Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility.

BACKGROUND: Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF). METHODS: For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist. RESULTS: Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations). CONCLUSIONS: Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Data-driven identification of ageing-related diseases from electronic health records.

Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82-83) for Cluster 1, 77 years (IQR 75-77) for Cluster 2, 69 years (IQR 66-71) for Cluster 3 and 57 years (IQR 54-59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.

Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK.

OBJECTIVE: To monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects. METHODS: Retrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018-2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends. RESULTS: Across nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%-58.6%) and 52.9% (52.2%-53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown and fell by 31%-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020. CONCLUSIONS: Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.

Clinical academic research in the time of Corona: A simulation study in England and a call for action.

OBJECTIVES: We aimed to model the impact of coronavirus (COVID-19) on the clinical academic response in England, and to provide recommendations for COVID-related research. DESIGN: A stochastic model to determine clinical academic capacity in England, incorporating the following key factors which affect the ability to conduct research in the COVID-19 climate: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics). SETTING: Clinical academics in primary and secondary care in England. PARTICIPANTS: Equivalent of 3200 full-time clinical academics in England. INTERVENTIONS: Four policy approaches to COVID-19 with differing population infection rates: "Italy model" (6%), "mitigation" (10%), "relaxed mitigation" (40%) and "do-nothing" (80%) scenarios. Low and high strain on the health system (no clinical academics able to do research at 10% and 5% infection rate, respectively. MAIN OUTCOME MEASURES: Number of full-time clinical academics available to conduct clinical research during the pandemic in England. RESULTS: In the "Italy model", "mitigation", "relaxed mitigation" and "do-nothing" scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively-with no clinical academics at all for 37 days in the "do-nothing" scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11, 12, 30 and 26 weeks respectively. CONCLUSIONS: Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.

Global health competencies in UK postgraduate medical training: a scoping review and curricular content analysis.

OBJECTIVE: To assess global health (GH) training in all postgraduate medical education in the UK. DESIGN: Mixed methodology: scoping review and curricular content analysis using two GH competency frameworks. SETTING AND PARTICIPANTS: A scoping review (until December 2017) was used to develop a framework of GH competencies for doctors. National postgraduate medical training curricula were analysed against this and a prior framework for GH competencies. The number of core competencies addressed and/or appearing in each programme was recorded. OUTCOMES: The scoping review identified eight relevant publications. A 16-competency framework was developed and, with a prior 5-competency framework, used to analyse each of 71 postgraduate medical curricula. Curricula were examined by a team of researchers and relevant learning outcomes were coded as one of the 5 or 16 core competencies. The number of core competencies in each programme was recorded. RESULTS: Using the 5-competency and 16-competency frameworks, 23 and 20, respectively, out of 71 programmes contained no global health competencies, most notably the Foundation Programme (equivalent to internship), a compulsory programme for UK medical graduates. Of a possible 16 competencies, the mean number across all 71 programmes was 1.73 (95% CI 1.42 to 2.04) and the highest number were in paediatrics and infectious diseases, each with five competencies. Of the 16 core competencies, global burden of disease and socioeconomic determinants of health were the two most cited with 47 and 35 citations, respectively. 8/16 competencies were not cited in any curriculum. CONCLUSIONS: Equity of care and the challenges of practising in an increasingly globalised world necessitate GH competencies for all doctors. Across the whole of postgraduate training, the majority of UK doctors are receiving minimal or no training in GH. Our GH competency framework can be used to map and plan integration across postgraduate programmes.

Socioeconomic disparities in first stroke incidence, quality of care, and survival: a nationwide registry-based cohort study of 44 million adults in England.

