A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy

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

doi:10.1016/j.jclinepi.2020.10.006

Cited by 8 publications

(11 citation statements)

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“…cRCTs included in PROPERmed aimed to optimize medication in older chronically ill patients from general practices. Based on the core dataset that included variables on socio-demographics, well-being, morbidity, and medication, two prognostic models were developed that predict deterioration of health-related quality of life ( González-González et al, 2020 ) and all-cause hospitalization (Meid et al, submitted). Details on the rationale and conduct of PROPERmed will be described elsewhere ( González-González et al, re-submitted ).…”

Section: Methodsmentioning

confidence: 99%

“…Based on the core dataset that included variables on socio-demographics, well-being, morbidity, and medication, two prognostic models were developed that predict deterioration of health-related quality of life ( González-González et al, 2020 ) and all-cause hospitalization (Meid et al, submitted). Details on the rationale and conduct of PROPERmed will be described elsewhere ( González-González et al, re-submitted ). To address the research question in our study, other variables in addition to PROPERmed’s core dataset are needed from the trials, which include variables on falls as well as on ACh symptoms (e.g., vertigo/dizziness, dry mouth).…”

Section: Methodsmentioning

confidence: 99%

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Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy? Study Protocol for the Development and Validation of a Prognostic Model

et al. 2021

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Background: Cumulative anticholinergic exposure, also known as anticholinergic burden, is associated with a variety of adverse outcomes. However, studies show that anticholinergic effects tend to be underestimated by prescribers, and anticholinergics are the most frequently prescribed potentially inappropriate medication in older patients. The grading systems and drugs included in existing scales to quantify anticholinergic burden differ considerably and do not adequately account for patients’ susceptibility to medications. Furthermore, their ability to link anticholinergic burden with adverse outcomes such as falls is unclear. This study aims to develop a prognostic model that predicts falls in older general practice patients, to assess the performance of several anticholinergic burden scales, and to quantify the added predictive value of anticholinergic symptoms in this context.Methods: Data from two cluster-randomized controlled trials investigating medication optimization in older general practice patients in Germany will be used. One trial (RIME, n = 1,197) will be used for the model development and the other trial (PRIMUM, n = 502) will be used to externally validate the model. A priori, candidate predictors will be selected based on a literature search, predictor availability, and clinical reasoning. Candidate predictors will include socio-demographics (e.g. age, sex), morbidity (e.g. single conditions), medication (e.g. polypharmacy, anticholinergic burden as defined by scales), and well-being (e.g. quality of life, physical function). A prognostic model including sociodemographic and lifestyle-related factors, as well as variables on morbidity, medication, health status, and well-being, will be developed, whereby the prognostic value of extending the model to include additional patient-reported symptoms will be also assessed. Logistic regression will be used for the binary outcome, which will be defined as “no falls” vs. “≥1 fall” within six months of baseline, as reported in patient interviews.Discussion: As the ability of different anticholinergic burden scales to predict falls in older patients is unclear, this study may provide insights into their relative importance as well as into the overall contribution of anticholinergic symptoms and other patient characteristics. The results may support general practitioners in their clinical decision-making and in prescribing fewer medications with anticholinergic properties.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy? Study Protocol for the Development and Validation of a Prognostic Model

et al. 2021

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“…Inclusion criteria for the study participants were identical to our previous work, 38 with patients from general practices being eligible if they were aged 60 years or older, had been diagnosed with at least one chronic condition defined using the O'Halloran list, 39 and had at least one chronic prescription at study baseline (≤2 weeks duration in PRIMUM, ≤2 months in ISCOPE and ≤3 months in Opti-Med and RIME).…”

Section: Source Of Data and Participantsmentioning

confidence: 99%

“…We thus considered five iterations of each of six multiple-imputed (MI) datasets, 64 and pooled them according to Rubin's Rules. 65 This procedure was extensively investigated in the PROPERmed database in a previous project 38 with no impact on predictive performance with higher numbers of iterations and imputations. All results were compared with complete-case (CC) analyses, whenever applicable.…”

Section: Sample Size and Missing Datamentioning

confidence: 99%

“…To quantify potential optimism, the uniform shrinkage factor was obtained by applying the mean difference in the calibration slopes for each bootstrap model to both the original IPD and in-sample bootstrap performance. 38 In addition, estimates of generalisability were obtained using IECV, with each study just the once serving as a validation sample for a model developed in the remaining studies. 25 The c-statistic 73 and CITL 74 were the numerical metrics of choice, while calibration plots were visually explored.…”

Section: Sample Size and Missing Datamentioning

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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Quality of life in individuals with knee osteoarthritis versus asymptomatic individuals

Obara,

Cardoso,

Reis

et al. 2023

Musculoskeletal Care

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BackgroundOsteoarthritis (OA) is characterised by joint degeneration and represents the leading cause of disability in old age. OA entails a personal burden, with suffering and reduced quality of life (QoL).ObjectiveTo compare the QoL of individuals with OA to that of asymptomatic individuals in order to determine the actual impact of OA on the affected population.MethodCross‐sectional study with a sample of 140 patients diagnosed with OA. Another 51 sex‐ and age‐matched asymptomatic individuals with no clinical signs of knee OA or lower limb osteoarticular symptoms for at least the preceding six months were recruited. Knee OA was stratified radiologically according to the Ahlbäck classification (1968). QoL was measured using the SF‐36 questionnaire (Medical Outcomes Study 36‐Item Short‐Form Health Survey). The participants were assigned to “osteoarthritis” (OG) or “asymptomatic” (AG) groups.ResultsThe OG presented greater body mass and BMI than the AG. Perceived QoL was worse for the OG than the AG across SF‐36 domains. In the comparison of the grades of OA degeneration, the group with Ahlbäck grades 4 and 5 (severe) perceived their QoL as poorer than those with grades 1, 2, and 3 (moderate). The grade of OA, older age, and BMI were shown to be strong independent predictors of poor perceived quality of life.ConclusionIndividuals with knee OA showed worse perceived QoL compared with asymptomatic individuals. The domains with the lowest scores were physical functioning and functional limitation. Quality of life was influenced by BMI, age, sex, and grade of osteoarthritis.

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A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy

Cited by 8 publications

References 70 publications

Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy? Study Protocol for the Development and Validation of a Prognostic Model

Are Anticholinergic Symptoms a Risk Factor for Falls in Older General Practice Patients With Polypharmacy? Study Protocol for the 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

Quality of life in individuals with knee osteoarthritis versus asymptomatic individuals

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