Risk Factor Analysis and Prediction Model Construction for Postoperative Delirium in Elderly Patients with Hip Fracture
DOI:
https://doi.org/10.18063/apm.v10i2.887Keywords:
Hip fractures in the elderly, Postoperative delirium, Risk factors, Prediction modelAbstract
Objective: To analyze the independent risk factors for postoperative delirium (POD) in elderly patients with hip fracture and construct and validate a risk prediction model. Methods: A total of 134 elderly patients with hip fractures aged over 60 years old who underwent elective surgical treatment in the hospital from September 2022 to September 2023 were selected as the study subjects. The occurrence of POD was evaluated using the Confusion Assessment Method (CAM) scale from 1 to 5 days after surgery, and the patients were divided into the POD group (n = 35) and the non-POD group (n = 95) based on the diagnosis. General information was compared between the two groups, and logistic regression analysis was used to investigate the independent risk factors for POD and construct a nomogram model. The predictive value of the nomogram model for POD was tested by drawing a Receiver Operating Characteristic (ROC) curve. Results: There were significant differences in age, comorbid diabetes, comorbid stroke, preoperative albumin, and intraoperative blood loss between the two groups (P < 0.05). Multivariate logistic regression analysis revealed that increasing age, comorbid diabetes, comorbid stroke, and low preoperative albumin levels were independent risk factors for POD. The ROC curve showed that the AUC of the nomogram model for predicting POD in elderly patients with hip fracture was 0.896 (95% CI: 0.841–0.951, P < 0.001), with specificity, sensitivity, and Youden’s index of 0.989, 0.907, and 0.893, respectively. Conclusion: Increasing age, comorbid diabetes, history of stroke, and preoperative hypoalbuminemia are independent risk factors for POD in elderly patients with hip fracture. The nomogram prediction model constructed based on these risk factors demonstrates good discriminatory performance in clinical risk stratification. It is suggested that precise intervention strategies should be implemented for high-risk populations in clinical practice to optimize perioperative management.
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