Changes in prescribed medicines in older patients with multimorbidity and polypharmacy in general practice.

BACKGROUND: Treatment complexity rises in line with the number of drugs, single doses, and administration methods, thereby threatening patient adherence. Patients with multimorbidity often need flexible, individualised treatment regimens, but alterations during the course of treatment may further increase complexity. The objective of our study was to explore medication changes in older patients with multimorbidity and polypharmacy in general practice. METHODS: We retrospectively analysed data from the cluster-randomised PRIMUM trial (PRIoritisation of MUltimedication in Multimorbidity) conducted in 72 general practices. We developed an algorithm for active pharmaceutical ingredients (API), strength, dosage, and administration method to assess changes in physician-reported medication data during two intervals (baseline to six-months: ∆1; six- to nine-months: ∆2), analysed them descriptively at prescription and patient levels, and checked for intervention effects. RESULTS: Of 502 patients (median age 72 years, 52% female), 464 completed the study. Changes occurred in 98.6% of patients (changes were 19% more likely in the intervention group): API changes during ∆1 and ∆2 occurred in 414 (82.5%) and 338 (67.3%) of patients, dosage alterations in 372 (74.1%) and 296 (59.2%), and changes in API strength in 158 (31.5%) and 138 (27.5%) respectively. Administration method changed in 79 (16%) of patients in both ∆1 and ∆2. Simvastatin, metformin and aspirin were most frequently subject to alterations. CONCLUSION: Medication regimens in older patients with multimorbidity and polypharmacy changed frequently. These are mostly due to discontinuations and dosage alterations, followed by additions and restarts. These findings cast doubt on the effectiveness of cross-sectional assessments of medication and support longitudinal assessments where possible. TRIAL REGISTRATION: 1. Prospective registration: Trial registration number: NCT01171339 ; Name of registry: ClinicalTrials.gov; Date of registration: July 27, 2010; Date of enrolment of the first participant to the trial: August 12, 2010. 2. Peer reviewed trial registration: Trial registration number: ISRCTN99526053 ; Name of registry: Controlled Trials; Date of registration: August 31, 2010; Date of enrolment of the first participant to the trial: August 12, 2010.

Development and internal validation of prognostic models to predict negative health outcomes in older patients with multimorbidity and polypharmacy in general practice.

BACKGROUND: Polypharmacy interventions are resource-intensive and should be targeted to those at risk of negative health outcomes. Our aim was to develop and internally validate prognostic models to predict health-related quality of life (HRQoL) and the combined outcome of falls, hospitalisation, institutionalisation and nursing care needs, in older patients with multimorbidity and polypharmacy in general practices. METHODS: Design: two independent data sets, one comprising health insurance claims data (n=592 456), the other data from the PRIoritising MUltimedication in Multimorbidity (PRIMUM) cluster randomised controlled trial (n=502). Population: ≥60 years, ≥5 drugs, ≥3 chronic diseases, excluding dementia. Outcomes: combined outcome of falls, hospitalisation, institutionalisation and nursing care needs (after 6, 9 and 24 months) (claims data); and HRQoL (after 6 and 9 months) (trial data). Predictor variables in both data sets: age, sex, morbidity-related variables (disease count), medication-related variables (European Union-Potentially Inappropriate Medication list (EU-PIM list)) and health service utilisation. Predictor variables exclusively in trial data: additional socio-demographics, morbidity-related variables (Cumulative Illness Rating Scale, depression), Medication Appropriateness Index (MAI), lifestyle, functional status and HRQoL (EuroQol EQ-5D-3L). Analysis: mixed regression models, combined with stepwise variable selection, 10-fold cross validation and sensitivity analyses. RESULTS: Most important predictors of EQ-5D-3L at 6 months in best model (Nagelkerke's R² 0.507) were depressive symptoms (-2.73 (95% CI: -3.56 to -1.91)), MAI (-0.39 (95% CI: -0.7 to -0.08)), baseline EQ-5D-3L (0.55 (95% CI: 0.47 to 0.64)). Models based on claims data and those predicting long-term outcomes based on both data sets produced low R² values. In claims data-based model with highest explanatory power (R²=0.16), previous falls/fall-related injuries, previous hospitalisations, age, number of involved physicians and disease count were most important predictor variables. CONCLUSIONS: Best trial data-based model predicted HRQoL after 6 months well and included parameters of well-being not found in claims. Performance of claims data-based models and models predicting long-term outcomes was relatively weak. For generalisability, future studies should refit models by considering parameters representing well-being and functional status.