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Development and Validation of a Predictive Model for the Risk of Postoperative Frailty in Elderly Patients With
Jiahui Yu1,2, Panpan Xu1,2, Linjing Du3
1Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, China.
Objective:
This study aimed to identify the key predictors of frailty in elderly patients with benign primary brain tumors (PBTs) and develop and validate a nomogram for predicting postoperative frailty in these patients, which integrates disease characteristics, individual factors, psychosocial indicators, and multidimensional health parameters.
Methods:
A cohort of 313 elderly patients with benign PBTs was recruited from a tertiary hospital in Nantong, China, and was then randomly split into a training set (n = 219) and a validation set (n = 94). Binary logistic regression analysis was used to identify risk factors for frailty in the training set. LASSO regression identified five key predictors, which were then used to construct a predictive model. The model's performance was evaluated in terms of discriminative ability, calibration, and clinical utility using ROC curves, calibration plots, and decision curve analysis (DCA).
Results:
In this study of 313 patients with benign PBTs, the five factors (KPS, Hb, MDASI-BT, SSRS, and CD-RISC) were identified as independent predictors of frailty in the elderly patients. In the training set as well as the validation set, the AUC was 0.936 (95% CI: 0.90-0.97) and 0.939 (95% CI: 0.89-0.98). DCA further verified the favorable predictive efficacy of the model.
Conclusion:
We developed a reliable predictive model to predict frailty in elderly patients with benign PBTs after undergoing intracranial tumor resection. This model is intended to help clinical staff assess frailty risk and screen high-risk patients.
