemoglobin 11.7. platelets 38, white cell count 1.0, sodium 132.0, potassium 4.3, urea 14.8, creatinine 569.0, chest radiography performed but findings not recorded, CURB-65 score 1, PMEWS score 1, negative for all swine flu hospital pathway criteria, required non-invasive ventilation. Also required haemodialysis for pre-existing renal failure.

4. Female, aged 25, known epilepsy, presented with respiratory rate 22, heart rate 90, blood pressure 80/40, temperature 37.5, peripheral oxygen saturation 79%, GCS 15, haemoglobin 11.8. platelets 75, white cell count 1.7, sodium 136.0, potassium 3.2, urea 4.7, creatinine 53.0, chest radiography not recorded, ECG not recorded, CURB-65 score 1, PMEWS score 7, positive for swine flu hospital pathway criteria C and E, required positive pressure ventilation and then died after 54 days.

5. Female, aged 51, known chronic lung disease, presented with respiratory rate 36, heart rate 135, temperature 37.8, blood pressure 116/80, peripheral oxygen saturation 95%, GCS 15, haemoglobin 15.3. platelets 247, white cell count 10.0, sodium 136.0, potassium 3.8, urea 4.4, creatinine 85.0, chest radiography not recorded, ECG abnormal, CURB-65 score 1, PMEWS score 8, positive for swine flu hospital pathway criterion B, required non-invasive ventilation and positive pressure ventilation.

All five patients were admitted to hospital at the initial attendance. CURB-65 scores were zero, one (three cases) and two. PMEWS scores were one, five, six, seven and eight. The swine flu hospital pathway was positive for three cases and negative for two. The C-statistic for each method was CURB-65 0.78 (95% CI 0.58 to 0.99), PMEWS 0.77 (95% CI 0.55 to 0.99) and the swine flu hospital pathway 0.70 (95% CI 0.45 to 0.96). Table 9 shows sensitivity and specificity for CURB-65 and PMEWS, with a threshold of > 1 and the swine flu hospital pathway with any criterion positive.

A further four adults and one child were admitted to critical care environments, but did not have interventions qualifying for our definition of a poor outcome. One other adult was admitted to the intensive therapy unit, but no specific interventions were recorded.

There were insufficient data for multivariate analysis to determine which clinical features and tests were independent predictors of outcome or develop new triage methods.

Chapter 5 Discussion

The number of cases of suspected pandemic influenza was much lower than predicted and the number of cases with a poor outcome was lower still. We identified two deaths and three patients who survived after requiring respiratory support among those who presented to the emergency departments of three hospitals during the second wave of the pandemic. All five cases were adults. The CURB-65 score and swine flu hospital pathway did not reliably detect these cases. A CURB-65 score of two or more has been recommended to trigger admission. 4 In our study the CURB-65 score was two or more in 7% of the adult patients and one of the five cases with a poor outcome. The swine flu hospital pathway was positive for 21% of the adult patients and three out of five cases with a poor outcome. The PMEWS score does not have a recommended threshold but a threshold of two or more has been suggested (K Challen, University Hospitals of South Manchester, May 2010, personal communication). According to this threshold PMEWS would be positive in 60% of the adult patients and four out of five cases with a poor outcome.

The findings are substantially limited by the small sample and, in particular, only including five cases with a poor outcome. These five cases may have been atypical, so we can draw no firm conclusions regarding the value of these three triage tools, other than raise some concerns about the discriminant value of existing triage methods. Furthermore, we did not test the application of the methods in practice, but calculated or inferred their performance from clinical data. Some criteria, such as the swine flu hospital pathway criterion G (other clinical concern), may have identified some of the cases with a poor outcome when used in practice.

We did not require virological testing or confirmation as both national and local guidance recommended that patients with influenza-like illness fulfilling the HPA criteria (which we used as our inclusion criteria) should be assumed to be suffering from H1N1 influenza and treated accordingly. Our aim was to complete pragmatic ‘real world’ research, reflecting as closely as possible standard working conditions. We did not use hospital admission as an outcome because we thought that this would be heavily influenced by the triage method in use. However, it is interesting to note that CURB-65 and the swine flu hospital pathway appeared to discriminate poorly between those admitted and those discharged, despite being recommended for use in triage to hospital admission. It appears that clinicians in the participating hospitals were basing their decisions on other criteria.

