Clinical Likelihood Prediction of Hemodynamically Obstructive Coronary Artery Disease in Patients With Stable Chest Pain - JIMG October 2024-Article: Clinical Likelihood Prediction of Hemodynamically Obstructive Coronary Artery Disease in Patients With Stable Chest Pain

Article: Clinical Likelihood Prediction of Hemodynamically Obstructive Coronary Artery Disease in Patients With Stable Chest Pain

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The study aims to validate the risk factor–weighted clinical likelihood (RF-CL) and coronary artery calcium score–weighted clinical likelihood (CACS-CL) models for predicting hemodynamically obstructive coronary artery disease (CAD) against a reference standard of invasive fractional flow reserve (FFR) in patients with stable chest pain. This is important as current guidelines recommend revascularization based on physiological rather than anatomical measures, and traditional pretest probability (PTP) models do not incorporate physiological data.<br /><br />A study conducted on 4,371 patients, who underwent coronary computed tomography angiography and invasive coronary angiography with FFR measurements, found that 10.9% of patients had hemodynamically obstructive CAD. The RF-CL and CACS-CL models classified more patients as having a very low likelihood (≤5%) of obstructive CAD compared to the basic PTP model, while maintaining a low prevalence of hemodynamically obstructive CAD. The RF-CL and CACS-CL models showed better calibration and discrimination against hemodynamically obstructive CAD compared to the basic PTP model.<br /><br />The study concludes that RF-CL and CACS-CL models are well-calibrated and superior to basic PTP models in predicting hemodynamically obstructive CAD. Utilizing these models can better guide patient management, potentially avoiding unnecessary invasive procedures. The study reinforces the preference for RF-CL and CACS-CL models in the diagnostic work-up of patients with new-onset chest pain, improving patient safety and treatment outcomes by accurately assessing the need for further testing and intervention. Improved predictive models could enhance the cost-effectiveness and clinical decision-making process in managing coronary artery disease.

Keywords

risk factor-weighted clinical likelihood

coronary artery calcium score-weighted clinical likelihood

hemodynamically obstructive coronary artery disease

fractional flow reserve

stable chest pain

revascularization

pretest probability models

coronary computed tomography angiography

patient management

diagnostic work-up