Key result
Prediction tools for drug-related problems showed poor to good discriminatory ability (AUROC 0.62-0.81), with scarce evidence of external validation and clinical usefulness.
Why the study?
The study aimed to systematically review all available prediction tools identifying adult hospitalized patients at risk of drug-related problems and synthesize evidence on their performance and clinical usefulness.
What is the performance and clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems?
Systematic Review (n=21)
What is the performance and clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems?
Current prediction tools for identifying drug-related problems in hospitalized adults show inconsistent performance and lack robust external validation or evidence of clinical impact.
Prediction models for drug-related problems need external validation before use; leaves open routine clinical adoption in hospitalized adults.
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.
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Deawjaroen et al. (2021) conducted a systematic review in drug-related problems (n=21). Prediction tools for drug-related problems was evaluated on tool performance and clinical usefulness. Prediction tools for drug-related problems showed poor to good discriminatory ability (AUROC 0.62-0.81), with scarce evidence of external validation and clinical usefulness.
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