Evaluation of real-world evidence to assess health outcomes related to deprescribing medications in older adults: an International Society for Pharmacoepidemiology–endorsed systematic review of methodology - Scorecard - MDSpire
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Evaluation of real-world evidence to assess health outcomes related to deprescribing medications in older adults: an International Society for Pharmacoepidemiology–endorsed systematic review of methodology
Clinical Scorecard: Analysis of Real-World Data Methodologies for Evaluating Health Outcomes of Medication Deprescribing in Older Adults: A Systematic Review Supported by the International Society for Pharmacoepidemiology
At a Glance
Category
Detail
Condition
Polypharmacy and medication-related complications in older adults
Key Mechanisms
Clinically supervised tapering or stopping of medications to reduce low-value prescribing, adverse effects, and improve outcomes
Target Population
Older adults aged 50 years and older
Care Setting
Real-world clinical settings using observational data sources such as electronic health records and administrative claims
Key Highlights
Observational studies using real-world data are common for evaluating deprescribing effects but show heterogeneity in defining deprescribing exposures.
Methodological challenges include potential misclassification of deprescribing, confounding by indication, selection bias, immortal time bias, and defining appropriate comparators.
There is a lack of transparency and consistency in reporting deprescribing definitions and methods, highlighting the need for minimum reporting criteria.
Guideline-Based Recommendations
Diagnosis
Identify medications with high potential for adverse effects or uncertain net benefit as targets for deprescribing (e.g., Beers Criteria, STOPP criteria).
Management
Use clinically supervised processes to taper or stop medications aiming to minimize polypharmacy and improve patient outcomes.
Consider patient life expectancy and time to benefit when deciding on deprescribing.
Monitoring & Follow-up
Monitor for adverse effects and clinical outcomes longitudinally using real-world data sources.
Address potential biases such as immortal time bias and confounding in observational analyses.
Risks
Be aware of misclassification of deprescribing exposure due to nonadherence or temporary medication holds.
Recognize selection bias and confounding by indication when comparing deprescribed versus continued medication groups.
Patient & Prescribing Data
Older adults aged 50 years and older, including frail and complex patients often underrepresented in randomized controlled trials.
Real-world data studies provide insights on long-term and clinically relevant outcomes of deprescribing but require careful methodological approaches to reduce bias.
Clinical Best Practices
Clearly define deprescribing exposure with justification for minimum duration thresholds.
Use appropriate observational study designs that address time-related biases such as immortal time bias.
Ensure transparent reporting of methods and definitions to improve reproducibility and comparability across studies.
by Kaleen N Hayes, Joshua David Niznik, Danijela Gnjidic, Frank Moriarty, Nha Tran, Antoinette B Coe, Andrew R Zullo, Sirui Zhang, Matthew Alcusky, Dimitri Bennett, Sirpa Hartikainen, Marie-Laure Laroche, Xiaojuan Li, Joshua K Lin, Jennifer L Lund, Maurizio Sessa, Shahar Shmuel, Caroline Sirois, Denis Talbot, Miia Tiihonen, Xuerong Wen, Mouna J Sawan, Daniela C Moga
