Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 22, 2021
Open Peer Review Period: Jul 22, 2021 - Sep 16, 2021
Date Accepted: Dec 27, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Validation of an Automated Pulmonary Embolism Risk Assessment Using Well’s Criteria
ABSTRACT
Background:
Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for 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 Well’s Score for Pulmonary Embolism for patients in the ED, which could potentially reduce unnecessary CTPA studies.
Methods:
We designed an automated process using electronic health records (EHR) data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Well’s Scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at two tertiary care hospitals in New York over a two-month period. To validate the automated process, scores were compared to those derived from a two-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 had a PE prevalence of 15.9%, whereas those classified as “PE unlikely” (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 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 EHR data elements, including free-text fields, to classify risk for PE in ED visits.
Citation
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Copyright
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