Prediction of incident chronic kidney disease in community-based electronic health records: a systematic review and meta-analysis.

Background: Chronic kidney disease (CKD) is a major global health problem and its early identification would allow timely intervention to reduce complications. We performed a systematic review and meta-analysis of multivariable prediction models derived and/or validated in community-based electronic health records (EHRs) for the prediction of incident CKD in the community. Methods: Ovid Medline and Ovid Embase were searched for records from 1947 to 31 January 2024. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST) and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Results: Seven studies met inclusion criteria, describing 12 prediction models, with two eligible for meta-analysis including 2 173 202 patients. The Chronic Kidney Disease Prognosis Consortium (CKD-PC) (summary c-statistic 0.847; 95% CI 0.827-0.867; 95% PI 0.780-0.905) and SCreening for Occult REnal Disease (SCORED) (summary c-statistic 0.811; 95% CI 0.691-0.926; 95% PI 0.514-0.992) models had good model discrimination performance. Risk of bias was high in 64% of models, and driven by the analysis domain. No model met eligibility for meta-analysis if studies at high risk of bias were excluded, and certainty of effect estimates was 'low'. No clinical utility analyses or clinical impact studies were found for any of the models. Conclusions: Models derived and/or externally validated for prediction of incident CKD in community-based EHRs demonstrate good prediction performance, but assessment of clinical usefulness is limited by high risk of bias, low certainty of evidence and a lack of impact studies.

Risk calculator for incident atrial fibrillation across a range of prediction horizons.

BACKGROUND: The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years. METHODS: We used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc and C2HEST scores. RESULTS: Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHA2DS2-VASc and C2HEST scores. The models are available as a web-based FIND-AF calculator. CONCLUSIONS: The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study.

Bone scintigraphy with SPECT/CT in paediatric patients with neck and back pain in comparison with other imaging modalities: the experience of a tertiary paediatric referral centre.

OBJECTIVE: Retrospective review of the additional clinical value provided by single photon emission computed tomography-computed tomography (SPECT/CT) in children and adolescents with back pain. METHODS: A total of 207 consecutive paediatric patients (94 males, 113 females, age range 5-17 years, median age 14 years) were reviewed after referral to the spinal surgery clinic of a paediatric teaching hospital between November 2009 and February 2021. All patients had either only whole spine X-rays or whole spine x-rays and MRI, along with bone scan with planar whole-body images and SPECT/CT (with spot views of painful area). RESULTS: X-ray identified the pain generator in 23 of 177 (13.0%) cases. MRI identified the pain generator in 49 of 165 (29.7%) cases. SPECT/CT reported relevant positive findings which identified the pain generator in 107 of 185 (57.8%) cases. SPECT/CT changed patients' management in 72 of 185 (38.9%) cases. SPECT/CT was most effective at identifying the pain generator in cases of facet arthropathy, previous vertebral fracture and patients with previous deformity correction, where the pain generator was identified in 76.5% (13 of 17), 71.4% (5 of 7) and 63.4% (26 of 41) of cases, respectively. CT settings were adjusted to minimise the radiation burden (50 mAs/80kVp under 8 years, 24 mAs/110 kVp over 8 years). CONCLUSION: The role of SPECT/CT in diagnosing back pain is justified in selected paediatric patients, particularly with diagnostic uncertainty using conventional imaging. The CT component of the SPECT/CT study produced a lower radiation dose than conventional CT imaging, whilst producing bone images of diagnostic quality.

Diabetes mellitus and presentation, care and outcomes of patients with NSTEMI: the Association for Acute Cardiovascular Care-European Association of Percutaneous Cardiovascular Interventions EURObservational Research Programme NSTEMI Registry of the European Society of Cardiology.

