ArticleEuropean journal of preventive cardiology2023
Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study.
Article in European journal of preventive cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Risk prediction for individual patients and the pitfalls of selecting an optimal prediction model: do not judge a model by its c-statistic.European journal of epidemiology · 2026Article
- Identification of key cardiovascular disease predictive factors from the China Health and Retirement Longitudinal Study dataset using machine learning-based algorithms.Journal of translational medicine · 2026Article
- Characteristics and prognosis in acute myocarditis and unexplained acute chest pain: a nationwide longitudinal cohort study.Open heart · 2025Article
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Authors and funding
11 authors at 4 institutions in 2 countries.
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Abstract
aimsMost adults presenting in primary care with chest pain symptoms will not receive a diagnosis ('unattributed' chest pain) but are at increased risk of cardiovascular events. To assess within patients with unattributed chest pain, risk factors for cardiovascular events and whether those at greatest risk of cardiovascular disease can be ascertained by an existing general population risk prediction model or by development of a new model. METHODS AND
resultsThe study used UK primary care electronic health records from the Clinical Practice Research Datalink linked to admitted hospitalizations. Study population was patients aged 18 plus with recorded unattributed chest pain 2002-2018. Cardiovascular risk prediction models were developed with external validation and comparison of performance to QRISK3, a general population risk prediction model. There were 374 917 patients with unattributed chest pain in the development data set. The strongest risk factors for cardiovascular disease included diabetes, atrial fibrillation, and hypertension. Risk was increased in males, patients of Asian ethnicity, those in more deprived areas, obese patients, and smokers. The final developed model had good predictive performance (external validation c-statistic 0.81, calibration slope 1.02). A model using a subset of key risk factors for cardiovascular disease gave nearly identical performance. QRISK3 underestimated cardiovascular risk.
conclusionPatients presenting with unattributed chest pain are at increased risk of cardiovascular events. It is feasible to accurately estimate individual risk using routinely recorded information in the primary care record, focusing on a small number of risk factors. Patients at highest risk could be targeted for preventative measures.
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