TY - JOUR
T1 - Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis
T2 - a systematic review
AU - Yasrebi-de Kom, Izak A R
AU - Dongelmans, Dave A
AU - de Keizer, Nicolette F
AU - Jager, Kitty J
AU - Schut, Martijn C
AU - Abu-Hanna, Ameen
AU - Klopotowska, Joanna E
N1 - Funding Information: This study was part of the project “Towards a leaRning mEdication Safety system in a national network of intensive Care Units-timely detection of adverse drug Events” (RESCUE), which is funded by The Netherlands Organization for Health Research and Development (ZonMw project number: 848018004). The funder had no role in the design of the study, the collection, analysis, and interpretation of the data or in writing the manuscript. Publisher Copyright: © The Author(s) 2023.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - 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.
AB - 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.
KW - adverse drug events
KW - electronic health records
KW - hospitals
KW - machine learning
KW - prediction models
UR - http://www.scopus.com/inward/record.url?scp=85160073926&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/jamia/ocad014
DO - https://doi.org/10.1093/jamia/ocad014
M3 - Review article
C2 - 36805926
SN - 1067-5027
VL - 30
SP - 978
EP - 988
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 5
ER -