Open AccessAutomated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study
Author(s) -
Nasen Zhang,
Philippe Rameau,
Marsophia Julemis,
Yanping Liu,
Jeffrey Solomon,
Sundas Khan,
Thomas McGinn,
Safiya Richardson
Publication year - 2022
Publication title -
jmir formative research
Language(s) - English
Resource type - Journals
ISSN - 2561-326X
DOI - 10.2196/32230
Subject(s) - medicine , pulmonary embolism , emergency department , retrospective cohort study , malignancy , predictive value , chart , electronic health record , emergency medicine , cohort , radiology , health care , statistics , mathematics , psychiatry , economics , economic growth
Background Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy. Objective We aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing. Methods We designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period. To validate the automated process, the scores were compared to those derived from a 2-clinician chart review. Results A total of 202 ED encounters resulted in a completed CTPA to form the retrospective study cohort. Patients classified as “PE likely” by the automated process (126/202, 62%) had a PE prevalence of 15.9%, whereas those classified as “PE unlikely” (76/202, 38%; Wells score >4) had a PE prevalence of 7.9%. With respect to classification of the patient as “PE likely,” the automated process achieved an accuracy of 92.1% when compared with the chart review, with sensitivity, specificity, positive predictive value, and negative predictive value of 93%, 90.5%, 94.4%, and 88.2%, respectively. Conclusions This was a successful development and validation of an automated process using electronic health records data elements, including free-text fields, to classify risk for PE in ED visits.