Dataset Information

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

ABSTRACT:

Background

Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.

Methods

Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.

Results

We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.

Conclusions

Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

SUBMITTER: Dormosh N

PROVIDER: S-EPMC11231951 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

Dormosh Noman N van de Loo Bob B Heymans Martijn W MW Schut Martijn C MC Medlock Stephanie S van Schoor Natasja M NM van der Velde Nathalie N Abu-Hanna Ameen A

Age and ageing 20240701 7

<h4>Background</h4>Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.<h4>Methods</h4>Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation ...[more]

PMID: 38979796

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Project description:IntroductionFall rates and fall-related injuries among community-dwelling older adults (≥65 years) are expected to increase rapidly, due to the aging population worldwide. Fall prevention programs (FPPs), consisting of strength and balance exercises, have been proven effective in reducing fall rates among older adults. However, these FPPs have not reached their full potential as most programs are under-enrolled. Therefore, this study aims to identify promising strategies that promote participation in FPPs among community-dwelling older adults.MethodsThis is an exploratory qualitative study. Previously, barriers and facilitators for participation in FPPs by older adults had been identified. Next, six strategies had been designed using the Intervention Mapping approach: (1) reframing; (2) informing about benefits; (3) raising awareness of risks; (4) involving social environment; (5) offering tailored intervention; (6) arranging practicalities. Strategies were validated during semi-structured interviews with community-dwelling older adults (n = 12) at risk of falling. Interviews were audio-recorded, transcribed, and analyzed following a qualitative thematic methodology, with a hybrid approach.ResultsAll strategies were considered important by at least some of the respondents. However, two strategies stood out: (1) reframing 'aging' and 'fall prevention': respondents preferred to be approached differently, taking a 'life course' perspective about falls, and avoiding confronting words; and (2) 'informing about benefits' (e.g., 'living independently for longer'); which was mentioned to improve the understanding of the relevance of participating in FPPs. Other strategies were considered important to take into account too, but opinions varied more strongly.DiscussionThis study provides insight into potential strategies to stimulate older adults to participate in FPPs. Results suggest that reframing 'aging' and 'fall prevention' may facilitate the dialogue about fall prevention, by communicating differently about the topic, for example 'staying fit and healthy', while focusing on the benefits of participating in FPPs. Gaining insight into the strategies' effectiveness and working mechanisms is an area for future research. This could lead to practical recommendations and help professionals to enhance older adults' participation in FPPs. Currently, the strategies are further developed to be applied and evaluated for effectiveness in multiple field labs in a central Dutch region (Utrecht).

Project description:BackgroundFalls in older adults remain a pressing health concern. With advancements in data analytics and increasing uptake of electronic health records, developing comprehensive predictive models for fall risk is now possible. We aimed to systematically identify studies involving the development and implementation of predictive falls models which used routinely collected electronic health record data in home-based, community and residential aged care settings.MethodsA systematic search of entries in Cochrane Library, CINAHL, MEDLINE, Scopus, and Web of Science was conducted in July 2020 using search terms relevant to aged care, prediction, and falls. Selection criteria included English-language studies, published in peer-reviewed journals, had an outcome of falls, and involved fall risk modelling using routinely collected electronic health record data. Screening, data extraction and quality appraisal using the Critical Appraisal Skills Program for Clinical Prediction Rule Studies were conducted. Study content was synthesised and reported narratively.ResultsFrom 7,329 unique entries, four relevant studies were identified. All predictive models were built using different statistical techniques. Predictors across seven categories were used: demographics, assessments of care, fall history, medication use, health conditions, physical abilities, and environmental factors. Only one of the four studies had been validated externally. Three studies reported on the performance of the models.ConclusionsAdopting predictive modelling in aged care services for adverse events, such as falls, is in its infancy. The increased availability of electronic health record data and the potential of predictive modelling to document fall risk and inform appropriate interventions is making use of such models achievable. Having a dynamic prediction model that reflects the changing status of an aged care client is key to this moving forward for fall prevention interventions.

Project description:Hyperkyphosis, an increased kyphosis angle of the thoracic spine, was associated with a higher fall incidence in the oldest quartile of a large prospective cohort of community-dwelling older adults. Hyperkyphosis could serve as an indicator of an increased fall risk as well as a treatable condition.IntroductionHyperkyphosis is frequently found in adults aged 65 years and older and may be associated with falls. We aimed to investigate prospectively in community-dwelling older adults whether hyperkyphosis or change in the kyphosis angle is associated with fall incidence.MethodsCommunity-dwelling older adults (n = 1220, mean age 72.9 ± 5.7 years) reported falls weekly over 2 years. We measured thoracic kyphosis through the Cobb angle between the fourth and 12th thoracic vertebra on DXA-based vertebral fracture assessments and defined hyperkyphosis as a Cobb angle ≥ 50°. The change in the Cobb angle during follow-up was dichotomized (< 5 or ≥ 5°). Through multifactorial regression analysis, we investigated the association between the kyphosis angle and falls.ResultsHyperkyphosis was present in 15% of the participants. During follow-up, 48% of the participants fell at least once. In the total study population, hyperkyphosis was not associated with the number of falls (adjusted IRR 1.12, 95% CI 0.91-1.39). We observed effect modification by age (p = 0.002). In the oldest quartile, aged 77 years and older, hyperkyphosis was prospectively associated with a higher number of falls (adjusted IRR 1.67, 95% CI 1.14-2.45). Change in the kyphosis angle was not associated with fall incidence.ConclusionsHyperkyphosis was associated with a higher fall incidence in the oldest quartile of a large prospective cohort of community-dwelling older adults. Because hyperkyphosis is a partially reversible condition, we recommend investigating whether hyperkyphosis is one of the causes of falls and whether a decrease in the kyphosis angle may contribute to fall prevention.

Dataset Information

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

Background

Methods

Results

Conclusions

Publications

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

Similar Datasets

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