The study was unable to deliver on its main objectives because the caseload arising from the pandemic was much smaller than predicted. The Department of Health prepandemic planning assumptions used a base scenario of a cumulative attack rate of 25% of the population over one or more waves of 15 weeks each, with a 0.37% case fatality rate. 1 This was based on the occurrence in previous UK pandemics of an attack rate of 25–35%, and case fatality of 0.2–2%. 1 Based on data from Mexico in early 2009, the critical care bed requirement was calculated to be 140% of capacity in North West England and 160% of capacity in Yorkshire. 15 The first clinical data from Mexico in March–April 2009 demonstrated a 10- to 11-fold increase in severe pneumonia mortality in the 20- to 30-year-old age group. 16 Similarly, intensive care admissions in Australia and New Zealand were 28.7 cases per million population (15 times the normal admission rate for viral pneumonitis) in winter (June–August) 2009. 17 First-wave hospitalisations in Ontario, Canada, resulted in a 25% intensive care admission rate,18 as did the first 272 hospitalisations in the USA, with the USA also reporting a 7% mortality rate. 19 However, the second pandemic wave in Mexico in June–July 2009 demonstrated much lower severity and mortality rates, possibly due to earlier antiviral treatment coincident with a nationwide publicity campaign. 20 Similarly, there were no fatalities in the first 426 hospitalised cases in China, and only a 3.9% attack rate in screened close contacts. 21 Worldwide case severity, hospitalisation and mortality rates were all low, and in fact lower than seasonal influenza in some countries. 22

It is unclear why the experience in the UK was not similar to that in Australasia. Retrospective immunological examination of samples taken pre-pandemic (2008 and early 2009) showed protective levels of antibody to the pandemic H1N1 influenza strain in 23% of patients aged over 65 years. 23 This presumably represents crossreactivity from previous H1N1 exposure. However, there were significant pockets of H1N1 activity, notably in Birmingham and London. As of 18 March 2010, 342 deaths due to H1N1 influenza had been reported in England. Birmingham Heartlands Hospital reported that 7 out of 78 inpatients had required intensive care admission (including two patients requiring extracorporeal membrane oxygenation),24 and serological testing demonstrated an odds ratio of 5.23 for exposure to the H1N1 virus in the West Midlands (using East Midlands as a referent group). 23

There are a number of potential explanations for the lack of similar findings in the North West and Yorkshire. It may be that our inclusion criteria (derived from the HPA case definition) excluded a significant number of patients who were, in fact, infected with the H1N1 virus. It is notable that 29 out of 71 children admitted to hospital in Birmingham did not fulfil the HPA criteria. 25 As Birmingham and London were early hotspots it may be that the populations of Manchester and Sheffield were more aware of the availability of antiviral agents and therefore sought treatment earlier and mitigated the severity of their infection. There may also be confounding factors in terms of local viral evolution and pre-existing local population health that will be explored by other national projects, such as Flu-CIN (Influenza Clinical Information Network).

Although the lack of available cases was the main reason for the failure to address the main research questions, the study was also hampered by delays in acquiring research governance approval and our inability to find a case–control method that was both acceptable to the NIGB and REC and likely to yield worthwhile results. Our experience contrasts with other studies undertaken during the pandemic. At the start of the H1N1 swine influenza pandemic, participating case mix programme units were asked to submit data for confirmed H1N1 cases for rapid analysis and feedback. In addition, the Intensive Care National Audit and Research Network (ICNARC) gained ethics and research regulatory approval within 6 weeks for approximately 250 acute hospitals to collect data on critical care admissions with confirmed or suspected H1N1 influenza. 26 This process was presumably facilitated by existing ICNARC data management processes that support routine critical care audit and allow collection of anonymised data. This highlights the importance of having routine collection of audit and research data and the need to develop similar systems in emergency care. It also highlights the need to have research centres with established expertise in data processing and management. We would not have been able to meet the requirements of the NIGB within a practical timeframe were it not for our previous experience of applying for approval for a similar project.

