Zhou M, Zhang G, Wang N, Zhao T, Liu Y, Geng Y, Zhang J, Wang N, Peng N, Huang L. A novel score for predicting falls in community-dwelling older people: a derivation and validation study.
BMC Geriatr 2024;
24:491. [PMID:
38834944 DOI:
10.1186/s12877-024-05064-4]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/09/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND
Early detection of patients at risk of falling is crucial. This study was designed to develop and internally validate a novel risk score to classify patients at risk of falls.
METHODS
A total of 334 older people from a fall clinic in a medical center were selected. Least absolute shrinkage and selection operator (LASSO) regression was used to minimize the potential concatenation of variables measured from the same patient and the overfitting of variables. A logistic regression model for 1-year fall prediction was developed for the entire dataset using newly identified relevant variables. Model performance was evaluated using the bootstrap method, which included measures of overall predictive performance, discrimination, and calibration. To streamline the assessment process, a scoring system for predicting 1-year fall risk was created.
RESULTS
We developed a new model for predicting 1-year falls, which included the FRQ-Q1, FRQ-Q3, and single-leg standing time (left foot). After internal validation, the model showed good discrimination (C statistic, 0.803 [95% CI 0.749-0.857]) and overall accuracy (Brier score, 0.146). Compared to another model that used the total FRQ score instead, the new model showed better continuous net reclassification improvement (NRI) [0.468 (0.314-0.622), P < 0.01], categorical NRI [0.507 (0.291-0.724), P < 0.01; cutoff: 0.200-0.800], and integrated discrimination [0.205 (0.147-0.262), P < 0.01]. The variables in the new model were subsequently incorporated into a risk score. The discriminatory ability of the scoring system was similar (C statistic, 0.809; 95% CI, 0.756-0.861; optimism-corrected C statistic, 0.808) to that of the logistic regression model at internal bootstrap validation.
CONCLUSIONS
This study resulted in the development and internal verification of a scoring system to classify 334 patients at risk for falls. The newly developed score demonstrated greater accuracy in predicting falls in elderly people than did the Timed Up and Go test and the 30-Second Chair Sit-Stand test. Additionally, the scale demonstrated superior clinical validity for identifying fall risk.
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