AIM: Diabetes mellitus (diabetes) is common amongst patients with NSTEMI. We describe presentation, care and outcomes of patients admitted with NSTEMI by diabetes status. METHODS: Prospective cohort study including 2928 patients (1104 with prior diabetes, 1824 without) admitted to hospital with NSTEMI from 287 centres in 59 countries. Quality of care was evaluated based on 12 guideline-recommended care interventions. Outcomes included in-hospital acute heart failure, cardiogenic shock, repeat myocardial infarction, stroke/transient ischaemic attack (TIA), BARC Type ≥ 3 bleeding and death, as well as 30-day mortality. RESULTS: Patients with diabetes had higher comorbidity burden and more frequently presented with Killip Class II-IV heart failure (10.2% vs 3.7%, P < 0.001), haemodynamic instability (7.1% vs 3.7%, P < 0.001) and ongoing chest pain (43.1% vs 37.0%, P < 0.001), than those without diabetes. Overall, care quality received was similar by diabetes status (60.0% vs 60.5% received ≥ 80% of eligible care interventions, P = 0.786), but patients with diabetes experienced higher rates of in-hospital acute heart failure (15.3% vs 6.8% P < 0.001), cardiogenic shock (4.5% vs 2.5%, P = 0.002), stroke/TIA (2.0% vs 0.8%, P = 0.006) and death (2.5% vs 1.4%, P = 0.022), and higher 30-day mortality (3.3% vs 2.0%, P = 0.025). Of NSTEMI with diabetes, only 1.9% and 9.0% received prescription for GLP-1 RAs and SGLT2 inhibitors, respectively, on discharge, and only 45.9% were referred for cardiac rehabilitation. CONCLUSION: NSTEMI patients with diabetes, compared to those without, present more clinically unwell and have worse outcomes despite receiving equal quality of care. Prescription of cardiovascular-protective glycaemic agents is an actionable target to reduce risk of further events.

Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study.

INTRODUCTION: Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF. METHODS AND ANALYSIS: We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely collected primary care EHRs to predict risk of new-onset HF over 1, 5 and 10 years prediction horizons. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation (training and testing) and the CPRD-AURUM dataset for external validation. Both comprise large cohorts of patients, representative of the population of England in terms of age, sex and ethnicity. Primary care records are linked at patient level to secondary care and mortality data. The performance of the prediction model will be assessed by discrimination, calibration and clinical utility. We will only use variables routinely accessible in primary care. ETHICS AND DISSEMINATION: Permissions for CPRD-GOLD and CPRD-AURUM datasets were obtained from CPRD (ref no: 21_000324). The CPRD ethical approval committee approved the study. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION DETAILS: The study was registered on Clinical Trials.gov (NCT05756127). A systematic review for the project was registered on PROSPERO (registration number: CRD42022380892).

Sex-specific presentation, care, and clinical events in individuals admitted with NSTEMI: the ACVC-EAPCI EORP NSTEMI registry of the European Society of Cardiology.

AIMS: Women have historically been disadvantaged in terms of care and outcomes for non-ST-segment elevation myocardial infarction (NSTEMI). We describe patterns of presentation, care, and outcomes for NSTEMI by sex in a contemporary and geographically diverse cohort. METHODS AND RESULTS: Prospective cohort study including 2947 patients (907 women, 2040 men) with Type I NSTEMI from 287 centres in 59 countries, stratified by sex. Quality of care was evaluated based on 12 guideline-recommended care interventions. The all-or-none scoring composite performance measure was used to define receipt of optimal care. Outcomes included acute heart failure, cardiogenic shock, repeat myocardial infarction, stroke/transient ischaemic attack, BARC Type ≥3 bleeding, or death in-hospital, as well as 30-day mortality. Women admitted with NSTEMI were older, more comorbid, and more frequently categorized as at higher ischaemic (GRACE >140, 54.0% vs. 41.7%, P < 0.001) and bleeding (CRUSADE >40, 51.7% vs. 17.6%, P < 0.001) risk than men. Women less frequently received invasive coronary angiography (ICA; 83.0% vs. 89.5%, P < 0.001), smoking cessation advice (46.4% vs. 69.5%, P < 0.001), and P2Y12 inhibitor prescription at discharge (81.9% vs. 90.0%, P < 0.001). Non-receipt of ICA was more often due to frailty for women than men (16.7% vs. 7.8%, P = 0.010). At ICA, more women than men had non-obstructive coronary artery disease or angiographically normal arteries (15.8% vs. 6.3%, P < 0.001). Rates of in-hospital adverse outcomes and 30-day mortality were low and did not differ by sex. CONCLUSION: In contemporary practice, women presenting with NSTEMI, compared with men, less frequently receive antiplatelet prescription, smoking cessation advice, or are considered eligible for ICA.

