Falls are among the most common safety concerns in people with visual impairment and can lead to serious consequences, including fractures, prolonged hospitalization, and even death. Patients with glaucoma are at increased risk of falls due to visual field loss, impaired motor coordination, and declines in cognitive function compared with the general population. Resting pulse rate is an easily obtainable measure in routine clinical practice; however, its contribution to fall risk prediction in patients with glaucoma has not been sufficiently investigated. To address this knowledge gap, we developed and compared multiple predictive approaches by incorporating a broad range of fall-related variables into prediction models, and we used explainable machine learning to quantify the contribution of resting pulse rate to fall risk prediction in glaucoma. In doing so, we aimed to explore the potential contribution of resting pulse rate as one of the model features in fall risk estimation, rather than as a standalone glaucoma-specific ophthalmic indicator. Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). We included 249 participants with self-reported physician-diagnosed glaucoma who had no history of falls at the 2015 baseline survey and completed follow-up in 2018. The outcome was the occurrence of any fall between 2015 and 2018. To further characterize baseline differences, we also included 12,297 participants without glaucoma and without a history of falls at the 2015 baseline survey for comparative analyses.Candidate predictors comprised demographic characteristics, clinical comorbidities, medication use, self-reported vision status, and relevant laboratory measures. Self-reported near and distance vision were treated as limited visual functional information available in the database and were not considered equivalent to objective glaucoma-specific ophthalmic indicators. To compare machine learning models with a conventional statistical approach, we developed a logistic regression (LR) baseline model and trained six machine learning models: random forest, XGBoost, gradient boosting decision tree (GBDT), support vector machine (SVM), k-nearest neighbors (KNN), and AdaBoost. Feature selection was performed in the training set using recursive feature elimination with 5-fold cross-validation; within each fold, feature selection was conducted using only the fold-specific training subset and evaluated on the corresponding validation subset to reduce the risk of information leakage and overly optimistic performance estimates. After determining the final feature subset, hyperparameters were tuned and models were fitted using cross-validation within the training set. Model stability was assessed using 1,000 bootstrap resamples of the training set, and we reported the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals, accuracy, and F1 score. Calibration curves and decision curve analysis were used to evaluate calibration and clinical net benefit. Finally, SHAP was applied to interpret the best-performing XGBoost model. A total of 249 eligible participants with glaucoma were included. During follow-up, 36 participants reported at least one fall, yielding a fall incidence of 14.46%. In contrast, among the 12,297 non-glaucoma participants included for baseline comparison, 873 reported at least one fall (7.1%; P < 0.001).In model development, the conventional logistic regression model showed the lowest discriminative performance, with an AUC of 0.676 (95% CI, 0.628–0.724). The XGBoost model achieved the best performance, with an AUC of 0.851 (95% CI, 0.812–0.886). Decision curve analysis indicated that, within a threshold probability range of 51.5% to 67.5%, the XGBoost model provided greater net benefit than the other machine learning models. SHAP-based feature importance further identified key predictors of falls in patients with glaucoma, with resting pulse rate ranking among the top contributing features in the XGBoost model. In this study, the XGBoost model demonstrated the best performance for estimating fall risk among participants with self-reported glaucoma. SHAP analyses indicated that resting pulse rate, creatinine, age, blood urea nitrogen, frailty status, and height made relatively large contributions within the final model. Given the absence of objective ophthalmic parameters, these findings should be regarded as exploratory and interpreted cautiously. Resting pulse rate may provide supplementary information within model-based fall risk estimation, but it should not be interpreted as a standalone glaucoma-specific indicator or as evidence of causality.
Zh et al. (Sun,) studied this question.
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