Effect of Algorithm on CT in Patients with Blunt Abdominopelvic Trauma
An algorithm’s effect on CT use in the emergency department.
The implementation of a clinical triaging algorithm decreases the use of multidetector CT in patients who present to the ED with blunt abdominopelvic trauma (BAPT), according to a study published in Radiology.
Researchers from Boston University Medical Center in Massachusetts and Palantir Technologies, in Palo Alto, California, evaluated the effect of an institutional clinical triaging algorithm on the rate of multidetector CT utilization in BAPT.
The study included 13,096 patients who presented at an urban level I trauma center over an eight-year period with BAPT. The patients ranged in age from 15 to 95 (mean age 42). These patients were divided into two groups: those who were admitted between January 1, 2006 to June 30, 2010, before the algorithm was implemented (the prealgorithm group) and after implementation, which was from July 1, 2010 to December 31, 2013 (the postalgorithm group). Parameters were recorded from abdominopelvic CT study reports for the pre- and postalgorithm groups: number of abdominopelvic CT examinations at admission, number of abdominopelvic CT examinations with positive BAPT-related findings, injury severity score, length of hospital stay, and number of mortalities.
The results showed there was a 32.1% decrease in use of CT following implementation of the algorithm:
“The implementation of a clinical triaging algorithm resulted in decreased use of multidetector CT in patients who presented with BAPT to the emergency department,” the researchers concluded.
Newsletter
Stay at the forefront of radiology with the Diagnostic Imaging newsletter, delivering the latest news, clinical insights, and imaging advancements for today’s radiologists.
Study Shows Enhanced Diagnosis of Coronary Artery Stenosis with Photon-Counting CTA
July 10th 2025In a new study comparing standard resolution and ultra-high resolution modes for patients undergoing coronary CTA with photon-counting detector CT, researchers found that segment-level sensitivity and accuracy rates for diagnosing coronary artery stenosis were consistently > 89.6 percent.
Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?
June 19th 2025Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.
