A novel score for predicting falls in community-dwelling older people: a derivation and validation study

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.

Keywords: Accidental falls; Fall prediction; Older adults; Risk assessment.

Fig. 1

Texture feature selection using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. A, Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via minimum criteria. The area under the receiver-operating characteristic curve (AUROC) was plotted versus log(λ) in Model 1. B, Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via minimum criteria. The AUROC was plotted versus log(λ) in Model 2

Fig. 2

Nomogram for the prediction of falls (A) A nomogram was constructed based on the data in Model 1 (B) A nomogram was constructed based on the data in Model 2 The points of each feature were added to obtain the total points, and a vertical line was drawn on the total points to obtain the corresponding ‘risk of fall’. FRQ-Q1 = I have fallen in the past year; FRQ-Q3 = Sometimes I feel unsteady when I am walking; FR-Total = The total score of the self-rated Fall Risk Questionnaire

Fig. 3

(A) Calibration curves of the nomogram prediction in Model 1 and Model 2. (B) The test result variable(s) C-statistics of Model 1 (0.803), Model2 (0.752), the TUG test (0.530) and 30s’chair sit-to-stand test were 0.512; C-statistics = Area Under Curve. (C) Decision curve analysis (DCA) of the nomogram prediction in Model 1 and Model 2

Fig. 4

Observed vs. Estimated Fall Risk According to the Numerical Risk Score. 1: Estimated risk; 2: Observed risk