Development and Validation of an Electronic Adverse Event Model for Patient Safety Surveillance in Interventional Radiology.

Mull HJ, Foster MV, Higgins MCSS, Sturgeon DJ, Hederstedt K, Bart N, Lamkin RP, Sullivan BA, Ayeni C, Branch-Elliman W, Malloy PC. Development and Validation of an Electronic Adverse Event Model for Patient Safety Surveillance in Interventional Radiology. Journal of the American College of Radiology : JACR. 2024 May 1; 21(5):752-766.

Related HSR&D Project(s)

• IIR 18-034 – How Can We Make Invasive Non-Surgical Procedures Safer? Using Big Data to Identify Adverse Events and Opportunities to Mitigate Harm

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Abstract:

BACKGROUND: Comprehensive adverse event (AE) surveillance programs in interventional radiology (IR) are rare. Our aim was to develop and validate a retrospective electronic surveillance model to identify outpatient IR procedures that are likely to have an AE, to support patient safety and quality improvement. METHODS: We identified outpatient IR procedures performed in the period from October 2017 to September 2019 from the Veterans Health Administration (n  = 135,283) and applied electronic triggers based on posyprocedure care to flag cases with a potential AE. From the trigger-flagged cases, we randomly sampled n  = 1,500 for chart review to identify AEs. We also randomly sampled n  = 600 from the unflagged cases. Chart-reviewed cases were merged with patient, procedure, and facility factors to estimate a mixed-effects logistic regression model designed to predict whether an AE occurred. Using model fit and criterion validity, we determined the best predicted probability threshold to identify cases with a likely AE. We reviewed a random sample of 200 cases above the threshold and 100 cases from below the threshold from October 2019 to March 2020 (n  = 20,849) for model validation. RESULTS: In our development sample of mostly trigger-flagged cases, 444 of 2,096 cases (21.8%) had an AE. The optimal predicted probability threshold for a likely AE from our surveillance model was > 50%, with positive predictive value of 68.9%, sensitivity of 38.3%, and specificity of 95.3%. In validation, chart-reviewed cases with AE probability > 50% had a positive predictive value of 63% (n  = 203). For the period from October 2017 to March 2020, the model identified approximately 70 IR cases per month that were likely to have an AE. CONCLUSIONS: This electronic trigger-based approach to AE surveillance could be used for patient-safety reporting and quality review.