Multivariate Analysis of Shock Risk and Model Development and Validation in Elderly Patients Following Hip Fracture Surgery in the Intensive Care Unit (ICU): A Retrospective Cohort Study.

Abstract

Purpose: This study aimed to identify risk factors for postoperative shock and develop and validate a predictive model based on preoperative variables in elderly patients undergoing hip fracture surgery.

Patients and methods: We conducted a retrospective cohort study of elderly patients (>65 years) admitted to the ICU after hip fracture surgery in a single center between 2020 and 2024. Patients were stratified into shock (defined as a Shock Index ≥ 1.0) and non-shock groups. Data on demographics, comorbidities, and preoperative laboratory parameters were collected. Patients from 2020-2022 constituted the development cohort, which was randomly divided into training and internal validation sets at a ratio of 7:3, while patients from 2023-2024 formed the external validation cohort. Least absolute shrinkage and selection operator (LASSO) regression was used to identify candidate predictors, followed by multivariable logistic regression to construct the predictive model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA).

Results: A total of 740 patients were included, with 317 in the training cohort, 136 in the internal validation cohort, and 287 in the external validation cohort. LASSO regression identified four key predictors: body mass index (BMI), Charlson Comorbidity Index (CCI), hemoglobin (HGB), and albumin (ALB). These variables were incorporated into a nomogram. The nomogram demonstrated good discrimination and clinical utility, with AUC values of approximately 0.834 in the internal validation cohort and 0.801 in the external validation cohort. Decision curve analysis further supported its potential clinical benefit.

Conclusion: We developed and validated a practical nomogram that effectively predicts the risk of postoperative shock in elderly hip fracture patients using four preoperative parameters. This model may assist clinicians in early risk stratification and perioperative monitoring of elderly hip fracture patients.