Risk of atrial fibrillation and association with other diseases: protocol of the derivation and international external validation of a prediction model using nationwide population-based electronic health records.

INTRODUCTION: Atrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies. METHODS AND ANALYSIS: We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states. ETHICS AND DISSEMINATION: Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION NUMBER: A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).

Inequalities in care delivery and outcomes for myocardial infarction, heart failure, atrial fibrillation, and aortic stenosis in the United Kingdom.

Cardiovascular diseases are a leading cause of death and disability globally, with inequalities in burden and care delivery evident in Europe. To address this challenge, The Lancet Regional Health-Europe convened experts from a range of countries to summarise the current state of knowledge on cardiovascular disease inequalities across Europe. This Series paper presents evidence from nationwide secondary care registries and primary care healthcare records regarding inequalities in care delivery and outcomes for myocardial infarction, heart failure, atrial fibrillation, and aortic stenosis in the National Health Service (NHS) across the United Kingdom (UK) by age, sex, ethnicity and geographical location. Data suggest that women and older people less frequently receive guideline-recommended treatment than men and younger people. There are limited publications about ethnicity in the UK for the studied disease areas. Finally, there is inter-healthcare provider variation in cardiovascular care provision, especially for transcatheter aortic valve implantation, which is associated with differing outcomes for patients with the same disease. Providing equitable care is a founding principle of the UK NHS, which is well positioned to deliver innovative policy responses to reverse observed inequalities. Understanding differences in care may enable the implementation of appropriate strategies to mitigate differences in outcomes.

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

INTRODUCTION: Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway. METHODS AND ANALYSIS: The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring, and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring. ETHICS AND DISSEMINATION: The study has ethical approval (the North West-Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder's open access policy. TRIAL REGISTRATION NUMBER: NCT05898165.

Prognosis, characteristics, and provision of care for patients with the unspecified heart failure electronic health record phenotype: a population-based linked cohort study of 95262 individuals.

Background: Whether the accuracy of the phenotype ascribed to patients in electronic health records (EHRs) is associated with variation in prognosis and care provision is unknown. We investigated this for heart failure (HF, characterised as HF with preserved ejection fraction [HFpEF], HF with reduced ejection fraction [HFrEF] and unspecified HF). Methods: We included individuals aged 16 years and older with a new diagnosis of HF between January 2, 1998 and February 28, 2022 from linked primary and secondary care records in the Clinical Practice Research Datalink in England. We investigated the provision of guideline-recommended diagnostic investigations and pharmacological treatments. The primary outcome was a composite of HF hospitalisation or all-cause death, and secondary outcomes were time to HF hospitalisation, all-cause death and death from cardiovascular causes. We used Kaplan-Meier curves and log rank tests to compare survival across HF phenotypes and adjusted for potential confounders in Cox proportional hazards regression analyses. Findings: Of a cohort of 95,262 individuals, 1271 (1.3%) were recorded as having HFpEF, 10,793 (11.3%) as HFrEF and 83,198 (87.3%) as unspecified HF. Individuals recorded as unspecified HF were older with a higher prevalence of dementia. Unspecified HF, compared to patients with a recorded HF phenotype, were less likely to receive specialist assessment, echocardiography or natriuretic peptide testing in the peri-diagnostic period, or receive angiotensin-converting enzyme inhibitors, beta blockers or mineralocorticoid receptor antagonists up to 12 months after diagnosis (risk ratios compared to HFrEF, 0.64, 95% CI 0.63-0.64; 0.59, 0.58-0.60; 0.57, 0.55-0.59; respectively) and had significantly worse outcomes (adjusted hazard ratios compared to HFrEF, HF hospitalisation and death 1.66, 95% CI 1.59-1.74; all-cause mortality 2.00, 1.90-2.10; cardiovascular death 1.77, 1.65-1.90). Interpretation: Our findings suggested that absence of specification of HF phenotype in routine EHRs is inversely associated with clinical investigations, treatments and survival, representing an actionable target to mitigate prognostic and health resource burden. Funding: Japan Research Foundation for Healthy Aging and British Heart Foundation.

