Association between cardiometabolic disease multimorbidity and all-cause mortality in 2 million women and men registered in UK general practices.

Background: Myocardial infarction (MI), stroke and diabetes share underlying risk factors and commonalities in clinical management. We examined if their combined impact on mortality is proportional, amplified or less than the expected risk separately of each disease and whether the excess risk is explained by their associated comorbidities.

Methods: Using large-scale electronic health records, we identified 2,007,731 eligible patients (51% women) and registered with general practices in the UK and extracted clinical information including diagnosis of myocardial infarction (MI), stroke, diabetes and 53 other long-term conditions before 2005 (study baseline).

Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation.

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population - both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations.

Prenatal, Early-Life, and Childhood Exposure to Air Pollution and Lung Function: The ALSPAC Cohort.

: Exposure to air pollution during intrauterine development and through childhood may have lasting effects on respiratory health.: To investigate lung function at ages 8 and 15 years in relation to air pollution exposures during pregnancy, infancy, and childhood in a UK population-based birth cohort.: Individual exposures to source-specific particulate matter ≤10 μm in aerodynamic diameter (PM) during each trimester, 0-6 months, 7-12 months (1990-1993), and up to age 15 years (1991-2008) were examined in relation to FEV% predicted and FVC% predicted at ages 8 ( = 5,276) and 15 ( = 3,446) years using linear regression models adjusted for potential confounders.

Deep learning for electronic health records: A comparative review of multiple deep neural architectures.

Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories.

Long-Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large-Scale Routine Electronic Health Records.

Background How measures of long-term exposure to elevated blood pressure might add to the performance of "current" blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressure alone or in combination. Methods and Results Using data from UK primary care linked electronic health records, we applied a landmark cohort study design and identified 80 964 people, aged 50 years (derivation cohort=64 772; validation cohort=16 192), who, at study entry, had recorded blood pressure, no prior cardiovascular disease, and no previous antihypertensive or lipid-lowering prescriptions.