Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: A systematic review of clinical prediction models and risk assessment tools
Issued Date
2022-02-01
Resource Type
ISSN
03065251
eISSN
13652125
Scopus ID
2-s2.0-85118233520
Pubmed ID
34626130
Journal Title
British Journal of Clinical Pharmacology
Volume
88
Issue
4
Start Page
1613
End Page
1629
Rights Holder(s)
SCOPUS
Bibliographic Citation
British Journal of Clinical Pharmacology Vol.88 No.4 (2022) , 1613-1629
Suggested Citation
Deawjaroen K., Sillabutra J., Poolsup N., Stewart D., Suksomboon N. Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: A systematic review of clinical prediction models and risk assessment tools. British Journal of Clinical Pharmacology Vol.88 No.4 (2022) , 1613-1629. 1629. doi:10.1111/bcp.15104 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86186
Title
Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: A systematic review of clinical prediction models and risk assessment tools
Author's Affiliation
Other Contributor(s)
Abstract
Aims: This study aimed to review systematically all available prediction tools identifying adult hospitalized patients at risk of drug-related problems, and to synthesize the evidence on performance and clinical usefulness. Methods: PubMed, Scopus, Web of Science, Embase, and CINAHL databases were searched for relevant studies. Titles, abstracts and full-text studies were sequentially screened for inclusion by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool (PROBAST) and the Revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklists were used to assess risk of bias and applicability of prediction tools. A narrative synthesis was performed. Results: A total of 21 studies were included, 14 of which described the development of new prediction tools (four risk assessment tools and ten clinical prediction models) and six studies were validation based and one an impact study. There were variations in tool development processes, outcome measures and included predictors. Overall, tool performance had limitations in reporting and consistency, with the discriminatory ability based on area under the curve receiver operating characteristics (AUROC) ranging from poor to good (0.62–0.81), sensitivity and specificity ranging from 57.0% to 89.9% and 30.2% to 88.0%, respectively. The Medicines Optimisation Assessment tool and Assessment of Risk tool were prediction tools with the lowest risk of bias and low concern for applicability. Studies reporting external validation and impact on patient outcomes were scarce. Conclusion: Most prediction tools have limitations in development and validation processes, as well as scarce evidence of clinical usefulness. Future studies should attempt to either refine currently available tools or apply a rigorous process capturing evidence of acceptance, usefulness, performance and outcomes.