Temporal trends of cause-specific mortality after diagnosis of atrial fibrillation.

BACKGROUND AND AIMS: Reports of outcomes after atrial fibrillation (AF) diagnosis are conflicting. The aim of this study was to investigate mortality and hospitalization rates following AF diagnosis over time, by cause and by patient features. METHODS: Individuals aged ≥16 years with a first diagnosis of AF were identified from the UK Clinical Practice Research Datalink-GOLD dataset from 1 January 2001, to 31 December 2017. The primary outcomes were all-cause and cause-specific mortality and hospitalization at 1 year following diagnosis. Poisson regression was used to calculate rate ratios (RRs) for mortality and incidence RRs (IRRs) for hospitalization and 95% confidence intervals (CIs) comparing 2001/02 and 2016/17, adjusted for age, sex, region, socio-economic status, and 18 major comorbidities. RESULTS: Of 72 412 participants, mean (standard deviation) age was 75.6 (12.4) years, and 44 762 (61.8%) had ≥3 comorbidities. All-cause mortality declined (RR 2016/17 vs. 2001/02 0.72; 95% CI 0.65-0.80), with large declines for cardiovascular (RR 0.46; 95% CI 0.37-0.58) and cerebrovascular mortality (RR 0.41; 95% CI 0.29-0.60) but not for non-cardio/cerebrovascular causes of death (RR 0.91; 95% CI 0.80-1.04). In 2016/17, deaths caused from dementia (67, 8.0%), outstripped deaths from acute myocardial infarction, heart failure, and acute stroke combined (56, 6.7%, P < .001). Overall hospitalization rates increased (IRR 2016/17 vs. 2001/02 1.17; 95% CI, 1.13-1.22), especially for non-cardio/cerebrovascular causes (IRR 1.42; 95% CI 1.39-1.45). Older, more deprived, and hospital-diagnosed AF patients experienced higher event rates. CONCLUSIONS: After AF diagnosis, cardio/cerebrovascular mortality and hospitalization has declined, whilst hospitalization for non-cardio/cerebrovascular disease has increased.

Incident cardiovascular, renal, metabolic diseases and death in individuals identified for risk-guided atrial fibrillation screening: a nationwide cohort study.

OBJECTIVE: Risk-guided atrial fibrillation (AF) screening may be an opportunity to prevent adverse events in addition to stroke. We compared events rates for new diagnoses of cardio-renal-metabolic diseases and death in individuals identified at higher versus lower-predicted AF risk. METHODS: From the UK Clinical Practice Research Datalink-GOLD dataset, 2 January 1998-30 November 2018, we identified individuals aged ≥30 years without known AF. The risk of AF was estimated using the FIND-AF (Future Innovations in Novel Detection of Atrial Fibrillation) risk score. We calculated cumulative incidence rates and fit Fine and Gray's models at 1, 5 and 10 years for nine diseases and death adjusting for competing risks. RESULTS: Of 416 228 individuals in the cohort, 82 942 were identified as higher risk for AF. Higher-predicted risk, compared with lower-predicted risk, was associated with incident chronic kidney disease (cumulative incidence per 1000 persons at 10 years 245.2; HR 6.85, 95% CI 6.70 to 7.00; median time to event 5.44 years), heart failure (124.7; 12.54, 12.08 to 13.01; 4.06), diabetes mellitus (123.3; 2.05, 2.00 to 2.10; 3.45), stroke/transient ischaemic attack (118.9; 8.07, 7.80 to 8.34; 4.27), myocardial infarction (69.6; 5.02, 4.82 to 5.22; 4.32), peripheral vascular disease (44.6; 6.62, 6.28 to 6.98; 4.28), valvular heart disease (37.8; 6.49, 6.14 to 6.85; 4.54), aortic stenosis (18.7; 9.98, 9.16 to 10.87; 4.41) and death from any cause (273.9; 10.45, 10.23 to 10.68; 4.75). The higher-risk group constituted 74% of deaths from cardiovascular or cerebrovascular causes (8582 of 11 676). CONCLUSIONS: Individuals identified for risk-guided AF screening are at risk of new diseases across the cardio-renal-metabolic spectrum and death, and may benefit from interventions beyond ECG monitoring.

