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Background: Unplanned admissions to hospital represent a hazardous event for older people. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. Aim: To develop and validate an easy-to-use prediction model for unplanned admissions to hospital in community-dwelling older adults using readily available data to allow rapid bedside assessment by GPs. Design and setting: This was a retrospective study using the general practice electronic health records of 243 324 community-dwelling adults aged ≥65 years linked with national administrative data to predict unplanned admissions to hospital within 6 months. Method: The dataset was geographically split into a development (n = 142 791/243 324, 58.7%) and validation (n = 100 533/243 324, 41.3%) sample to predict unplanned admissions to hospital within 6 months. The performance of three different models was evaluated with increasingly smaller selections of candidate predictors (optimal, readily available, and easy-to-use models). Logistic regression was used with backward selection for model development. The models were validated internally and externally. Predictive performance was assessed by area under the curve (AUC) and calibration plots. Results: In both samples, 7.6% (development cohort: n = 10 839/142 791, validation cohort: n = 7675/100 533) had ≥1 unplanned hospital admission within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior admissions to hospital, pulmonary emphysema, heart failure, and polypharmacy. Its discriminative ability after validation was AUC 0.72 (95% confidence interval = 0.71 to 0.72). Calibration plots showed good calibration. Conclusion: The models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not have an impact on predictive performance, demonstrating the robustness of the model. An easy-to-use tool has been developed in this study that may assist GPs in decision making and with targeted preventive interventions. [ABSTRACT FROM AUTHOR]

Introduction: This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support. Methods: This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability). Results: The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources. Conclusions: A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data. [ABSTRACT FROM AUTHOR]

Objectives: Nonpharmacologic interventions (NPIs) constitute an important part of treatment for older adults, cover a broad and diverse range of interventions, and have advantages over pharmacologic interventions (eg, limited adverse side effects). However, an unambiguous definition of NPIs is still lacking. Defining NPIs may facilitate research on this topic and enhance comparability of results between studies, and might help to face the challenges of recognition, acceptation, funding, and implementation. Therefore, the aim of this review was to provide an overview and comparison of the definitions of NPIs used in the current literature on older adults., Design: A systematic review was performed to provide an overview of the definitions of NPIs that are used in the current literature on older populations and to organize the characteristics involved in the definitions., Setting and Participants: People ≥60 years of age were included, not limited to a specific setting., Methods: A systematic search was performed in the following 5 databases: PubMed, Embase, Clarivate Analytics/Web of Science Core Collection, Cumulative Index to Nursing and Allied Health Literature, and Wiley/Cochrane Library. The time frame within the databases was from inception to December 4, 2023. Review articles, editorials and consensus papers were included., Results: We included 28 articles. We organized the definitions of NPI according to 4 different aspects: types of interventions involved, target population, goals the interventions addressed, and requirements of the interventions. Definitions in the current literature can generally be divided into 2 groups: NPIs described as not involving medication, and more elaborated multidomain definitions. Based on the results, we formulated criteria for types of interventions that can be considered an NPI., Conclusions and Implications: Using current descriptions and characteristics, elements for a new definition for NPIs were proposed. To improve research in this field, consensus needs to be reached regarding elements covered by a definition of NPIs., Competing Interests: Disclosures The authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)