Background: Older patients are at an increased risk of developing adverse drug reactions (ADRs). The aim of this study was to develop a risk prediction model (ADAPTiP) for ADR-related hospital admissions in older populations, based on predictors available at the time of hospital admission. Methods: We used the Adverse Drug reactions in an Ageing PopulaTion (ADAPT) cohort (N=798; 361 ADR-related admissions; 437 non-ADR-related admissions), a cross-sectional and prospective cohort study designed to examine the prevalence of and risk factors for ADR-related hospital admissions in patients aged ≥ 65 years. Twenty predictors (categorised as sociodemographic-related, functional ability-related, disease-related and medication-related) were considered in the development of the model. A multivariable logistic regression model was developed using statistically significant univariate associations and/or clinically relevant predictors to estimate adjusted odds ratios and 95% confidence intervals (CI). Calibration and discriminative performance of the model was assessed by the Hosmer-Lemeshow test and by calculating the area under the receiver operator characteristic (AUROC) curve. Results: The multivariable model (ADAPTiP) included ten predictors. Antithrombotic agents, diuretics and RAAS drug classes and the primary presenting complaints of bleeding disorders, gastrointestinal disorders and syncope and frailty were significantly associated with an ADR-related hospital admission. Increasing age and having chronic lung disease were significantly associated with having a non-ADR-related hospital admission. The AUROC was 0.78 [95%CI:0.75;0.81] with sensitivity and specificity values of 59% and 83%, respectively. Conclusion: ADAPTiP has potential as a risk prediction model for ADR-related hospital admissions. Future research will validate this model in other settings.

Deprescribing is an essential component of safe prescribing, especially for people with higher levels of polypharmacy. Identifying individuals prepared to consider medicine changes may facilitate deprescribing-orientated reviews. We aimed to explore the relationship between revised patient attitudes towards deprescribing (rPATD) scores and medication changes in older people prescribed ≥15 medicines. A secondary analysis of rPATD scores and prescription data from a cluster randomised controlled trial of a GP-delivered, deprescribing-orientated medication review was conducted. The association between number of medicines stopped, started and changed and baseline rPATD scores was assessed using Poisson regression adjusting for patient age, gender, study group allocation, baseline number of medicines and effects of clustering. Participants (n=404) had a mean age of 76.4 years and were prescribed a mean of 17.1 medicines at baseline. Willingness to stop a medicine was associated with higher rates of both deprescribing (IRR: 1.40; 95%CI: 1.06-1.84) and initiating medicines (IRR: 1.43; 95%CI: 1.09-1.88). Satisfaction with current medicines was associated with a lower rate of deprescribing (IRR: 0.69; 95%CI: 0.57-0.85). The rPATD questionnaire could be used as part of a deprescribing intervention to identify participants who may be prepared to engage in deprescribing, enabling more efficient use of clinician time during complex consultations.