A prediction model for persistent postoperative frailty in older patients undergoing elective orthopedic surgery.

Frailty is a medical syndrome with multiple causes and contributors that is characterized by diminished strength, endurance, and reduced physiologic function. It is highly prevalent among older orthopedic patients and significantly increases the risk of surgery and postoperative complications. Therefore, preoperative identification of high-risk groups for postoperative frailty is crucial for optimizing clinical decision making. This study aimed to explore the predictors of persistent postoperative frailty in older patients undergoing elective orthopedic surgery. Patients aged > 65 years who underwent elective orthopedic surgery between January 2020 and January 2022 were included in this single-center retrospective cohort study. Baseline characteristics, clinical data, laboratory findings, and frailty assessments were recorded. Persistent postoperative frailty was defined as a FRAILTY score >2 at the 3-, 6-, and 12-month follow-ups. Patients were randomly divided into training and validation cohorts at a ratio of 7:3 using stratified sampling. Least absolute shrinkage and selection operator and logistic regression were used for variable screening and analysis. A nomogram was constructed to visualize the predicted model. Receiver operating characteristic curves and area under the curve (AUC) values were used to assess the diagnostic accuracy. Calibration curves were performed to evaluate the calibration. A decision curve analysis was used to evaluate the clinical utility. Grip strength, gait speed, systemic immune-inflammation index, and the systemic inflammatory response index were identified as the significant predictors. The corresponding odds ratios were 2.467, 1.214, 1.809, and 1.743, respectively. A nomogram was used to visualize the logistic model, which achieved an AUC value of 0.772 (with a sensitivity of 77.9% and specificity of 64.3%) in the training cohort and an AUC value of 0.788 (with a sensitivity of 86.1% and specificity of 82.2%) in the validation cohort. Calibration curves indicating the acceptable agreements between the nomogram-predicted probability and the actual probability of persistent postoperative frailty. The decision curve analysis curves showed that the prediction model consistently outperformed the "treat-all" and "treat-none" strategies. The prediction model incorporating grip strength, gait speed, systemic immune-inflammation index, and systemic inflammatory response index enables early perioperative identification of older patients undergoing elective orthopedic surgery who are at high risk for developing persistent postoperative frailty.