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

Abstract

BackgroundPolypharmacy 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.MethodsDesign: 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.ResultsMost 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.ConclusionsBest 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.

How to Improve Healthcare for Patients with Multimorbidity and Polypharmacy in Primary Care: A Pragmatic Cluster-Randomized Clinical Trial of the MULTIPAP Intervention

“…The MAI is considered to be the most reliable and valid implicit instrument to measure the appropriateness of a patient’s medication regime. The studies collected in Hanlon et al [ 39 ] demonstrated both validity and its association with relevant and specific service, in line with the results of the study by Müller et al [ 41 ]. In 1997, Schamder et al [ 42 ] found that a difference of two points was associated with differences in hospitalization and unscheduled outpatient or emergency visits.…”

The burden of neurological comorbidities in six autoimmune bullous diseases: a population‐based study

“…The current study was designed as a controlled, cross‐sectional, large‐scale study utilizing the computerized data set of Techniker Krankenkasse (TK). TK is the largest statutory health insurer in Germany, providing health insurance for 10.7 million inhabitants nowadays and 8.3 million inhabitants in 2012 17 . Based on the German Social Codebook V, all statutory health insurance companies collect basic data on sociodemographic variables and on comorbidity profile of enrolees 17 .…”

“…TK is the largest statutory health insurer in Germany, providing health insurance for 10.7 million inhabitants nowadays and 8.3 million inhabitants in 2012 17 . Based on the German Social Codebook V, all statutory health insurance companies collect basic data on sociodemographic variables and on comorbidity profile of enrolees 17 . All data were fully anonymized by the insurer before they were accessed by the authors.…”

The Medication Appropriateness Index: A Clinimetric Measure

“…The C-statistics showed similar discriminatory abilities, i.e., 0.71 (80+ score), 0.57 (STOPP), 0.54 (START), and 0.63 (MAI), demonstrating that all measures had an association with rehospitalization or death. In another study that aimed to internally validate prognostic models to predict health-related quality of life as measured by the EQ-5D-5L, investigators used data from the PRIoritising MUltimedication in Multimorbidity (PRIMUM) cluster randomized controlled trial ( n = 502, ≥60 years, ≥5 drugs, ≥3 chronic diseases) and examined multiple variables in mixed regression models [15]. They found that the MAI score was a significant predictor of health-related quality of life.…”