医学
急诊科
肺栓塞
D-二聚体
假阳性悖论
危险分层
放射科
计算机断层摄影术
人工智能
内科学
计算机科学
精神科
作者
A. Haimovich,Karla López López,H. Forman,J. Kline,A. Venkatesh,R. Taylor
标识
DOI:10.1016/j.annemergmed.2022.08.079
摘要
Pulmonary embolism (PE) is a common emergency department (ED) diagnosis with a greater than 4% mortality rate, resulting in frequent over-testing via computed tomography PE (CT-PE) and exposing patients to the harms of ionizing radiation, iodinated contrasts, and increased costs. Clinical decision rules incorporating D-dimer testing are used to identify patients whose PE risk is below a CT-PE imaging threshold, but are subject to high rates of false positives and inconsistent use. We hypothesized that an artificial intelligence (AI) algorithm would augment existing D-dimer-based risk stratification and identify patients who are unlikely to benefit from imaging.
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