Development of a personalized fall rate prediction model: the GERICO cohort analysis

Wapp, Christina; Ferrari, Serge; Zysset, Philippe; Zwahlen, Marcel

doi:10.1016/j.bonr.2022.101457

Cited by 1 publication

(4 citation statements)

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“…Although it is known that there exists a vast amount of risk factors that are associated with falling, the previously conducted analysis of the three cohorts showed that prior falls were superior in predicting future falls compared to other predictors. Variables such as physical performance tests, age, sex, comorbidities, medication, or quality of life were not improving the predictive accuracy of the models in combination with the history of falls [17,18]. Fear of falling was the only additional predictor selected with variable selection in the SCT study.…”

Section: Discussionmentioning

confidence: 92%

“…Fall rate prediction models were developed using a count regression modelling approach, and two of the three analyses have been published previously [17,18]. In short, the results showed that the history of falls measured as the number of prior falls within 12 months before the study examination was the best predictor for future falls in all three cohorts [17,18]. Furthermore, we showed the importance of how the information about the fall history is treated as a predictor.…”

Section: Introductionmentioning

confidence: 92%

“…Individual participant data were available from the original data sets of all cohorts. The analysis and development of the GERICO and SCT prediction models have been published previously [17,18], and the analysis of the KFPS is available in the supplementary material. A list of all predictors investigated in the prior analyses can be found in the supplementary material, eTable 1.…”

Section: Cohorts and Datamentioning

confidence: 99%

“…The three cohorts are the Geneva Retirees Cohort (GERICO) [13], the Swiss CHEF Trial (SCT) [14], and the Kuopio Fall Prevention Study (KFPS) [15,16]. Fall rate prediction models were developed using a count regression modelling approach, and two of the three analyses have been published previously [17,18]. In short, the results showed that the history of falls measured as the number of prior falls within 12 months before the study examination was the best predictor for future falls in all three cohorts [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants

Wapp,

Mittaz Hager,

Rikkonen

et al. 2024

BMC Geriatr

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Background Fragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction. Methods A combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage. Results The coefficient estimates for the prior number of falls were consistent among the three studies. Higgin’s I2 as heterogeneity measure ranged from 0 to 55.39%. The overall coefficient estimates indicated that the expected fall rate increases with an increasing number of previous falls. External model validation revealed that the prediction errors for the data sets were independent of the model to which they were applied. Conclusion This analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.

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Section: Discussionmentioning

confidence: 92%