BACKGROUND: We aimed to estimate socioeconomic disparities in the incidence of hospitalisation for first-ever stroke, quality of care, and post-stroke survival for the adult population of England. METHODS: In this cohort study, we obtained data collected by a nationwide register on patients aged 18 years or older hospitalised for first-ever acute ischaemic stroke or primary intracerebral haemorrhage in England from July 1, 2013, to March 31, 2016. We classified socioeconomic status at the level of Lower Super Output Areas using the Index of Multiple Deprivation, a neighbourhood measure of deprivation. Multivariable models were fitted to estimate the incidence of hospitalisation for first stroke (negative binomial), quality of care using 12 quality metrics (multilevel logistic), and all-cause 1 year case fatality (Cox proportional hazards). FINDINGS: Of the 43·8 million adults in England, 145 324 were admitted to hospital with their first-ever stroke: 126 640 (87%) with ischaemic stroke, 17 233 (12%) with intracerebral haemorrhage, and 1451 (1%) with undetermined stroke type. We observed a socioeconomic gradient in the incidence of hospitalisation for ischaemic stroke (adjusted incidence rate ratio 2·0, 95% CI 1·7-2·3 for the most vs least deprived deciles) and intracerebral haemorrhage (1·6, 1·3-1·9). Patients from the lowest socioeconomic groups had first stroke a median of 7 years earlier than those from the highest (p<0·0001), and had a higher prevalence of pre-stroke disability and diabetes. Patients from lower socioeconomic groups were less likely to receive five of 12 care processes but were more likely to receive early supported discharge (adjusted odds ratio 1·14, 95% CI 1·07-1·22). Low socioeconomic status was associated with a 26% higher adjusted risk of 1-year mortality (adjusted hazard ratio 1·26, 95% CI 1·20-1·33, for highest vs lowest deprivation decile), but this gradient was largely attenuated after adjustment for the presence of pre-stroke diabetes, hypertension, and atrial fibrillation (1·11, 1·05-1·17). INTERPRETATION: Wide socioeconomic disparities exist in the burden of ischaemic stroke and intracerebral haemorrhage in England, most notably in stroke hospitalisation risk and case fatality and, to a lesser extent, in the quality of health care. Reducing these disparities requires interventions to improve the quality of acute stroke care and address disparities in cardiovascular risk factors present before stroke. FUNDING: NHS England and the Welsh Government.

Multimorbidity and survival for patients with acute myocardial infarction in England and Wales: Latent class analysis of a nationwide population-based cohort.

BACKGROUND: There is limited knowledge of the scale and impact of multimorbidity for patients who have had an acute myocardial infarction (AMI). Therefore, this study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI. METHODS AND FINDINGS: This national observational study included 693,388 patients (median age 70.7 years, 452,896 [65.5%] male) from the Myocardial Ischaemia National Audit Project (England and Wales) who were admitted with AMI between 1 January 2003 and 30 June 2013. There were 412,809 (59.5%) patients with multimorbidity at the time of admission with AMI, i.e., having at least 1 of the following long-term health conditions: diabetes, chronic obstructive pulmonary disease or asthma, heart failure, renal failure, cerebrovascular disease, peripheral vascular disease, or hypertension. Those with heart failure, renal failure, or cerebrovascular disease had the worst outcomes (39.5 [95% CI 39.0-40.0], 38.2 [27.7-26.8], and 26.6 [25.2-26.4] deaths per 100 person-years, respectively). Latent class analysis revealed 3 multimorbidity phenotype clusters: (1) a high multimorbidity class, with concomitant heart failure, peripheral vascular disease, and hypertension, (2) a medium multimorbidity class, with peripheral vascular disease and hypertension, and (3) a low multimorbidity class. Patients in class 1 were less likely to receive pharmacological therapies compared with class 2 and 3 patients (including aspirin, 83.8% versus 87.3% and 87.2%, respectively; ß-blockers, 74.0% versus 80.9% and 81.4%; and statins, 80.6% versus 85.9% and 85.2%). Flexible parametric survival modelling indicated that patients in class 1 and class 2 had a 2.4-fold (95% CI 2.3-2.5) and 1.5-fold (95% CI 1.4-1.5) increased risk of death and a loss in life expectancy of 2.89 and 1.52 years, respectively, compared with those in class 3 over the 8.4-year follow-up period. The study was limited to all-cause mortality due to the lack of available cause-specific mortality data. However, we isolated the disease-specific association with mortality by providing the loss in life expectancy following AMI according to multimorbidity phenotype cluster compared with the general age-, sex-, and year-matched population. CONCLUSIONS: Multimorbidity among patients with AMI was common, and conferred an accumulative increased risk of death. Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03037255.

