Factors Influencing Length of Stay in Orthopedic Class I Incision Surgery: Development and Validation of a Nomogram Using 31,248 Patient Records

Objective:Prolonged length of stay (LOS) following orthopedic surgery places a significant strain on healthcare systems. However, effective tools for predicting LOS in patients undergoing clean (Class I) orthopedic surgery are lacking. This study aims to identify factors influencing length of stay in orthopedic Class I incision surgery and construct a predictive nomogram based on these factors. Methods:Retrospective analysis of patients undergoing orthopedic Class I incision surgery in Taihe Hospital from January 1, 2018 to October 31, 2023. Patients meeting the inclusion criteria were enrolled. Using prolonged length of stay (LOS > 7 days) as the primary outcome, we performed univariate analysis followed by binary logistic regression to identify risk factors. An individual nomogram was developed using R 4.3.3. Results:31,248 patients were ultimately included, with 20,419 (65.34%) patients demonstrating prolonged length of stay (LOS > 7 days). The results of binary logistic regression show that the independent risk factors for prolonged LOS (LOS > 7 days) in patients undergoing orthopedic Class I incision surgery were: age, surgical duration, surgical grade, American Society of Anesthesiologists' Physical Status Classification System (ASA PS), antibiotic use, combined antibiotic, and blood potassium(K), sodium (Na), magnesium(Mg) and calcium(Ca) concentrations. Validation using the receiver operating characteristic (ROC) curve showed that the nomogram had an area under the curve (AUC) of 0.846 (95% CI: 0.841-0.850), demonstrating good accuracy. The bootstrap method was used to repeatedly sample 1,000 times to verify the nomogram. The mean absolute error of the calibration curve was 0.003, indicating that the calibration curve fits well with the ideal curve. Decision curve analysis showed a significantly greater net benefit of the nomogram. Conclusion:The developed nomogram accurately predicts prolonged hospitalization risk in orthopedic patients with Class I incisions, integrating key determinants including age, surgical complexity, physiological status, and electrolyte levels. This tool demonstrates robust performance and offers tangible clinical utility for optimizing resource allocation and guiding personalized perioperative management.