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Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health
Kelvin P Jordan1, Trishna Rathod-Mistry1,2, Danielle A van der Windt1
1School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire ST5 5BG, UK.
Insights
Patients with unattributed chest pain face higher cardiovascular risks. A new model using primary care data accurately identifies high-risk individuals for targeted prevention, outperforming existing general population models.
Area of Science:
- Cardiology
- Primary Care Medicine
- Health Informatics
Background:
- Unattributed chest pain in primary care is common, with patients at elevated cardiovascular disease (CVD) risk.
- Existing risk prediction models may not adequately identify high-risk individuals within this specific population.
Purpose of the Study:
- To identify cardiovascular risk factors in patients with unattributed chest pain.
- To evaluate the efficacy of existing general population risk models versus a newly developed model for CVD risk prediction in this cohort.
Main Methods:
- Utilized UK primary care electronic health records (2002-2018) from the Clinical Practice Research Datalink.
- Developed and externally validated cardiovascular risk prediction models, comparing them against QRISK3.
- Analyzed data from 374,917 patients with unattributed chest pain.
Main Results:
- Key risk factors identified: diabetes, atrial fibrillation, hypertension, male sex, Asian ethnicity, deprivation, obesity, and smoking.
- The developed model demonstrated strong predictive performance (c-statistic 0.81).
- A simplified model with key factors showed comparable performance; QRISK3 underestimated risk.
Conclusions:
- Patients with unattributed chest pain have increased CVD event risk.
- Individual risk can be accurately estimated using routine primary care data and key risk factors.
- Targeted preventative strategies can be implemented for high-risk individuals.
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.
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