Risk engine score for cad6/3/2023 ![]() ![]() 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 Measuresĭiscrimination 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. I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.Coronary artery disease (CAD) risk prediction tools are useful decision supports. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as. This project was approved by the IRB of the Quebec Heart and Lung Institute.Īll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. The details of the IRB/oversight body that provided approval or exemption for the research described are given below:Īnalyses performed with the UK Biobank dataset were conducted under UK Biobank data application number 25205. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. This work was supported by a grant from the Canadian Institutes of Health Research (PJT-162344) to ST. PM holds a FRQS Research Chair on the Pathobiology of Calcific Aortic Valve Disease. YB holds a Canada Research Chair in Genomics of Heart and Lung Diseases. Funding StatementīJA and ST hold junior scholar awards from the FRQS. PM is a consultant for Casebia Therapeutics. Competing Interest StatementīJA is a consultant for Novartis and Silence Therapeutics and has received research funding from Pfizer and Ionis Pharmaceuticals. PRS could optimize the identification and management of individuals at risk for CAD. ![]() Conclusion Our PRS CAD predicts MI incidence and all-cause mortality, especially in men aged between 40-51 years. ![]()
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