The patient and his attending doctors (primary care physician and cardiologist) were blinded to the CAD pre-test probability (PTP) results, computed using the various models tested. Patients without investigations in the preceding year had fasting blood tests taken upon enrolment to determine their lipid and glucose levels. Electronic medical records (EMR) were accessed to determine clinical history and laboratory test results. Patients completed an interviewer-administered questionnaire and underwent resting electrocardiogram (ECG). All participants provided informed consent. Ethical approval was obtained from SingHealth Centralised Institutional Review Board (CIRB2018/2851). Those with (a) existing or prior history of CAD, (b) acute coronary syndrome such as unstable angina and evolving acute myocardial infarction, and (c) age below 30 years were excluded. They were stable clinically and were subsequently referred for cardiac evaluation at NHCS between July 2013 and December 2016. ParticipantsĪ prospective cohort study was conducted on consecutive patients who attended all SHP branch clinics for chest pain. Its affiliate, the National Heart Centre Singapore (NHCS), is the largest local tertiary referral center for cardiovascular (CV) diseases. Its network of eight polyclinics covered 1.8 million attendances in 2018 7. SingHealth Polyclinics (SHP) is a provider of subsidized primary healthcare services. Singapore is an urbanized island-state in Southeast Asia with a multi-ethnic population of 5.7 million 6. We report our study in accordance with the TRIPOD statement 5. It is also not known which tool is best calibrated for use in an Asian population. To date, the clinical implications of using these risk scores have not been compared in a primary care setting. The Duke Clinical Score 2 (DCS), CAD Consortium Score 3 (CCS), and Marburg Heart Score 4 (MHS) are commonly used prediction models for CAD diagnosis. However, conventional methods used to evaluate and compare various prediction models, namely the discrimination and calibration statistics, are not intuitive enough to aid decision-making in routine clinical practice. When reaching a shared decision to refer a patient for further cardiac investigations, one should take into account individual risk appetite and also consider the trade-offs, namely between correctly diagnosing disease versus unnecessary added tests in the otherwise healthy. The pre-test probability of CAD reflects a continuum of risk. Such decision support is particularly useful at a clinical setting where the actual disease prevalence is low, such as at the primary healthcare setting 1. Risk prediction tools aid physicians to objectively evaluate the probability of coronary artery disease (CAD) among patients presenting with chest pain. PRECISE (Predictive Risk scorE for CAD In Southeast Asians with chEst pain) performs well and demonstrates utility as a clinical decision support for diagnosing CAD among Southeast Asians. The net benefit for DCS, CCS-basic, and CCS-clinical was 0.056, 0.060, and 0.065. At 5% threshold probability, the net benefit for our model (with ECG) was 0.063. Our model (with ECG) correctly reclassified 100% of patients when compared with DCS and CCS-clinical respectively. Our model included age, gender, type 2 diabetes mellitus, hypertension, smoking, chest pain type, neck radiation, Q waves, and ST-T changes. Key ResultsĬAD prevalence was 9.5% (158 of 1658 patients). Main MeasuresÄiscrimination and calibration quantify model performance, while net reclassification improvement and net benefit provide clinical insights. We validated the Duke Clinical Score (DCS), CAD Consortium Score (CCS), and Marburg Heart Score (MHS). A logistic regression model was built, with validation by resampling. CAD was diagnosed at tertiary institution and adjudicated. We prospectively recruited patients presenting to primary care for chest pain between July 2013 and December 2016. We aimed to develop and validate a diagnostic prediction model for CAD in Southeast Asians by comparing it against three existing tools. Their clinical impact has not been evaluated amongst Asians in primary care. Coronary artery disease (CAD) risk prediction tools are useful decision supports.
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