Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 19;30(5):978-988.
doi: 10.1093/jamia/ocad014.

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Izak A R Yasrebi-de Kom  1   2 , Dave A Dongelmans  2   3 , Nicolette F de Keizer  1   2 , Kitty J Jager  1   2   4 , Martijn C Schut  1   2   5 , Ameen Abu-Hanna  1   2 , Joanna E Klopotowska  1   2
Affiliations

Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Izak A R Yasrebi-de Kom et al. J Am Med Inform Assoc. .

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.

Keywords: adverse drug events; electronic health records; hospitals; machine learning; prediction models.

PubMed Disclaimer

Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
PRISMA flow diagram of the citation search and selection strategy. Adopted from Moher et al. ADE: adverse drug event; EHR: electronic health record; PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses.
Figure 2.
Figure 2.
Two approaches to develop ADE prediction models for diagnosis or prognosis. ADE: adverse drug event; EHR: electronic health records.
See this image and copyright information in PMC

References

    1. Wolfe D, Yazdi F, Kanji S, et al. Incidence, causes, and consequences of preventable adverse drug reactions occurring in inpatients: A systematic review of systematic reviews. PLoS One 2018; 13 (10): e0205426. - PMC - PubMed
    1. Classen DC. Adverse drug events in hospitalized patients. excess length of stay, extra costs, and attributable mortality. JAMA 1997; 277 (4): 301–6. - PubMed
    1. Shojania KG, Thomas EJ.. Trends in adverse events over time: why are we not improving? BMJ Qual Saf 2013; 22 (4): 273–7. - PubMed
    1. Manias E. Detection of medication-related problems in hospital practice: a review. Br J Clin Pharmacol 2013; 76 (1): 7–20. - PMC - PubMed
    1. Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162 (1): W1–73. - PubMed

Publication types