This study also highlights the value of having reliable estimates of pandemic size and severity to assist sample size estimates. Predictions of pandemic size and severity are inevitably subject to substantial uncertainty. We could be justifiably criticised for not taking this uncertainty into account in planning this project. Future proposals for pandemic research should base sample size estimates on the full range of potential scenarios, including the possibility of no significant pandemic. Simulation methods could be useful to explore the potential value of different research methods in a range of different scenarios. These could be used to refine the research question and focus data collection upon the most useful variables, and guide adaptation of the study design as the pandemic emerges. However, it is important that simulation and analysis of different scenarios takes place well in advance of any emerging pandemic. Our proposal was developed over a few weeks in response to the emerging 2009 H1N1 pandemic, leaving no time for sophisticated protocol development. Future pandemic research should be planned and any preparatory work undertaken before the next pandemic emerges. In a similar vein, pilot data would have been helpful for protocol development and sample size estimates. However, the unpredictable nature of a pandemic means that the only opportunity to collect pilot data may also represent the only opportunity to undertake the full project. Some piloting could be undertaken prior to a pandemic, such as developing systems for data collection and protection and addressing information governance requirements. Undertaking this pilot work prior to the emergence of a pandemic could allow research to commence in a quick and efficient manner when a pandemic occurs.

As the 2009–10 H1N1 pandemic in the UK has not produced adequate numbers of severely ill patients from which to draw robust conclusions, health service planners must revert to the pre-existing evidence base. This includes information from multiple sources: non-flu risk stratification tools, SARS and H5N1, and international experience of H1N1.

Pre-pandemic advice advocated the use of pneumonia severity scores to risk stratify influenza patients. 3 Some evidence exists to support their use in identifying patients who are likely to require critical care facilities; the Pneumonia Severity Index predicts critical care admission with AUROC scores of 0.627–0.7528 and CURB-65 similarly achieves AUROC scores of 0.6129–0.77. 30 Other tools designed specifically to predict requirement for critical care exist,31–32 but have yet to be fully validated. However, in extrapolating from pneumonia-specific severity scores, it should be remembered that atypical presentation was well recognised in H5N1 patients. 33 A significant minority of both paediatric and adult patients eventually diagnosed with H1N1 did not fulfil HPA screening criteria, notably for pyrexia. 25,34 Little literature exists on risk assessment of undifferentiated emergency patients, and what there is concentrates on mortality risk. 35–37

It appears from the international experience that obesity,17 pre-existing comorbidity19 and pregnancy17,38 convey a worse prognosis during pandemic influenza infection. A single study of bacterial pneumonic superinfection in influenza from Taiwan identified shock, respiratory rate of over 24 breaths/minute, acidosis, raised creatinine and a pneumonia severity index of class IV or V as indicators of poor prognosis. 39

The SARS outbreaks in South-East Asia and Toronto, Canada, highlighted the importance of developing surge capacity in the hospital and critical care spheres, and of being able to alter institutional priorities. 40 Changes in working pattern were particularly driven by high risks of nosocomial viral transmission. 41 The surge capacity and resilience of the NHS was not severely tested by the 2009–10 A/H1N1 influenza virus outbreak, except in isolated pockets. However, there were significant problems identified with misdiagnosis and missed diagnoses during the outbreak. 42

Emergency departments should remain prepared to deal with patients with diffuse non-specific symptomatology from influenza, and retain the capability to cohort these potentially infectious patients in the emergency department and the hospital. Risk assessment will still take place in an absence of evidence but should be guided by information from the international experience.

Chapter 6 Conclusions

We can draw no reliable conclusions from the data available, other than raise potential concerns about existing triage methods for patients with suspected pandemic influenza. Our very limited data suggest that these methods may fail to discriminate between patients who will have an adverse outcome and those with a benign course. Furthermore, clinicians in our study did not generally appear to admit or discharge on the basis of these tools, despite being recommended for use in the pandemic.

Acknowledgements

We thank Susan Proctor for providing clerical assistance, and the members of the project management group, the steering committee, and staff at the participating hospitals for their help with this project. We would also like to thank Martina Santarelli and Susan Chadwick for all their hard work undertaking data entry in Sheffield and Manchester.

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Appendix 1 CURB-65 score

One point each for:

• Confusion

• Urea > 7 mmol/l

• Respiratory rate ≥ 30/minute

• Blood pressure: low systolic (< 90 mmHg) or diastolic (≤ 60 mmHg)

• age ≥ 65 years.

Appendix 2 Pandemic Modified Early Warning Score (PMEWS)

(PDF download)

Appendix 3 Community Assessment Tool for Adults

(PDF download)

Appendix 4 Community Assessment Tool for Children

(PDF download)

Appendix 5 Clinical Assessment Form (CAF)

(PDF download)

Appendix 6 Notice of substantial amendment submitted to NIGB and REC

Appendix 7 Response from NIGB

12 February 2010

Re: Application to Ethics and Confidentiality Committee for section 251 support – Emergency department triage methods for suspected pandemic influenza

Thank you for the revised study protocol for section 251 support to access patient identifiable data without consent.

