@Article{info:doi/10.2196/58800,
author="Joyce, Elizabeth
and McMullen, James
and Kong, Xiaowen
and O'Hare, Connor
and Gavrila, Valerie
and Cuttitta, Anthony
and Barnes, Geoffrey D
and Greineder, Colin F",
title="Performance of an Electronic Health Record--Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department",
journal="JMIR Med Inform",
year="2025",
month="Jan",
day="20",
volume="13",
pages="e58800",
keywords="pulmonary embolism; low-risk pulmonary embolism; risk; artery; pulmonary embolism severity index; clinical decision support; emergency department; hospital; lung; blood; clot; clotting; cardiovascular; index; score; measure; scale; tomography; image; imaging; PESI; CDS; ED",
abstract="Background: Studies suggest that less than 4{\%} of patients with pulmonary embolisms (PEs) are managed in the outpatient setting. Strong evidence and multiple guidelines support the use of the Pulmonary Embolism Severity Index (PESI) for the identification of acute PE patients appropriate for outpatient management. However, calculating the PESI score can be inconvenient in a busy emergency department (ED). To facilitate integration into ED workflow, we created a 2023 Epic-compatible clinical decision support tool that automatically calculates the PESI score in real-time with patients' electronic health data (ePESI [Electronic Pulmonary Embolism Severity Index]). Objective: The primary objectives of this study were to determine the overall accuracy of ePESI and its ability to correctly distinguish high- and low-risk PESI scores within the Epic 2023 software. The secondary objective was to identify variables that impact ePESI accuracy. Methods: We collected ePESI scores on 500 consecutive patients at least 18 years old who underwent a computerized tomography-pulmonary embolism scan in the ED of our tertiary, academic health center between January 3 and February 15, 2023. We compared ePESI results to a PESI score calculated by 2 independent, medically-trained abstractors blinded to the ePESI and each other's results. ePESI accuracy was calculated with binomial test. The odds ratio (OR) was calculated using logistic regression. Results: Of the 500 patients, a total of 203 (40.6{\%}) and 297 (59.4{\%}) patients had low- and high-risk PESI scores, respectively. The ePESI exactly matched the calculated PESI in 394 out of 500 cases, with an accuracy of 78.8{\%} (95{\%} CI 74.9{\%}‐82.3{\%}), and correctly identified low- versus high-risk in 477 out of 500 (95.4{\%}) cases. The accuracy of the ePESI was higher for low-risk scores (OR 2.96, P<.001) and lower when patients were without prior encounters in the health system (OR 0.42, P=.008). Conclusions: In this single-center study, the ePESI was highly accurate in discriminating between low- and high-risk scores. The clinical decision support should facilitate real-time identification of patients who may be candidates for outpatient PE management. ",
issn="2291-9694",
doi="10.2196/58800",
url="https://medinform.jmir.org/2025/1/e58800",
url="https://doi.org/10.2196/58800"
}