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The benefits of using electronic health records (EHRs) for disease risk screening and personalized health-care decisions are being increasingly recognized. Here we present a computationally feasible statistical approach with which to address the methodological challenges involved in utilizing historical repeat measures of multiple risk factors recorded in EHRs to systematically identify patients at high risk of future disease. The approach is principally based on a 2-stage dynamic landmark model. The first stage estimates current risk factor values from all available historical repeat risk factor measurements via landmark-age-specific multivariate linear mixed-effects models with correlated random intercepts, which account for sporadically recorded repeat measures, unobserved data, and measurement errors. The second stage predicts future disease risk from a sex-stratified Cox proportional hazards model, with estimated current risk factor values from the first stage. We exemplify these methods by developing and validating a dynamic 10-year cardiovascular disease risk prediction model using primary-care EHRs for age, diabetes status, hypertension treatment, smoking status, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol in 41,373 persons from 10 primary-care practices in England and Wales contributing to The Health Improvement Network (1997-2016). Using cross-validation, the model was well-calibrated (Brier score = 0.041, 95% confidence interval: 0.039, 0.042) and had good discrimination (C-index = 0.768, 95% confidence interval: 0.759, 0.777).

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Objective: To provide quantitative evidence for systematically prioritising individuals for full formal cardiovascular disease (CVD) risk assessment using primary care records with a novel tool (eHEART) with age- and sex- specific risk thresholds. Methods and analysis: eHEART was derived using landmark Cox models for incident CVD with repeated measures of conventional CVD risk predictors in 1,642,498 individuals from the Clinical Practice Research Datalink. Using 119,137 individuals from UK Biobank, we modelled the implications of initiating guideline-recommended statin therapy using eHEART with age- and sex-specific prioritisation thresholds corresponding to 5% false negative rates to prioritise adults aged 40–69 years in a population in England for invitation to a formal CVD risk assessment. Results: Formal CVD risk assessment on all adults would identify 76% and 49% of future CVD events amongst men and women respectively, and 93 (95% CI: 90, 95) men and 279 (95% CI: 259, 297) women would need to be screened (NNS) to prevent one CVD event. In contrast, if eHEART was first used to prioritise individuals for formal CVD risk assessment, we would identify 73% and 47% of future events amongst men and women respectively, and a NNS of 75 (95% CI: 72, 77) men and 162 (95% CI: 150, 172) women. Replacing the age- and sex-specific prioritisation thresholds with a 10% threshold identify around 10% less events. Conclusions: The use of prioritisation tools with age- and sex-specific thresholds could lead to more efficient CVD assessment programmes with only small reductions in effectiveness at preventing new CVD events. [ABSTRACT FROM AUTHOR]

Summary: Cardiovascular disease (CVD) is a leading cause of preventable morbidity and mortality in Aboriginal and Torres Strait Islander peoples. This statement from the Australian Chronic Disease Prevention Alliance, the Royal Australian College of General Practitioners, the National Aboriginal Community Controlled Health Organisation and the Editorial Committee for Remote Primary Health Care Manuals communicates the latest consensus advice of guideline developers, aligning recommendations on the age to commence Aboriginal and Torres Strait Islander CVD risk assessment across three guidelines. Main recommendations: In Aboriginal and Torres Strait Islander peoples without existing CVD: CVD risk factor screening should commence from the age of 18 years at the latest, including for blood glucose level or glycated haemoglobin, estimated glomerular filtration rate, serum lipids, urine albumin to creatinine ratio, and other risk factors such as blood pressure, history of familial hypercholesterolaemia, and smoking status.Individuals aged 18–29 years with the following clinical conditions are automatically conferred high CVD risk: ▶type 2 diabetes and microalbuminuria;▶moderate to severe chronic kidney disease;▶systolic blood pressure ≥ 180 mmHg or diastolic blood pressure ≥ 110 mmHg;▶familial hypercholesterolaemia; or▶serum total cholesterol > 7.5 mmol/L.Assessment using the National Vascular Disease Prevention Alliance absolute CVD risk algorithm should commence from the age of 30 years at the latest — consider upward adjustment of calculated CVD risk score, accounting for local guideline use, risk factor and CVD epidemiology, and clinical discretion.Assessment should occur as part of an annual health check or opportunistically. Subsequent review should be conducted according to level of risk. Changes in management as a result of this statement: From age 18 years (at the latest), Aboriginal and Torres Strait Islander adults should undergo CVD risk factor screening, and from age 30 years (at the latest), they should undergo absolute CVD risk assessment using the NVDPA risk algorithm. [ABSTRACT FROM AUTHOR]