Abstract
BACKGROUND: Acute kidney injury (AKI), characterized by sudden impairment of kidney function, is an uncommon complication following hip fracture surgery that is associated with increased morbidity and mortality. We constructed a nomogram to stratify patients according to risk of AKI after hip fracture surgery to guide clinicians in the implementation of timely interventions. METHODS: Patients who received hip fracture surgery from January 2015 to December 2021 were retrospectively identified and divided into a training set (n=448, surgery from January 2015 to December 2019) and a validation set (n=200, surgery from January 2020 to December 2021). Univariate and multivariate logistic regression were used to identify risk factors for AKI after surgery in the training set. A nomogram was constructed based the risk factors for AKI, and was evaluated by receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). RESULTS: The mean age was 82.0±6.22 years-old and the prevalence of post-surgical AKI was 13.3%. Age, American Society of Anesthesiologists (ASA) score, the preexistence of chronic kidney disease (CKD), cemented surgery and the decrease of hemoglobin on the first day after surgery were identified as independent risk factors of AKI after hip fracture surgery, and a predictive nomogram was established based on the multivariable model. The predictive nomogram had good discrimination ability (training set: AUC: 0.784, 95% CI: 0.720-0.848; validation set: AUC: 0.804, 95% CI: 0.704-0.903), and showed good validation ability and clinical usefulness based on a calibration plot and decision curve analysis. CONCLUSION: A nomogram that incorporated five risk factors including age, ASA score, preexisting CKD, cemented surgery and the decrease of hemoglobin on the first day after surgery had good predictive performance and discrimination. Use of our results for early stratification and intervention has the potential to improve the outcomes of patients receiving hip fracture surgery. Future large, multicenter cohorts are needed to verify the model's performance.