Members have considered the revised protocol and due to the issues raised, requested that a new application be submitted to the next Ethics and Confidentiality Committee (ECC) meeting taking place in March. Members agreed that this application would not be suitable to be considered under the fast track procedure for the following reasons:

1. The previous application was given fast track approval at a time when there was a real urgency to have the application considered due to the level of risk which the pandemic influenza A/H1N1 2009 may have presented.

2. The pandemic influenza did not turn out to be as widespread as expected and this has implications on considerations within this new request for section 251 approval.

3. The proposed changes to the study appear to be a complete change of study methodology with a new retrospective arm.

Please explicitly consider and address the following in your submitted application:

1. The ECC position on retrospective studies of relatively small numbers of patients is that consent should be sought via the members of the direct clinical care team involved in the care and treatment of the individual cohort. If consent is not feasible, data extraction from the clinical record should be carried out by the direct clinical care team and only fully anonymised data returned to the researchers.

2. If consent is to be sought from the living cohort as described above then section 251 approval would not be required. If consent is considered to be impracticable then the section 251 application must provide strong justification as to why consent cannot be sought.

3. Similarly, justification should be provided in relation to those patients who are deceased and consent from the family cannot be obtained. Please note a clear differentiation is needed in the application between patients who are still alive and those that are deceased.

4. Please note that the deadline for submitting a fully completed application is 26 February 2010. Please ensure all required documents are submitted along with the application. A list of the documents can be found here.

If you have any queries please do not hesitate to contact me on 020 7633 7021.

Appendix 8 Response from REC

(PDF download)

Appendix 9 Chief investigator reply to NIGB

Re: Application to Ethics and Confidentiality Committee for section 251 support Emergency department triage methods for suspected pandemic influenza

Thank you for your letter of 12 February 2010 and for considering the revised protocol for this study. We will not be submitting a new application to the next Ethics and Confidentiality Committee (ECC) meeting but will instead complete the project according to the original protocol. Unfortunately, based on the information outlined in your letter, it is apparent that we will not be able to undertake a meaningful study with section 251 support using the proposed case–control methodology.

The ECC position, as outlined in your letter, is that consent should be sought via the members of the direct clinical care team involved in the care and treatment of the individual cohort, and apparently that consent should be sought from the family of those who are deceased. We have some ethical concerns about contacting recently bereaved family members but there are no insurmountable barriers to seeking consent, so we cannot claim this is not feasible. However, we would anticipate that a substantial proportion of patients or relatives would not respond to our request for consent and subsequent responder bias would render the findings of the study worthless, or at least of such limited value as to not justify the expense of the project or intrusion into patients and relatives lives.

Even if the ECC could be persuaded to alter its position on this issue we would not be able to complete the project in the timeframe required. We do not have funding available to extend staff contracts while considerations continue and would therefore have no staff available to complete the project work by the time section 251 support for the revised protocol were in place. Furthermore, clinical staff in the participating hospitals have informed us that they are neither willing nor able to commit time to extract data from the clinical records as they already have a heavy burden of clinical commitments.

We would be grateful if our comments could be fed back to the ECC.

Appendix 10 Study protocol

List of abbreviations

AUROC

area under the receiver–operator characteristic curve (C-statistic): a measure of the discriminant value of a risk prediction score

CAF

Clinical Assessment Form

CAT

Community Assessment Tool: a decision pathway for determining which patients with suspected pandemic influenza require hospital assessment and admission; it forms the basis of the swine flu hospital pathway

CLRN

Comprehensive Local Research Network

CURB-65

A risk prediction score for pneumonia, based on confusion, urea level, respiratory rate, blood pressure and age over 65 years

ECC

Ethics and Confidentiality Committee: a subcommittee of the NIGB

ECG

electrocardiogram

GCS

Glasgow Coma Score

HPA

Health Protection Agency

ICNARC

Intensive Care National Audit and Research Network

IRAS

Integrated Research Application System

NIGB

National Information Governance Board

PMEWS

Pandemic Modified Early Warning Score: a risk score for pandemic influenza based on physiological variables, age, social factors, chronic disease and performance status

PMG

Project Management Group

REC

Research Ethics Committee

ROC

receiver-operator characteristic

SD

standard deviation

SLSP

System Level Security Policy

SSI

Site Specific Information