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.

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.

Kline JA1, Stubblefield WB2.

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Abstract

STUDY OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.

PMID:: 24070658; [PubMed - in process]

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2014 Mar;63(3):281-8. doi: 10.1016/j.annemergmed.2013.09.009. Epub 2013 Oct 10.

Outcomes and Radiation Exposure of Emergency Department Patients With Chest Pain and Shortness of Breath and Ultralow Pretest Probability: A Multicenter Study.

Kline JA1, Shapiro NI2, Jones AE3, Hernandez J4, Hogg MM4, Troyer J5, Nelson RD6.

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Abstract

STUDY OBJECTIVE:

Excessive radiation exposure remains a concern for patients with symptoms suggesting acute coronary syndrome and pulmonary embolism but must be judged in the perspective of pretest probability and outcomes. We quantify and qualify the pretest probability, outcomes, and radiation exposure of adults with both chest pain and dyspnea.

METHODS:

This was a prospective, 4-center, outcomes study. Patients were adults with dyspnea and chest pain, nondiagnostic ECGs, and no obvious diagnosis. Pretest probability for both acute coronary syndrome and pulmonary embolism was assessed with a validated method; ultralow risk was defined as pretest probability less than 2.5% for both acute coronary syndrome and pulmonary embolism. Patients were followed for diagnosis and total medical radiation exposure for 90 days.

RESULTS:

Eight hundred forty patients had complete data; 23 (3%) had acute coronary syndrome and 15 (2%) had pulmonary embolism. The cohort received an average of 4.9 mSv radiation to the chest, 48% from computed tomography pulmonary angiography. The pretest probability estimates for acute coronary syndrome and pulmonary embolism were less than 2.5% in 227 patients (27%), of whom 0 of 277 (0%; 95% confidence interval 0% to 1.7%) had acute coronary syndrome or pulmonary embolism and 7 of 227 (3%) had any significant cardiopulmonary diagnosis. The estimated chest radiation exposure per patient in this ultralow-risk group was 3.5 mSv, including 26 (3%) with greater than 5 mSv radiation to the chest and no significant cardiopulmonary diagnosis.

CONCLUSION:

One quarter of patients with chest pain and dyspnea had ultralow risk and no acute coronary syndrome or pulmonary embolism but were exposed to an average of 3.5 mSv radiation to the chest. These data can be used in a clinical guideline to reduce radiation exposure.