Cohort profile: Evaluation of the Methods and Management of Acute Coronary Events (EMMACE) Longitudinal Cohort.

AIMS: The Evaluation of the Methods and Management of Acute Coronary Events (EMMACE) longitudinal cohort study aims to investigate health trajectories of individuals following hospitalization for myocardial infarction (MI). METHODS AND RESULTS: EMMACE is a linked multicentre prospective cohort study of 14 899 patients with MI admitted to 77 hospitals in England who participated in the EMMACE-3 and -4 studies between 1st November 2011 and 24th June 2015. Long-term follow-up of the EMMACE cohorts was conducted through the EMMACE-XL (27th September 2020 to 31st March 2022) and EMMACE-XXL (1st July 2021 to 1st July 2023) studies. EMMACE collected individual participant data for health-related quality of life (HRQoL) measured by three-level EuroQol five-dimension and visual analogy scale at admission, 1 month, 6 months, 12 months, and 10 years follow-up, as well as medications, medication adherence, beliefs about medicines, Satisfaction with Information about Medicines Scale, and illness perceptions. Participant data were deterministically linked to the Myocardial Infarction National Audit Project (MINAP) for information on baseline treatments and comorbidities, Hospital Episode Statistics Admitted Patient Care (for cause-specific hospitalization data), and the Office for National Statistics (for mortality data) up to 2020. CONCLUSION: EMMACE is a nationwide prospective cohort that will provide unique insights into fatal and non-fatal outcomes, medication adherence, and HRQoL following MI.Trial registration: ClinicalTrials.gov NCT01808027 and NCT01819103.

Process modelling of NHS cardiovascular waiting lists in response to the COVID-19 pandemic.

OBJECTIVE: To model the referral, diagnostic and treatment pathway for cardiovascular disease (CVD) in the English National Health Service (NHS) to provide commissioners and managers with a methodology to optimise patient flow and reduce waiting lists. STUDY DESIGN: A systems dynamics approach modelling the CVD healthcare system in England. The model is designed to capture current and predict future states of waiting lists. SETTING: Routinely collected, publicly available data streams of primary and secondary care, sourced from NHS Digital, NHS England, the Office of National Statistics and StatsWales. DATA COLLECTION AND EXTRACTION METHODS: The data used to train and validate the model were routinely collected and publicly available data. It was extracted and implemented in the model using the PySD package in python. RESULTS: NHS cardiovascular waiting lists in England have increased by over 40% compared with pre- COVID-19 levels. The rise in waiting lists was primarily due to restrictions in referrals from primary care, creating a bottleneck postpandemic. Predictive models show increasing point capacities within the system may paradoxically worsen downstream flow. While there is no simple rate-limiting step, the intervention that would most improve patient flow would be to increase consultant outpatient appointments. CONCLUSIONS: The increase in NHS CVD waiting lists in England can be captured using a systems dynamics approach, as can the future state of waiting lists in the presence of further shocks/interventions. It is important for those planning services to use such a systems-oriented approach because the feed-forward and feedback nature of patient flow through referral, diagnostics and treatment leads to counterintuitive effects of interventions designed to reduce waiting lists.

Prediction models for heart failure in the community: A systematic review and meta-analysis.

AIMS: Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. METHODS AND RESULTS: From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS: Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

Comparison of care and outcomes for myocardial infarction by heart failure status between United Kingdom and Japan.