Prognostic burden of heart failure recorded in primary care, acute hospital admissions, or both: a population-based linked electronic health record cohort study in 2.1 million people.

AIMS: The prognosis of patients hospitalized for worsening heart failure (HF) is well described, but not that of patients managed solely in non-acute settings such as primary care or secondary outpatient care. We assessed the distribution of HF across levels of healthcare, and assessed the prognostic differences for patients with HF either recorded in primary care (including secondary outpatient care) (PC), hospital admissions alone, or known in both contexts. METHODS AND RESULTS: This study was part of the CALIBER programme, which comprises linked data from primary care, hospital admissions, and death certificates for 2.1 million inhabitants of England. We identified 89 554 patients with newly recorded HF, of whom 23 547 (26%) were recorded in PC but never hospitalized, 30 629 (34%) in hospital admissions but not known in PC, 23 681 (27%) in both, and 11 697 (13%) in death certificates only. The highest prescription rates of ACE inhibitors, beta-blockers, and mineralocorticoid receptor antagonists was found in patients known in both contexts. The respective 5-year survival in the first three groups was 43.9% [95% confidence interval (CI) 43.2-44.6%], 21.7% (95% CI 21.1-22.2%), and 39.8% (95% CI 39.2-40.5%), compared with 88.1% (95% CI 87.9-88.3%) in the age- and sex-matched general population. CONCLUSION: In the general population, one in four patients with HF will not be hospitalized for worsening HF within a median follow-up of 1.7 years, yet they still have a poor 5-year prognosis. Patients admitted to hospital with worsening HF but not known with HF in primary care have the worst prognosis and management. Mitigating the prognostic burden of HF requires greater consistency across primary and secondary care in the identification, profiling, and treatment of patients. TRIAL REGISTRATION: NCT02551016.

Long-term healthcare use and costs in patients with stable coronary artery disease: a population-based cohort using linked health records (CALIBER).

AIMS: To examine long-term healthcare utilization and costs of patients with stable coronary artery disease (SCAD). METHODS AND RESULTS: Linked cohort study of 94 966 patients with SCAD in England, 1 January 2001 to 31 March 2010, identified from primary care, secondary care, disease, and death registries. Resource use and costs, and cost predictors by time and 5-year cardiovascular disease (CVD) risk profile were estimated using generalized linear models. Coronary heart disease hospitalizations were 20.5% in the first year and 66% in the year following a non-fatal (myocardial infarction, ischaemic or haemorrhagic stroke) event. Mean healthcare costs were £3133 per patient in the first year and £10 377 in the year following a non-fatal event. First-year predictors of cost included sex (mean cost £549 lower in females), SCAD diagnosis (non-ST-elevation myocardial infarction cost £656 more than stable angina), and co-morbidities (heart failure cost £657 more per patient). Compared with lower risk patients (5-year CVD risk 3.5%), those of higher risk (5-year CVD risk 44.2%) had higher 5-year costs (£23 393 vs. £9335) and lower lifetime costs (£43 020 vs. £116 888). CONCLUSION: Patients with SCAD incur substantial healthcare utilization and costs, which varies and may be predicted by 5-year CVD risk profile. Higher risk patients have higher initial but lower lifetime costs than lower risk patients as a result of shorter life expectancy. Improved cardiovascular survivorship among an ageing CVD population is likely to require stratified care in anticipation of the burgeoning demand.

Using electronic health records to predict costs and outcomes in stable coronary artery disease.

OBJECTIVES: To use electronic health records (EHR) to predict lifetime costs and health outcomes of patients with stable coronary artery disease (stable-CAD) stratified by their risk of future cardiovascular events, and to evaluate the cost-effectiveness of treatments targeted at these populations. METHODS: The analysis was based on 94 966 patients with stable-CAD in England between 2001 and 2010, identified in four prospectively collected, linked EHR sources. Markov modelling was used to estimate lifetime costs and quality-adjusted life years (QALYs) stratified by baseline cardiovascular risk. RESULTS: For the lowest risk tenth of patients with stable-CAD, predicted discounted remaining lifetime healthcare costs and QALYs were £62 210 (95% CI £33 724 to £90 043) and 12.0 (95% CI 11.5 to 12.5) years, respectively. For the highest risk tenth of the population, the equivalent costs and QALYs were £35 549 (95% CI £31 679 to £39 615) and 2.9 (95% CI 2.6 to 3.1) years, respectively. A new treatment with a hazard reduction of 20% for myocardial infarction, stroke and cardiovascular disease death and no side-effects would be cost-effective if priced below £72 per year for the lowest risk patients and £646 per year for the highest risk patients. CONCLUSIONS: Existing EHRs may be used to estimate lifetime healthcare costs and outcomes of patients with stable-CAD. The stable-CAD model developed in this study lends itself to informing decisions about commissioning, pricing and reimbursement. At current prices, to be cost-effective some established as well as future stable-CAD treatments may require stratification by patient risk.

