Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study

Aims 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. 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 results The 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. 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. Lay Summary It is known that patients with chest pain without a recognized cause are at increased risk of future cardiovascular events (for example, heart disease) and so this study aimed to find out whether those patients at greatest risk could be determined using information in their health records.It is possible to accurately estimate a person's risk of future cardiovascular events using the information entered into their health records, and this risk can be estimated using only a small number of factors.Patients at highest risk could now be targeted for management to help prevent future cardiovascular events.

Automated summary

This study aimed to determine if patients at highest risk of cardiovascular disease can be identified using information in their health records and if their risk can be accurately estimated using only a small number of factors. The results showed that diabetes, atrial fibrillation, and hypertension were the strongest risk factors for cardiovascular disease in patients with unattributed chest pain. It was found that existing risk prediction models or the development of new models can accurately estimate individual cardiovascular disease risk.