Estimating the incidence and key risk factors of cardiovascular disease in patients at high risk of imminent fracture using routinely collected real-world data from the UK.

The objective of this work was to estimate the incidence rate of cardiovascular disease (CVD) events (myocardial infarction, stroke or CVD-death) at 1 year among 3 cohorts of patients at high risk of fracture (osteoporosis; previous fracture; and anti-osteoporosis medication), and to identify the key risk factors of CVD events in these three cohorts. To do so, this prospective cohort study used data from the Clinical Practice Research Datalink, a primary care database from United Kingdom. Major Adverse Cardiovascular Events (MACE: a composite outcome for the occurrence of either myocardial infarction (MI), stroke or CVD death) were identified in patients aged fifty or over at high or imminent fracture risk identified in three different cohorts (not mutually exclusive): recently diagnosed with osteoporosis (OST, n=65,295), incident fragility fracture (IFX, n=67,065), and starting oral bisphosphonates (OBP, n=145,959). About 1.90%, 4.39% and 2.38% of the participants in OST, IFX and OBP cohorts, respectively, experienced MACE events. IFX was the cohort with the higher risk: MACE incidence rates (cases/1000 person-years) were 19.63 (18.54;20.73) in OST, 52.64 (50.7,54.5) in IFX, and 26.26 (25.41;27.12) in OBP cohorts. Risk of MACE events at 1-year was predicted in the 3 cohorts. Models using a set of general, CVD, and fracture candidates selected by lasso regression had a good discrimination (≥70%) and internal validity, and generally outperformed the models using only the CVD risk factors of general population listed in QRISK tool. Main risk factors common in all MACE models were sex, age, smoking, alcohol, atrial fibrillation, anti-hypertensive medication, prior MI/stroke, established CVD, glomerular filtration rate, systolic blood pressure, cholesterol levels, and number of concomitant medicines. Identified key risk factors highlight the differences of patients at high risk of fracture versus general population. Proposed models could improve prediction of CVD events in patients with osteoporosis in primary care settings. This article is protected by copyright. All rights reserved.