Development and Validation of a Predictive Model for Fall Risk in Pre-Frail Older Adults.

Purpose: To develop a predictive model for fall risk in pre-frail older adults, providing a basis for early identification and prevention of falls among this population.

Method: This was a multicenter prospective cohort study. A total of 473 pre-frail older adults were included, 335 as the training set and 142 as the test set. Univariate and stepwise binary logistic regression analyses were conducted to identify the relationship between pre-frail and fall risk and establish the frailty risk prediction nomogram. The nomogram was constructed based on the results of logistic regression. The model assessment relied on the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, calibration curves, and decision curve analysis.

Results: Fall incidence rate among pre-frail older adults within 6 months was 13.63%. The final fall risk prediction model identified that sex, history of falls in the past year, visual impairment, increased nocturia, and fear of falling are the most critical risk factors for falls in pre-frail older adults. The model exhibited good accuracy in the testing set, with an area under the ROC curve of 0.825 (95% confidence interval [0.736, 0.914]).

Conclusion: Pre-frail older adults have a higher incidence of falls. The logistic regression model constructed in this study shows promising predictive capabilities and can be used as a screening tool to identify pre-frail older adults at high risk of falls in clinical practice. We anticipate that this model will assist clinical nurses in enhancing the efficiency of fall prevention efforts and reducing the incidence of falls among pre-frail older adults. [(1), 29-39.].