AIMS: Prognosis for ST-segment elevation myocardial infarction (STEMI) is worse when heart failure is present on admission. Understanding clinical practice in different health systems can identify areas for quality improvement initiatives to improve outcomes. In the absence of international comparison studies, we aimed to compare treatments and in-hospital outcomes of patients admitted with ST elevation myocardial infarction (STEMI) by heart failure status in two healthcare-wide cohorts. METHODS AND RESULTS: We used two nationwide databases to capture admissions with STEMI in the United Kingdom (Myocardial ischemia National Audit Project, MINAP) and Japan (Japanese Registry of All Cardiac and Vascular Diseases-Diagnostic Procedure Combination, JROAD-DPC) between 2012 and 2017. Participants were stratified using the HF Killip classification into three groups; Killip 1: no congestive heart failure, Killip 2-3: congestive heart failure, Killip 4: cardiogenic shock. We calculated crude rate and case mix standardized risk ratios (CSRR) for use of treatments and in-hospital death. Patients were younger in the United Kingdom (65.4 [13.6] vs. 69.1 [13.0] years) and more likely to have co-morbidities in the United Kingdom except for diabetes and hypertension. Japan had a higher percentage of heart failure and cardiogenic shock patients among STEMI during admission than that in the United Kingdom. Primary percutaneous coronary intervention (pPCI) rates were lower in the United Kingdom compared with Japan, especially for patients presenting with Killip 2-3 class heart failure (pPCI use in patients with Killip 1, 2-3, 4: Japan, 86.2%, 81.7%, 78.7%; United Kingdom, 79.6%, 58.2% and 79.9%). In contrast, beta-blocker use was consistently lower in Japan than in the United Kingdom (61.4% vs. 90.2%) across Killip classifications and length of hospital stay longer (17.0 [9.7] vs. 5.0 [7.4] days). The crude rate of in-hospital mortality increased with increasing Killip class group. Both the crude rate and CSRR was higher in the United Kingdom compared with Japan for Killip 2-3 (15.8% vs. 6.4%, CSRR 1.80 95% CI 1.73-1.87, P < 0.001), and similar for Killip 4 (36.9% vs. 36.3%, CSRR 1.11 95% CI 1.08-1.13, P < 0.001). CONCLUSIONS: Important differences in the care and outcomes for STEMI with heart failure exist between the United Kingdom and Japan. Specifically, in the United Kingdom, there was a lower rate of pPCI, and in Japan, fewer patients were prescribed beta blockers and hospital length of stay was longer. This international comparison can inform targeted quality improvement programmes to narrow the outcome gap between health systems.

Presentation, care, and outcomes of patients with NSTEMI according to World Bank country income classification: the ACVC-EAPCI EORP NSTEMI Registry of the European Society of Cardiology.

BACKGROUND: The majority of NSTEMI burden resides outside high-income countries (HICs). We describe presentation, care, and outcomes of NSTEMI by country income classification. METHODS AND RESULTS: Prospective cohort study including 2947 patients with NSTEMI from 287 centres in 59 countries, stratified by World Bank country income classification. Quality of care was evaluated based on 12 guideline-recommended care interventions. The all-or-none scoring composite performance measure was used to define receipt of optimal care. Outcomes included in-hospital acute heart failure, stroke/transient ischaemic attack, and death, and 30-day mortality. Patients admitted with NSTEMI in low to lower-middle-income countries (LLMICs), compared with patients in HICs, were younger, more commonly diabetic, and current smokers, but with a lower burden of other comorbidities, and 76.7% met very high risk criteria for an immediate invasive strategy. Invasive coronary angiography use increased with ascending income classification (LLMICs, 79.2%; upper middle income countries [UMICs], 83.7%; HICs, 91.0%), but overall care quality did not (≥80% of eligible interventions achieved: LLMICS, 64.8%; UMICs 69.6%; HICs 55.1%). Rates of acute heart failure (LLMICS, 21.3%; UMICs, 12.1%; HICs, 6.8%; P < 0.001), stroke/transient ischaemic attack (LLMICS: 2.5%; UMICs: 1.5%; HICs: 0.9%; P = 0.04), in-hospital mortality (LLMICS, 3.6%; UMICs: 2.8%; HICs: 1.0%; P < 0.001) and 30-day mortality (LLMICs, 4.9%; UMICs, 3.9%; HICs, 1.5%; P < 0.001) exhibited an inverse economic gradient. CONCLUSION: Patients with NSTEMI in LLMICs present with fewer comorbidities but a more advanced stage of acute disease, and have worse outcomes compared with HICs. A cardiovascular health narrative is needed to address this inequity across economic boundaries.

Prediction of short-term atrial fibrillation risk using primary care electronic health records.

OBJECTIVE: Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)). METHODS: We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C2HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. RESULTS: Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA2DS2-VASc (0.784, 0.773 to 0.794) and C2HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). CONCLUSIONS: FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.