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OBJECTIVE: 30-day readmission after hip fracture surgery in the elderly is common and costly. A predictive tool to identify high-risk patients could significantly improve outcomes. This study aims to develop and validate a risk nomogram for 30-day readmission after hip fracture surgery in geriatric patients. PATIENTS AND METHODS: We retrospectively analyzed 1,249 geriatric hip fracture patients (≥60 years) undergoing surgery at Dandong Central Hospital from October 2011 to October 2023. Using a 7:3 ratio, patients were randomly divided into training (n=877) and validation (n=372) sets. Independent risk factors for 30-day readmission were identified using LASSO regression and logistic regression in the training set. A nomogram was constructed using the identified predictors. Finally, the C-index, ROC curve, calibration curve, and decision curve analysis were used to validate the model in the training and validation sets respectively. RESULTS: The nomogram was developed based on the 8 predictors of age, prior stroke, chronic liver disease, treatment, uric acid (UA), total protein (TP), albumin (ALB), and pneumonia that were found to be independently associated with 30-day readmission. The nomogram showed good discrimination with a C-index of 0.88 in the training set and 0.84 in the validation set. Calibration curves exhibited good agreement between predicted and observed outcomes. Decision curve analysis demonstrated clinical utility. CONCLUSIONS: We developed and validated a nomogram incorporating eight clinical variables to accurately predict the individualized risk of 30-day readmission after hip fracture surgery in elderly patients. The model demonstrated favorable discrimination, calibration, and clinical utility. It can help to identify high-risk patients needing additional interventions to prevent avoidable hospital readmissions. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study aims to develop and validate a risk nomogram for urinary tract infections (UTIs) in geriatric patients with hip fractures. PATIENTS AND METHODS: A total of 900 geriatric patients who underwent hip fracture surgery at Dandong Central Hospital between June 2017 and June 2023 were systematically collected. The cohort was randomly divided into a training set (70%, n=632) and a validation set (30%, n=268) for model development and validation, respectively. Univariate and multivariate logistic regression analyses were conducted to identify the independent risk factors associated with UTIs. Based on the results of the multivariate analysis, a UTI nomogram prediction model was developed and evaluated in the training and validation sets using the C-index, ROC curve, calibration curve, and decision curve analysis to assess discrimination, calibration, and clinical utility, respectively. RESULTS: Out of the 900 participants, 24.6% were diagnosed with UTIs. The nomogram was developed based on 9 predictors that were found to be independently associated with UTI. The area under the curve (AUC) for predicting UTI in geriatric patients with hip fractures was 0.829 in the training set and 0.803 in the validation set. Following internal verification, the modified C-index remained at 0.829. Furthermore, the nomogram's calibration plot and decision curve analysis demonstrated good performance in both the training and validation sets. CONCLUSIONS: The established and validated nomogram provides a reliable and convenient tool for predicting UTI risk in geriatric patients with hip fractures. This model facilitates the early identification of high-risk patients and offers guidance for implementing targeted preventive interventions. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study aimed to develop and validate a risk nomogram for preoperative proximal and distal deep vein thrombosis (DVT) in geriatric patients with hip fractures. PATIENTS AND METHODS: The 970 collected geriatric hip fracture patients were randomly divided into a training set (70%, n=682) and a validation set (30%, n=288). Multivariate logistic regression analyses were used to optimize the predictive risk variables for proximal and distal preoperative lower extremity DVT in the training set, respectively, and the selected variables were finally incorporated to establish preoperative DVT nomogram prediction models. Receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) were performed to validate the nomograms in the training and validation sets, respectively. RESULTS: Among the 970 patients, 125 (12.88%) were diagnosed with preoperative DVT. The area under the curve (AUC) for predicting preoperative proximal DVT was 0.888 in the training and 0.792 in the validation sets. The AUC for predicting preoperative distal DVT was 0.907 in the training and 0.790 in the validation sets. The calibration plots and decision curve analysis for preoperative proximal DVT performed well in the training set and slightly worse in the validation set. The calibration plots and decision curve analysis for preoperative distal DVT performed well in both the training and validation sets. CONCLUSIONS: To construct nomograms for predicting the risk of proximal and distal preoperative lower extremity DVT in geriatric hip fracture patients. For patients at high risk, as assessed by this model, clinicians should intervene and treat them promptly before surgery. [ABSTRACT FROM AUTHOR]