Validation of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Geriatric Outpatients
Bob van de Loo (Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam Public Health)
Martijn W. Heymans (Vrije Universiteit Amsterdam, Amsterdam Public Health)
Stephanie Medlock (Universiteit van Amsterdam, Amsterdam Public Health)
Nicole D.A. Boyé (Erasmus MC, Curaçao Medical Center, Willemstad)
Tischa J.M. van der Cammen (Erasmus MC, TU Delft - Human Factors)
Klaas A. Hartholt (Reinier de Graaf Gasthuis, Erasmus MC)
Marielle H. Emmelot-Vonk ( University Medical Centre Utrecht)
Ameen Abu-Hanna (Universiteit van Amsterdam, Amsterdam Public Health)
Natasja M. van Schoor (Amsterdam Public Health, Vrije Universiteit Amsterdam)
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Abstract
Objectives: Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone. Design: Retrospective, combined analysis of 2 prospective cohorts. Setting and Participants: Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department. Methods: We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models’ clinical value (ie, net benefit) against that of falls history for different decision thresholds. Results: During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any_fall and Recur_fall models, respectively. Any_fall overestimated the fall risk and we therefore updated only its intercept whereas Recur_fall showed good calibration and required no update. Compared with falls history, Any_fall and Recur_fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively. Conclusions and Implications: The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone.
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