Comparison of hospital variation in acute myocardial infarction care and outcome between Sweden and United Kingdom: population based cohort study using nationwide clinical registries.

OBJECTIVE: To assess the between hospital variation in use of guideline recommended treatments and clinical outcomes for acute myocardial infarction in Sweden and the United Kingdom. DESIGN: Population based longitudinal cohort study using nationwide clinical registries. SETTING AND PARTICIPANTS: Nationwide registry data comprising all hospitals providing acute myocardial infarction care in Sweden (SWEDEHEART/RIKS-HIA, n=87; 119,786 patients) and the UK (NICOR/MINAP, n=242; 391,077 patients), 2004-10. MAIN OUTCOME MEASURES: Between hospital variation in 30 day mortality of patients admitted with acute myocardial infarction. RESULTS: Case mix standardised 30 day mortality from acute myocardial infarction was lower in Swedish hospitals (8.4%) than in UK hospitals (9.7%), with less variation between hospitals (interquartile range 2.6% v 3.5%). In both countries, hospital level variation and 30 day mortality were inversely associated with provision of guideline recommended care. Compared with the highest quarter, hospitals in the lowest quarter for use of primary percutaneous coronary intervention had higher volume weighted 30 day mortality for ST elevation myocardial infarction (10.7% v 6.6% in Sweden; 12.7% v 5.8% in the UK). The adjusted odds ratio comparing the highest with the lowest quarters for hospitals' use of primary percutaneous coronary intervention was 0.70 (95% confidence interval 0.62 to 0.79) in Sweden and 0.68 (0.60 to 0.76) in the UK. Differences in risk between hospital quarters of treatment for non-ST elevation myocardial infarction and secondary prevention drugs for all discharged acute myocardial infarction patients were smaller than for reperfusion treatment in both countries. CONCLUSION: Between hospital variation in 30 day mortality for acute myocardial infarction was greater in the UK than in Sweden. This was associated with, and may be partly accounted for by, the higher practice variation in acute myocardial infarction guideline recommended treatment in the UK hospitals. High quality healthcare across all hospitals, especially in the UK, with better use of guideline recommended treatment, may not only reduce unacceptable practice variation but also deliver improved clinical outcomes for patients with acute myocardial infarction. Clinical trials registration Clinical trials NCT01359033.

Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention.

BACKGROUND: Recent experimental evidence suggests that socioeconomic characteristics of neighbourhoods influence cardiovascular health, but observational studies which examine deprivation across a wide range of cardiovascular diseases (CVDs) are lacking. METHODS: Record-linkage cohort study of 1.93 million people to examine the association between small-area socioeconomic deprivation and 12 CVDs. Health records covered primary care, hospital admissions, a myocardial infarction registry and cause-specific mortality in England (CALIBER). Patients were aged ≥30 years and were initially free of CVD. Cox proportional hazard models stratified by general practice were used. FINDINGS: During a median follow-up of 5.5 years 114,859 people had one of 12 initial CVD presentations. In women the hazards of all CVDs except abdominal aortic aneurysm increased linearly with higher small-area socioeconomic deprivation (adjusted HR for most vs. least deprived ranged from 1.05, 95%CI 0.83-1.32 for abdominal aortic aneurysm to 1.55, 95%CI 1.42-1.70 for heart failure; I2 = 81.9%, τ2 = 0.01). In men heterogeneity was higher (HR ranged from 0.89, 95%CI 0.75-1.06 for cardiac arrest to 1.85, 95%CI 1.67-2.04 for peripheral arterial disease; I2 = 96.0%, τ2 = 0.06) and no association was observed with stable angina, sudden cardiac death, subarachnoid haemorrhage, transient ischaemic attack and abdominal aortic aneurysm. Lifetime risk difference between least and most deprived quintiles was most marked for peripheral arterial disease in women (4.3% least deprived, 5.8% most deprived) and men (4.6% least deprived, 7.8% in most deprived); but it was small or negligible for sudden cardiac death, transient ischaemic attack, abdominal aortic aneurysm and ischaemic and intracerebral haemorrhage, in both women and men. CONCLUSIONS: Associations of small-area socioeconomic deprivation with 12 types of CVDs were heterogeneous, and in men absent for several diseases. Findings suggest that policies to reduce deprivation may impact more strongly on heart failure and peripheral arterial disease, and might be more effective in women.

Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER).

The goal of cardiovascular disease (CVD) research using linked bespoke studies and electronic health records (CALIBER) is to provide evidence to inform health care and public health policy for CVDs across different stages of translation, from discovery, through evaluation in trials to implementation, where linkages to electronic health records provide new scientific opportunities. The initial approach of the CALIBER programme is characterized as follows: (i) Linkages of multiple electronic heath record sources: examples include linkages between the longitudinal primary care data from the Clinical Practice Research Datalink, the national registry of acute coronary syndromes (Myocardial Ischaemia National Audit Project), hospitalization and procedure data from Hospital Episode Statistics and cause-specific mortality and social deprivation data from the Office of National Statistics. Current cohort analyses involve a million people in initially healthy populations and disease registries with ∼10(5) patients. (ii) Linkages of bespoke investigator-led cohort studies (e.g. UK Biobank) to registry data (e.g. Myocardial Ischaemia National Audit Project), providing new means of ascertaining, validating and phenotyping disease. (iii) A common data model in which routine electronic health record data are made research ready, and sharable, by defining and curating with meta-data >300 variables (categorical, continuous, event) on risk factors, CVDs and non-cardiovascular comorbidities. (iv) Transparency: all CALIBER studies have an analytic protocol registered in the public domain, and data are available (safe haven model) for use subject to approvals. For more information, e-mail s.denaxas@ucl.ac.uk.

Is access to specialist assessment of chest pain equitable by age, gender, ethnicity and socioeconomic status? An enhanced ecological analysis.

OBJECTIVES: To determine whether access to rapid access chest pain clinics of people with recent onset symptoms is equitable by age, socioeconomic status, ethnicity and gender, according to need. DESIGN: Retrospective cohort study with ecological analysis. SETTING: Patients referred from primary care to five rapid access chest pain clinics in secondary care, across England. PARTICIPANTS: Of 8647 patients aged ≥35 years referred to chest pain clinics with new-onset stable chest pain but no known cardiac history, 7570 with documented census ward codes, age, gender and ethnicity comprised the study group. Patients excluded were those with missing date of birth, gender or ethnicity (n=782) and those with missing census ward codes (n=295). OUTCOME MEASURES: Effects of age, gender, ethnicity and socioeconomic status on clinic attendance were calculated as attendance rate ratios, with number of attendances as the outcome and resident population-years as the exposure in each stratum, using Poisson regression. Attendance rate ratios were then compared with coronary heart disease (CHD) mortality ratios to determine whether attendance was equitable according to need. RESULTS: Adjusted attendance rate ratios for patients aged >65 years were similar to younger patients (1.1, 95% CI 1.05 to 1.16), despite population CHD mortality rate ratios nearly 15 times higher in the older age group. Women had lower attendance rate ratios (0.81, 95% CI 0.77 to 0.84) and also lower population CHD mortality rate ratios compared with men. South Asians had higher attendance rates (1.67, 95% CI 1.57 to 1.77) compared with whites and had a higher standardised CHD mortality ratio of 1.46 (95% CI 1.41 to 1.51). Although univariable analysis showed that the most deprived patients (quintile 5) had an attendance rate twice that of less deprived quintiles, the adjusted analysis showed their attendance to be 13% lower (0.87, 95% CI 0.81 to 0.94) despite a higher population CHD mortality rate. CONCLUSION: There is evidence of underutilisation of chest pain clinics by older people and those from lower socioeconomic status. More robust and patient focused administrative pathways need to be developed to detect inequity, correction of which has the potential to substantially reduce coronary mortality.

