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Predicting Coronary Artery Disease in Primary Care: Development and Validation of a Diagnostic Risk Score for Major
Zhen Sinead Wang1,2, Jonathan Yap3, Yi Ling Eileen Koh4
1SingHealth Polyclinics, Singapore, Singapore, Republic of Singapore. sinead.wang.zhen@singhealth.com.sg.
Insights
A new model, PRECISE, accurately predicts coronary artery disease (CAD) in Southeast Asians presenting with chest pain. This tool improves upon existing risk scores, offering better clinical decision support for primary care.
Area of Science:
- Cardiology
- Public Health
- Clinical Decision Support
Background:
- Coronary artery disease (CAD) risk prediction tools are crucial for primary care decision-making.
- The clinical utility of existing CAD risk tools has not been adequately evaluated in Asian populations.
Purpose of the Study:
- To develop and validate a novel diagnostic prediction model for CAD specifically in Southeast Asian patients.
- To compare the performance of the new model against established risk prediction tools.
Main Methods:
- Prospective recruitment of primary care patients presenting with chest pain (July 2013 - December 2016).
- Logistic regression model development and validation using resampling techniques.
- Comparison with Duke Clinical Score (DCS), CAD Consortium Score (CCS), and Marburg Heart Score (MHS).
Main Results:
- The developed model, PRECISE, demonstrated strong discrimination (C-statistic 0.815 with ECG).
- PRECISE showed superior performance compared to DCS, CCS, and MHS.
- The model achieved excellent reclassification of patients and provided significant net benefit.
Conclusions:
- The PRECISE model is a well-performing clinical decision support tool for diagnosing CAD in Southeast Asian primary care settings.
- PRECISE offers improved utility for risk stratification and management of chest pain patients in this demographic.
Background:
Coronary artery disease (CAD) risk prediction tools are useful decision supports. Their clinical impact has not been evaluated amongst Asians in primary care.
Objective:
We aimed to develop and validate a diagnostic prediction model for CAD in Southeast Asians by comparing it against three existing tools.
Design:
We 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 Measures:
Discrimination and calibration quantify model performance, while net reclassification improvement and net benefit provide clinical insights.
Key Results:
CAD 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.
Conclusions:
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.
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