Studies Show Computerized Decision Tools Can Aid Acute Coronary Syndrome, Pulmonary Embolism Diagnoses
New AHRQ-funded research finds that computerized diagnostic testing can help clinicians assess whether their patients are suffering serious, acute cardiovascular events such as heart attacks. Traditionally, pretest probability assessment—in which clinicians use their experience to discern whether a patient is in danger—has played a central role in diagnosis. This clinical judgment, or “doctor’s best guess,” can help reduce unnecessary and dangerous testing. However, pretest probability assessment is imperfect for ruling out acute coronary syndrome [ACS] (which includes heart attack and unstable angina) and pulmonary embolism [PE] (a sudden blockage in the lung artery). One AHRQ-funded paper, published in Annals of Emergency Medicine, found that clinicians routinely overestimated pretest probability of both ACS and PE compared with computerized pretest methods. A second paper, based on the same study and also published in Annals of Emergency Medicine, found that patients at very low risk of ACS or PE (less than 2.5 percent) may be able to skip imaging (which is often used to test for ACS and PE) and reduce their exposure to radiation. A third paper, published in Circulation: Cardiovascular Imaging, found that computerized pretest probability screening reduces dangerous and expensive testing (including the risk of radiation exposure) in low-risk ambulatory patients with symptoms of ACS and PE. This demonstrates direct benefit of an electronic decision support to aid in diagnosis. All three papers were based on AHRQ-funded research led by Jeffrey A. Kline, M.D., from the Department of Emergency Medicine, Department of Cellular and Integrative Physiology, Indiana University School of Medicine.
Ann Emerg Med. 2014 Mar;63(3):275-80. doi: 10.1016/j.annemergmed.2013.08.023. Epub 2013 Sep 23.
Clinician gestalt estimate of pretest probability for acute coronary syndrome and pulmonary embolism in patientswith chest pain and dyspnea.
Pretest probability helps guide diagnostic testing for patients with suspected acute coronary syndrome and pulmonary embolism. Pretest probability derived from the clinician's unstructured gestalt estimate is easier and more readily available than methods that require computation. We compare the diagnostic accuracy of physician gestalt estimate for the pretest probability of acute coronary syndrome andpulmonary embolism with a validated, computerized method.
This was a secondary analysis of a prospectively collected, multicenter study. Patients (N=840) had chest pain, dyspnea, nondiagnostic ECGs, and no obvious diagnosis. Clinician gestalt pretest probability for both acute coronary syndrome and pulmonary embolism was assessed by visual analog scale and from the method of attribute matching using a Web-based computer program. Patients were followed for outcomes at 90 days.
Clinicians had significantly higher estimates than attribute matching for both acute coronary syndrome (17% versus 4%; P<.001, paired t test) and pulmonary embolism (12% versus 6%; P<.001). The 2 methods had poor correlation for both acute coronary syndrome (r(2)=0.15) andpulmonary embolism (r(2)=0.06). Areas under the receiver operating characteristic curve were lower for clinician estimate compared with the computerized method for acute coronary syndrome: 0.64 (95% confidence interval [CI] 0.51 to 0.77) for clinician gestalt versus 0.78 (95% CI 0.71 to 0.85) for attribute matching. For pulmonary embolism, these values were 0.81 (95% CI 0.79 to 0.92) for clinician gestalt and 0.84 (95% CI 0.76 to 0.93) for attribute matching.
Compared with a validated machine-based method, clinicians consistently overestimated pretest probability but on receiver operating curve analysis were as accurate for pulmonary embolism but not acute coronary syndrome.
Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.
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