Risk factors for incident heart failure in age‐ and sex‐specific strata: a population‐based cohort using linked electronic health records

Abstract

Aims Several risk factors for incident heart failure (HF) have been previously identified, however large electronic health records (EHR) datasets may provide the opportunity to examine the consistency of risk factors across different subgroups from the general population. Methods and results We used linked EHR data from 2000 to 2010 as part of the UK‐based CALIBER resource to select a cohort of 871 687 individuals 55 years or older and free of HF at baseline. The primary endpoint was the first record of HF from primary or secondary care. Cox proportional hazards analysis was used to estimate hazard ratios for associations between risk factors and incident HF, separately for men and women and by age category: 55–64, 65–74, and > 75 years. During 5.8 years of median follow‐up, a total of 47 987 incident HF cases were recorded. Age, social deprivation, smoking, sedentary lifestyle, diabetes, atrial fibrillation, chronic obstructive pulmonary disease, body mass index, haemoglobin, total white blood cell count and creatinine were associated with HF. Smoking, atrial fibrillation and diabetes showed stronger associations with incident HF in women compared to men. Conclusion We confirmed associations of several risk factors with HF in this large population‐based cohort across age and sex subgroups. Mainly modifiable risk factors and comorbidities are strongly associated with incident HF, highlighting the importance of preventive strategies targeting such risk factors for HF.

Analyzing the heterogeneity of rule-based EHR phenotyping algorithms in CALIBER and the UK Biobank

“…The majority of phenotyping algorithms were created to ascertain disease status (n=54) (e.g. heart failure [Gho et al 2018;Uijl et al 2019], depression [Daskalopoulou et al 2016]), ten algorithms were created to extract information on biomarkers (e.g. heart rate [Archangelidi et al 2018], blood pressure [Rapsomaniki et al 2014]) and six algorithms were used to identify lifestyle risk factors (e.g.…”

“…However, primary care information for just under half of the cohort (n=230,000) will be made available for UK Biobank researchers in June 2019. Algorithms incorporating primary care data for the conditions already covered have been or are being developed [Wilkinson et al 2019]. Along with a range of additional algorithms expanding the range of health outcomes available, they will be available from UK Biobank later in 2019.…”

“…Prognostic 86% (n=66) and cross-source concordance 54% (n=43) validation approaches where the most widely-used algorithm evaluation approaches. The least-widely used validation approach was expert case note review, although this type of validation has been completed for a few UK Biobank algorithms, including dementia and its subtypes [Wilkinson et al, 2019], and is underway for several others. Most (93% [n=66]) of the algorithms used data stored in tabular format since tables are predominantly used to store lists of controlled clinical terminology terms.…”

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

“…Although the indirect effects of family history of CVD, age, lipid pro les, anthropometric indices, healthy lifestyle, quality of life, high-risk behaviours, having hypertension and high blood sugar on CVDs were not covered in the literature, many studies discussed the direct effect of such factors in which these results are in line with (9,11,12,15,(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Causal networks evaluate causal relationships among variables beyond partial correlations and thus play a fundamental step in risk prediction.…”

October 2019 at a glance: epidemiology, prevention, and modes of death

“…Cardiovascular (CV) risk factors and co-morbidities are major determinants of HF phenotypes. 3 Uijl et al 4 reported the association between them and incident HF in a large cohort of 871 687 subjects stratified by gender and age. Modifiable risk factors (social deprivation, smoking, sedentary lifestyle, body mass index) and co-morbidities (diabetes, AF, chronic obstructive pulmonary disease, renal insufficiency) were associated with incident HF.…”