Construction and validation of a predictive model for the risk of prolonged preoperative waiting time in patients with intertrochanteric fractures.

BACKGROUND

Intertrochanteric fractures are one of the most common types of hip fractures, with delayed surgical treatment beyond 48 h associated with increased postoperative complications and mortality, especially in older adults. This study aimed to develop a predictive model for delayed preoperative waiting times in intertrochanteric fracture cases, based on previous research, to offer a valuable reference for clinical decision-making.

METHODS

A retrospective analysis was conducted on 1,116 patients with intertrochanteric fractures admitted to the Affiliated Hospital of Shandong University of Traditional Chinese Medicine for internal fixation surgery from January 2017 to January 2024. Patient demographic data and clinical examination results were collected. A logistic regression model was used to construct a predictive model, which was then visualized through a nomogram. The model's performance was subsequently validated.

RESULTS

The predictive model developed from 728 patients in the training cohort, identified key predictors, including age, sex, lower extremity deep vein thrombosis, injury location, and biochemical markers. The model demonstrated strong discriminative ability, with an area under the receiver operating characteristic curve of 0.749 (95% confidence interval: 0.621-0.801) for the training set, and 0.745 in the validation set. Calibration curves indicated that the predicted risk of surgical delay closely aligned with observed outcomes. Furthermore, decision curve analysis verified the model's clinical utility, demonstrating its effectiveness in guiding treatment decisions.

CONCLUSION

The nomogram model developed in this study provides a reliable tool for predicting delayed surgical intervention in patients with intertrochanteric femur fractures. It offers clinicians a valuable reference to anticipate delays in surgical treatment and aids in the formulation of more timely and appropriate treatment strategies, potentially improving patient outcomes.