Diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome in the emergency department setting: a systematic review.
ABSTRACT We sought to determine the diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome (ACS) in the emergency department (ED) setting.
We searched MEDLINE, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews. We contacted content experts to identify additional articles for review. Reference lists of included studies were hand searched. We selected articles for review based on the following criteria: 1) enrolled consecutive ED patients; 2) incorporated variables from the history or physical examination, electrocardiogram and cardiac biomarkers; 3) did not incorporate cardiac stress testing or coronary angiography into prediction rule; 4) based on original research; 5) prospectively derived or validated; 6) did not require use of a computer; and 7) reported sufficient data to construct a 2 x 2 contingency table. We assessed study quality and extracted data independently and in duplicate using a standardized data extraction form.
Eight studies met inclusion criteria, encompassing 7937 patients. None of the studies verified the prediction rule with a reference standard on all or a random sample of patients. Six studies did not report blinding prediction rule assessors to reference standard results, and vice versa. Three prediction rules were prospectively validated. Sensitivities and specificities ranged from 94% to 100% and 13% to 57%, and positive and negative likelihood ratios from 1.1 to 2.2 and 0.01 to 0.17, respectively.
Current prediction rules for ACS have substantial methodological limitations and have not been successfully implemented in the clinical setting. Future methodologically sound studies are needed to guide clinical practice.
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ABSTRACT: BACKGROUND:: Studies have individually reported the relationship of age, cardiac risk factors, and history of preexisting coronary artery disease (CAD) for predicting acute coronary syndromes in chest pain patients undergoing cardiac stress testing. In this study, we investigate the interplay of all these factors on the incidence of acute coronary syndromes to develop a tool that may assist physicians in the selection of appropriate chest pain patients for stress testing. METHODS:: Retrospective analysis of a prospectively acquired database of consecutive chest pain patients undergoing nuclear stress testing. Backward stepwise logistic regression was used to develop a model for predicting risk of 30-day acute coronary events (ACE) using information obtained from age, sex, cardiac risk factors, and history of preexisting CAD. RESULTS:: A total of 800 chest pain patients underwent nuclear stress testing. ACE occurred in 74 patients (9.3%). Logistic regression analysis found only 6 factors predictive of ACE: age, male sex, preexisting CAD, diabetes, and hyperlipidemia. Area under the receiver operator characteristic curve of this model for predicting ACE was 0.767 (95% confidence interval, 0.719-0.815). There were no cases of ACE in the 173 patients with predicted probability estimates ≤2.5% (95% confidence interval, 0%-2.1%). CONCLUSIONS:: A regression model using age, sex, preexisting CAD, diabetes, and hyperlipidemia is predictive of 30-day ACE in chest pain patients undergoing nuclear stress testing. Prospective studies need to be performed to determine whether this model can assist physicians in the selection of appropriate low-to-intermediate risk chest pain patients for nuclear stress testing.Critical pathways in cardiology 12/2012; 11(4):171-176. DOI:10.1097/HPC.0b013e31826f367f
Article: Curriculum Vitae and Bibliography
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ABSTRACT: Considerable time and resources are consumed investigating the many patients who present to emergency departments but who do not have an acute coronary syndrome. Consequently, researchers are increasingly modifying or developing cardiovascular risk-scoring systems for rule-out of acute coronary syndrome in patients who present to emergency departments with chest pain. Scoring systems range from those developed from statistical weighting of variables collected in observational studies to those developed from clinical judgment, logic, and common sense. The trend is towards systems using clinical logic and simple criteria. Although sensitivity and negative predictive value are the key parameters of interest when deciding to use a risk-assessment score to assist with chest-pain rule-out decision-making, it is important to consider the proportion of patients that will be classified as low risk. If the numbers are significantly low, then adopting a risk-scoring system in an early rule-out strategy is unlikely to impact upon patient flow and emergency-department overcrowding.03/2013; 1(1). DOI:10.1007/s40138-012-0004-0