Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database

González‐González, Ana Isabel; Dinh, Truc Sophia; Meid, Andreas D.; Blom, Jeanet W.; Akker, Marjan van den; Elders, Petra; Thiem, Ulrich; Gaudry, Daniela Kuellenberg de; Snell, Kym I E; Perera, Rafael; Swart, Karin M. A.; Rudolf, Henrik; Bosch-Lenders, Donna; Trampisch, Hans–Joachim; Meerpohl, Joerg J; Flaig, Berthold Bodo; Kom, Ghainsom; Gerlach, Ferdinand M.; Hafaeli, Walter E; Glasziou, Paul; Muth, Christiane

doi:10.1016/j.mad.2021.111436

Cited by 5 publications

(7 citation statements)

References 76 publications

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“…In our case, this may be partly explained by differences in the inclusion criteria and in the baseline risk of patients from the development and validation datasets. On average, RIME patients were older (77 vs. 72 years), more frequently hospitalized, had more chronic conditions, took more drugs, and reported more symptoms than PRIMUM patients [ 30 ]. Other potential limitations relate to the harmonization of predictors, the insufficient sample sizes of the development and validation cohorts, and the low numbers of events ( n = 188 in the development cohort and n = 55 in the validation cohort).…”

Section: Discussionmentioning

confidence: 99%

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Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model

et al. 2023

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Background Anticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients’ individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context. Methods and findings To predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as “≥ 1 fall” vs. “no fall” within a 6-month follow-up period. Data from the RIME study (n = 1,197) were used in model development, and from PRIMUM (n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c-statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c-statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models. Conclusions The ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.

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Section: Discussionmentioning

confidence: 99%

“…Morbidity (in accordance with a list of 24 chronic conditions [ 30 ], number of chronic conditions);…”

Section: Methodsmentioning

confidence: 99%

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model

et al. 2023

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“…We used harmonised IPD from the PROPERmed database 32 that stem from four trials that qualified for inclusion because they recorded the precise times of study outcomes, namely ISCOPE (Integrated Systematic Care for Older PEople), 33 Opti-Med (Optimised clinical medication reviews in older people with 'geriatric giants' in general practice), 34 35 PRIMUM (PRIoritising MUltimedication in Multimorbidity in general practices) 36 37 and RIME (Reduction of potentially Inappropriate Medication in the Elderly; Deutsches Register Klinischer Studien-ID, DRKS00003610). Details of the origin and preparation of the source data for the PROPERmed database are described elsewhere.…”

Section: Source Of Data and Participantsmentioning

confidence: 99%

“…Details of the origin and preparation of the source data for the PROPERmed database are described elsewhere. 32 In brief, they were conducted in the Netherlands and Germany between 2009 and 2012 to optimise pharmacological treatment in older chronically ill patients. Three trials (Opti-Med, PRIMUM and RIME) compared a structured medication review consisting of several intervention components with usual care, whereas ISCOPE used a functional geriatric approach to compare usual care with a proactive and integrated plan.…”

Section: Source Of Data and Participantsmentioning

confidence: 99%

“…The recently introduced PROPERmed database provides such an IPD framework. 26 Basically, if we want our prediction model to perform well in new, independent patients, between-study heterogeneity with respect to missing values, covariate and endpoint distribution, baseline risks and predictor effects (ie, the associations between predictors and outcome) must be adequately accounted for during model development. 27 While exploring how these key elements drive (external) predictive performance, we are especially concerned with model calibration, the 'Achilles heel' of predictive analytics.…”

Section: Introductionmentioning

confidence: 99%

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Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity

et al. 2021

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ObjectiveTo explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients.Study design and settingUsing individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV).ResultsPrior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions.ConclusionsPredictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully.Trial registration numberPROSPERO id: CRD42018088129.

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What researchers on ageing should know about multimorbidity, geroprotectors and artificial intelligence

Bellantuono

Marengoni

2021

Mechanisms of Ageing and Development

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Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database

Cited by 5 publications

References 76 publications

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model

Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity

What researchers on ageing should know about multimorbidity, geroprotectors and artificial intelligence

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