Muralidhar R Papireddy1, Carl J Lavie2, Abhizith Deoker3, Hadii Mamudu4, Timir K Paul5. 1. Division of Cardiology, Department of Internal Medicine, Quillen College of Medicine, East Tennessee State University, 329 N State of Franklin Rd, Johnson City, TN, 37604, USA. 2. Department of Cardiology, Ochsner Clinical School, The University of Queensland School of Medicine, New Orleans, LA, USA. 3. Division of Cardiology, Department of Internal Medicine, Texas Tech University, El Paso, TX, USA. 4. Department of Health Services Management and Policy, College of Public Health, East Tennessee State University, Johnson City, TN, USA. 5. Division of Cardiology, Department of Internal Medicine, Quillen College of Medicine, East Tennessee State University, 329 N State of Franklin Rd, Johnson City, TN, 37604, USA. pault@etsu.edu.
Abstract
PURPOSE OF REVIEW: To review the landmark studies in predicting obstructive coronary artery disease (CAD) in symptomatic patients with stable chest pain and identify better prediction tools and propose a simplified algorithm to guide the health care providers in identifying low risk patients to defer further testing. RECENT FINDINGS: There are a few risk prediction models described for stable chest pain patients including Diamond-Forrester (DF), Duke Clinical Score (DCS), CAD Consortium Basic, Clinical, and Extended models. The CAD Consortium models demonstrated that DF and DCS models overestimate the probability of CAD. All CAD Consortium models performed well in the contemporary population. PROMISE trial secondary data results showed that a clinical tool using readily available ten very low-risk pre-test variables could discriminate low-risk patients to defer further testing safely. In the contemporary population, CAD Consortium Basic or Clinical model could be used with more confidence. Our proposed simple algorithm would guide the physicians in selecting low risk patients who can be managed conservatively with deferred testing strategy. Future research is needed to validate our proposed algorithm to identify the low-risk patients with stable chest pain for whom further testing may not be warranted.
PURPOSE OF REVIEW: To review the landmark studies in predicting obstructive coronary artery disease (CAD) in symptomatic patients with stable chest pain and identify better prediction tools and propose a simplified algorithm to guide the health care providers in identifying low risk patients to defer further testing. RECENT FINDINGS: There are a few risk prediction models described for stable chest painpatients including Diamond-Forrester (DF), Duke Clinical Score (DCS), CAD Consortium Basic, Clinical, and Extended models. The CAD Consortium models demonstrated that DF and DCS models overestimate the probability of CAD. All CAD Consortium models performed well in the contemporary population. PROMISE trial secondary data results showed that a clinical tool using readily available ten very low-risk pre-test variables could discriminate low-risk patients to defer further testing safely. In the contemporary population, CAD Consortium Basic or Clinical model could be used with more confidence. Our proposed simple algorithm would guide the physicians in selecting low risk patients who can be managed conservatively with deferred testing strategy. Future research is needed to validate our proposed algorithm to identify the low-risk patients with stable chest pain for whom further testing may not be warranted.
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