Angina pectoris: relation of epidemiological survey to registry data.

BACKGROUND: Self-reported angina symptoms are collected in epidemiological surveys. We aimed at validating the angina symptoms assessed by the Rose Questionnaire against registry data on coronary heart disease. A further aim was to examine the sex paradox in angina implying that women report more symptoms, whereas men have more coronary events. DESIGN: Angina symptoms of 6601 employees of the City of Helsinki were examined using the postal questionnaire survey data combined with coronary heart disease registries. METHODS: The self-reported angina was classified as no symptoms, atypical pain, exertional chest pain, and stable angina symptoms. Reimbursed medications and hospital admissions were available from registries 10 years before the survey. Binomial regression analysis was used. RESULTS: Stable angina symptoms were associated with hospital admissions and reimbursed medications [prevalence ratio (PR), 6.75; 95% confidence interval (CI), 4.56-9.99]. In addition, exertional chest pain (PR, 5.31; 95% CI, 3.45-8.18) was associated with coronary events. All events were more prevalent among men than women (PR, 2.36; 95% CI, 1.72-3.25). CONCLUSION: The Rose Questionnaire remains a valid tool to distinguish healthy people from those with coronary heart disease. However, a notable part of those reporting symptoms have no confirmation of coronary heart disease in the registries. The female excess of symptoms and male excess of events may reflect inequality or delay in access to treatment, problems in identification and diagnosis, or more complex issues related to self-reported angina symptoms.

Assessing the cost effectiveness of using prognostic biomarkers with decision models: case study in prioritising patients waiting for coronary artery surgery.

OBJECTIVE: To determine the effectiveness and cost effectiveness of using information from circulating biomarkers to inform the prioritisation process of patients with stable angina awaiting coronary artery bypass graft surgery. DESIGN: Decision analytical model comparing four prioritisation strategies without biomarkers (no formal prioritisation, two urgency scores, and a risk score) and three strategies based on a risk score using biomarkers: a routinely assessed biomarker (estimated glomerular filtration rate), a novel biomarker (C reactive protein), or both. The order in which to perform coronary artery bypass grafting in a cohort of patients was determined by each prioritisation strategy, and mean lifetime costs and quality adjusted life years (QALYs) were compared. DATA SOURCES: Swedish Coronary Angiography and Angioplasty Registry (9935 patients with stable angina awaiting coronary artery bypass grafting and then followed up for cardiovascular events after the procedure for 3.8 years), and meta-analyses of prognostic effects (relative risks) of biomarkers. RESULTS: The observed risk of cardiovascular events while on the waiting list for coronary artery bypass grafting was 3 per 10,000 patients per day within the first 90 days (184 events in 9935 patients). Using a cost effectiveness threshold of pound20,000- pound30,000 (euro22,000-euro33,000; $32,000-$48,000) per additional QALY, a prioritisation strategy using a risk score with estimated glomerular filtration rate was the most cost effective strategy (cost per additional QALY was < pound410 compared with the Ontario urgency score). The impact on population health of implementing this strategy was 800 QALYs per 100,000 patients at an additional cost of pound 245,000 to the National Health Service. The prioritisation strategy using a risk score with C reactive protein was associated with lower QALYs and higher costs compared with a risk score using estimated glomerular filtration rate. CONCLUSION: Evaluating the cost effectiveness of prognostic biomarkers is important even when effects at an individual level are small. Formal prioritisation of patients awaiting coronary artery bypass grafting using a routinely assessed biomarker (estimated glomerular filtration rate) along with simple, routinely collected clinical information was cost effective. Prioritisation strategies based on the prognostic information conferred by C reactive protein, which is not currently measured in this context, or a combination of C reactive protein and estimated glomerular filtration rate, is unlikely to be cost effective. The widespread practice of using only implicit or informal means of clinically ordering the waiting list may be harmful and should be replaced with formal prioritisation approaches.