A nomogram to predict postoperative infection for older hip fracture patients

Introduction Postoperative infection is one of the most common postoperative complications in hip fracture surgery. It is related with increased morbidity and mortality. This study aimed at developing a nomogram to predict the individual probability of postoperative infection to facilitate perioperative decision-making. Materials and Methods In this retrospective study, we included all patients over 65 years old admitted for hip fracture in West China Hospital of Sichuan University from 1 January 2015 to 31 December 2019. Univariate and multivariate logistic regression analyses were used to identify significant predictors. We used all-subsets regression to screen an optimal model, and visualized the model through drawing nomogram. To evaluate the model performance, we applied receiver operating characteristic curve and calibration curve. Results We enrolled 677 older patients. 136 (20.1%) patients developed postoperative infection during hospitalization. Variables retained in the final model were albumin [odds ratio (OR) 0.90, 95% confidence interval (CI) 0.84-0.96], cholesterol (OR 1.49, 95% CI 1.04-2.15), blood phosphorus (OR 0.16, 95% CI 0.05-0.48), high-density lipoprotein (OR 0.42, 95% CI 0.19-0.89), surgery type (OR 2.27, 95% CI 1.35-3.90), smoking (OR 1.95, 95% CI 1.02-3.66), American Society of Anesthesiologists classification [class III (OR 1.02, 95% CI 0.55-1.93); class IV (OR 1.93, 95% CI 0.76-4.82)], and chronic pulmonary disease (OR 2.16, 95% CI 1.25-3.68). The C-index of the nomogram was 0.752 (95% CI 0.697-0.806). Calibration curve showed good agreement between predicted value and observed outcome. In the validation group, our nomogram showed an area under the receiver operating characteristic curve of 0.723 (95% CI 0.639-0.807). Conclusion Our nomogram showed good discrimination ability in predicting individual probability of postoperative infection among older patients with hip fracture surgery. The nomogram could help clinicians identify patients at high risk of postoperative infection before surgery.