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

Abstract

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 validationprocesses, 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.

Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients

“…It also argues for caution when interpreting negative findings for risk factors. In correspondence with the findings of previous reviews evaluating the performance of predictive scoring tools, this supports the common conception that some risk factors and predictive scoring tools at this stage may not be transferrable to more diverse settings [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

“…There is a great clinical need for the improved identification of patients at risk for DRP. The numerous predictive scoring tools proposed, so far, have already been the topic of excellent reviews [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ], but to the best of our knowledge this is the first systematic review and analysis of scoring tools with a focus on the individual factors included in the tools. The heterogenicity previously noted at the level of the overall predictive scoring tools is matched by a comparable variability of individual risk factors as well as definitions of risk factors and thresholds of risk factors.…”

“…In recent years, several of these predictive scoring tools have been developed. Plenty of literature reviews evaluating the overall performance and quality of these predictive scoring tools have already been published [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. These reviews frequently came to the conclusion that—limited external validation aside—the performance of the existing predictive scoring tools for drug-related problems is at best moderate and that the scores are often not easily applicable to other settings.…”

Quality indicators for appropriate in‐hospital pharmacotherapeutic stewardship: An international modified Delphi study

“…In the current study, no consensus was reached on which patients should (at least) be targeted by IPS due to their risk of medication-related harm. Our findings support those of the recent systematic review by Deawjaroen et al 54 The authors identified 14 currently available prediction tools and assessed their clinical utility in identifying adult hospitalised patients at risk of medication-related harm. Interestingly, the authors concluded that none of the tools were optimal for this purpose.…”

Need for larger cohorts and standardized tools to study diseases in newborn infants

“…[21][22][23] Cautious analysis of these data using both conventional and newer prediction tools can be helpful. [24][25][26] Similarly, the development of newer, easily portable equipment has raised new possibilities for the collection of standardized data. 27 We need to study large patient cohorts over time or in larger geographical territories to draw clinically relevant conclusions.…”