• J Gen Intern Med · Jun 2021

    Predicting Coronary Artery Disease in Primary Care: Development and Validation of a Diagnostic Risk Score for Major Ethnic Groups in Southeast Asia.

    • Zhen Sinead Wang, Jonathan Yap, Yi Ling Eileen Koh, Shaw Yang Chia, N Nivedita, Teck Wee Andrew Ang, Soo Chye Paul Goh, Cia Sin Lee, Lee Lim Joanna Tan, Chai Wah Ooi, Matthew Seow, Khung Keong Yeo, ChuaSiang Jin TerranceSJTDuke-NUS Medical School, Singapore, Republic of Singapore.National Heart Centre Singapore, Singapore, Republic of Singapore., and Ngiap Chuan Tan.
    • SingHealth Polyclinics, Singapore, Singapore, Republic of Singapore. sinead.wang.zhen@singhealth.com.sg.
    • J Gen Intern Med. 2021 Jun 1; 36 (6): 151415241514-1524.

    BackgroundCoronary artery disease (CAD) risk prediction tools are useful decision supports. Their clinical impact has not been evaluated amongst Asians in primary care.ObjectiveWe aimed to develop and validate a diagnostic prediction model for CAD in Southeast Asians by comparing it against three existing tools.DesignWe prospectively recruited patients presenting to primary care for chest pain between July 2013 and December 2016. CAD was diagnosed at tertiary institution and adjudicated. A logistic regression model was built, with validation by resampling. We validated the Duke Clinical Score (DCS), CAD Consortium Score (CCS), and Marburg Heart Score (MHS).Main MeasuresDiscrimination and calibration quantify model performance, while net reclassification improvement and net benefit provide clinical insights.Key ResultsCAD prevalence was 9.5% (158 of 1658 patients). Our model included age, gender, type 2 diabetes mellitus, hypertension, smoking, chest pain type, neck radiation, Q waves, and ST-T changes. The C-statistic was 0.808 (95% CI 0.776-0.840) and 0.815 (95% CI 0.782-0.847), for model without and with ECG respectively. C-statistics for DCS, CCS-basic, CCS-clinical, and MHS were 0.795 (95% CI 0.759-0.831), 0.756 (95% CI 0.717-0.794), 0.787 (95% CI 0.752-0.823), and 0.661 (95% CI 0.621-0.701). Our model (with ECG) correctly reclassified 100% of patients when compared with DCS and CCS-clinical respectively. At 5% threshold probability, the net benefit for our model (with ECG) was 0.063. The net benefit for DCS, CCS-basic, and CCS-clinical was 0.056, 0.060, and 0.065.ConclusionsPRECISE (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.

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