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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.
There is limited knowledge on the prevalence and risk factors of diabetic retinopathy (DR) in dialysis patients. We have investigated the association between diabetes mellitus and lipid-related biomarkers and retinopathy in hemodialysis patients. We reviewed 1,255 hemodialysis patients with type 2 diabetes mellitus (T2DM) who participated in the German Diabetes and Dialysis Study (4D Study). Associations between categorical clinical, biochemical variables and diabetic retinopathy were examined by logistic regression. On average, patients were 66 ± 8 years of age, 54% were male and the HbA1c was 6.7% ± 1.3%. DR, found in 71% of the patients, was significantly and positively associated with fasting glucose, HbA1c, time on dialysis, age, systolic blood pressure, body mass index and the prevalence of other microvascular diseases (e.g. neuropathy). Unexpectedly, DR was associated with high HDL cholesterol and high apolipoproteins AI and AII. Patients with coronary artery disease were less likely to have DR. DR was not associated with gender, smoking, diastolic blood pressure, VLDL cholesterol, triglycerides, and LDL cholesterol. In summary, the prevalence of DR in patients with type 2 diabetes mellitus requiring hemodialysis is higher than in patients suffering from T2DM, who do not receive hemodialysis. DR was positively related to systolic blood pressure (BP), glucometabolic control, and, paradoxically, HDL cholesterol. This data suggests that glucose and blood pressure control may delay the development of DR in patients with diabetes mellitus on dialysis.