What is the clinical evidence from this study?

Study design: Cohort. Population: Hip fracture (n=740). Intervention: Nomogram predictive model (BMI, CCI, HGB, ALB). Primary outcome: Postoperative shock (Shock Index ≥ 1.0).

What does this research mean for the field?

A nomogram incorporating body mass index, Charlson Comorbidity Index, hemoglobin, and albumin effectively predicts postoperative shock in elderly hip fracture patients. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Identify risk factors for postoperative shock and develop a predictive model for elderly patients undergoing hip fracture surgery.

May 1, 2026Clinical Interventions in Aging0 citationsOpen Access

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

XWXue WangNanjing Agricultural University ZCZhen CuiPeking University MTMengqi TongPeking University

Key Result

A nomogram incorporating body mass index, Charlson Comorbidity Index, hemoglobin, and albumin effectively predicted postoperative shock in elderly hip fracture patients, achieving an AUC of 0.834.

Key Points

Identify risk factors for postoperative shock and develop a predictive model for elderly patients undergoing hip fracture surgery.
Retrospective cohort study of elderly patients (> 65 years) admitted to ICU post-hip fracture surgery between 2020-2024.
Patients were stratified into shock and non-shock groups; data on demographics and preoperative laboratory parameters collected.
LASSO regression followed by multivariable logistic regression used to develop the predictive model.
740 patients included: 317 in training cohort, 136 in internal validation cohort, 287 in external validation cohort.
Key predictors identified: body mass index (BMI), Charlson Comorbidity Index (CCI), hemoglobin (HGB), albumin (ALB), leading to a nomogram.
Nomogram had AUC values of 0.834 in internal validation cohort and 0.801 in external validation cohort, indicating good discrimination.

Study Design

Type

Cohort (n=740)

Multicenter

Structured PICO

Does a nomogram based on preoperative BMI, CCI, hemoglobin, and albumin predict postoperative shock in elderly patients undergoing hip fracture surgery?

Population

740 elderly patients (>65 years) admitted to the ICU after unilateral hip fracture surgery under spinal anesthesia in a single center in Beijing, China, between 2020 and 2024.

Intervention

Development and validation of a predictive nomogram based on four preoperative variables: body mass index (BMI), Charlson Comorbidity Index (CCI), hemoglobin (HGB), and albumin (ALB).

Outcome

Postoperative shock, defined as a maximum postoperative Shock Index (SI) ≥ 1.0.surrogate

A nomogram incorporating preoperative BMI, CCI, hemoglobin, and albumin can effectively predict the risk of postoperative shock in elderly patients undergoing hip fracture surgery.

Limitations

Single-center retrospective study, causal relationships cannot be established
Shock index may not fully capture the complex pathophysiology of shock
Could not account for the potential impact of certain intraoperative factors, such as the level of anesthesia or the use of bone cement

Abstract

Xue Wang, Zhen Cui, Mengqi Tong, Miaomiao Yu, Ying Bai Department of Critical Care Medicine, Beijing Jishuitan Hospital Affiliated to Capital Medical University, Beijing, Peoples Republic of ChinaCorrespondence: Ying Bai, Department of Critical Care Medicine, Beijing Jishuitan Hospital Affiliated to Capital Medical University, Beijing, Peoples Republic of China, Email baiyingcn@sina.cnPurpose: 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.Keywords: Hip fracture, postoperative shock, shock index, nomogram analysis, internal and external validation

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