Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review. (20th February 2023)

• Record Type: Journal Article
• Authors: Yasrebi-de Kom, Izak A R; Dongelmans, Dave A; de Keizer, Nicolette F; Jager, Kitty J; Schut, Martijn C; Abu-Hanna, Ameen; Klopotowska, Joanna E
• Is Part Of: Journal of the American Medical Informatics Association. Volume 30:Number 5(2023)
• Page Start: 978
• Page End: 988
• Publication Date: 2023-02-20
• Subjects: adverse drug events -- prediction models -- electronic health records -- hospitals -- machine learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocad014
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. Materials and Methods: We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Results: Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated. Conclusions: Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.