Risk Prediction Models for Adverse Drug Reactions and Adverse Drug Events in Older Adults – A Systematic Review and Meta-analysis

Abstract

Abstract Background Adverse drug reactions (ADRs) and adverse drug events (ADEs) are common and result in significant morbidity, mortality and healthcare costs. As the world’s population ages, the consumption of medications and healthcare will increase. Models predicting ADR risks in older adults have previously been found to lack reliability and validity. The aim of this systematic review and meta-analysis is to provide an updated, comprehensive quality assessment and analysis of ADR-risk prediction tools in older adults. Methods Standard computerised-databases and citations were searched (2012 to 2023) to identify relevant peer-reviewed studies. Studies which developed and/or validated an ADR/ADE prediction model for use in older adults were included. Four studies from a previous systematic review were also included. The TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines were used to evaluate each of the included studies. Random effects models were used to derive a pooled discrimination estimate (area under the receiver operation curve; AUROC) for model development studies and model validation studies. The prediction model risk of bias assessment tool (PROBAST) was applied to evaluate bias. Results Five out of 11,481 titles, plus four previous studies, met all inclusion criteria and underwent TRIPOD evaluation. Meta-analysis resulted in a favourable pooled AUROC 0.75 (95% CI 0.57, 0.87; I-squared= 96.65%) for model development studies and 0.70 (95% CI 0.57, 0.80; I-squared= 90.53%) for model validation studies but there was substantial heterogeneity. Studies had poor adherence (range 32-50%; median 44%; IQR 11.5%) to TRIPOD guidelines. The overall risk of bias was high for all included studies. Conclusion Seven risk prediction models were identified, all exhibiting poor adherence to TRIPOD guidelines which may question the investigational rigor of the studies and their usability. This underscores the need for the development of a validated, robust and reliable tool worthy of implementation and testing in a real-world setting.