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Br J Clin Pharmacol. 2018 May;84(5):846-864. doi: 10.1111/bcp.13514. Epub 2018 Feb 22.

Systematic review of predictive risk models for adverse drug events in hospitalized patients.

Author information

1
School of Pharmacy, Pharmacy Australia Centre of Excellence, The University of Queensland, Brisbane, QLD, 4102, Australia.
2
Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Woolloongabba, Brisbane, QLD, 4102, Australia.

Abstract

AIM:

An emerging approach to reducing hospital adverse drug events is the use of predictive risk scores. The aim of this systematic review was to critically appraise models developed for predicting adverse drug event risk in inpatients.

METHODS:

Embase, PubMed, CINAHL and Scopus databases were used to identify studies of predictive risk models for hospitalized adult inpatients. Studies had to have used multivariable logistic regression for model development, resulting in a score or rule with two or more variables, to predict the likelihood of inpatient adverse drug events. The Checklist for the critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) was used to critically appraise eligible studies.

RESULTS:

Eleven studies met the inclusion criteria and were included in the review. Ten described the development of a new model, whilst one study revalidated and updated an existing score. Studies used different definitions for outcome but were synonymous with or closely related to adverse drug events. Four studies undertook external validation, five internally validated and two studies did not validate their model. No studies evaluated impact of risk scores on patient outcomes.

CONCLUSION:

Adverse drug event risk prediction is a complex endeavour but could help to improve patient safety and hospital resource management. Studies in this review had some limitations in their methods for model development, reporting and validation. Two studies, the BADRI and Trivalle's risk scores, used better model development and validation methods and reported reasonable performance, and so could be considered for further research.

KEYWORDS:

adverse drug events; adverse drug reactions; clinical pharmacology; clinical pharmacy; drug related problems; medication errors; predictive risk model; risk score

PMID:
29337387
PMCID:
PMC5903258
DOI:
10.1111/bcp.13514
[Indexed for MEDLINE]
Free PMC Article

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