BACKGROUND: Most 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.
AIM: 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: The study used UK primary care electronic health records from the Clinical Practice Research Datalink (CPRD) linked to admitted hospitalisations. 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.
RESULTS: There were 374,917 patients with unattributed chest pain in the development dataset. 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.
CONCLUSION: Patients 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.
|ジャーナル名||European journal of preventive cardiology|
|投稿者||Jordan, Kelvin P; Rathod-Mistry, Trishna; van der Windt, Danielle A; Bailey, James; Chen, Ying; Clarson, Lorna; Denaxas, Spiros; Hayward, Richard A; Hemingway, Harry; Kyriacou, Theocharis; Mamas, Mamas A|
|組織名||School of Medicine, Keele University, Keele, UK.;Centre for Statistics in Medicine, Nuffield Department of Orthopaedics,;Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.;Department of Health and Environmental Sciences, Xi'an Jiaotong - Liverpool;University, China.;Institute of Health Informatics, University College London, London, UK; Health;Data Research UK, University College London, London, UK.;The National Institute for Health Research University College London Hospitals;Biomedical Research Centre.;School of Computing and Mathematics, Keele University, Keele, UK.;Keele Cardiovascular Research Group, School of Medicine, Keele University, Keele,;UK.|