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开发和验证老年髋部骨折患者术后严重并发症风险预测模型。

Development and validation of a risk prediction model for severe postoperative complications in elderly patients with hip fracture.

机构信息

Department of Orthopedics, Yongchuan Hospital of Chongqing Medical University, Yongchuan, Chongqing, China.

Department of Geriatrics, Yongchuan Hospital of Chongqing Medical University, Yongchuan, Chongqing, China.

出版信息

PLoS One. 2024 Nov 13;19(11):e0310416. doi: 10.1371/journal.pone.0310416. eCollection 2024.

Abstract

OBJECTIVE

This study aimed to investigate risk factors associated with severe postoperative complications following hip fracture surgery in elderly patients and to develop a nomogram-based risk prediction model for these complications.

METHODS

A total of 627 elderly patients with hip fractures treated at Yongchuan Hospital of Chongqing Medical University from January 2015 to April 2024 were collected. 439 patients were assigned to the training cohort for model development, and 188 to the validation cohort for model assessment. The training cohort was stratified based on the presence or absence of severe complications. We employed LASSO regression, as well as univariate and multivariate logistic regression analyses, to identify significant factors. A nomogram was constructed based on the outcomes of the multivariate regression. The model's discriminative ability was assessed using the area under the receiver operating characteristic curve (AUC), while calibration plots and decision curve analysis (DCA) evaluated its calibration and stability. Internal validation was performed using the validation cohort.

RESULTS

Out of the 627 patients, 118 (18.82%) experienced severe postoperative complications. Both LASSO regression and multivariate logistic analysis identified the modified 5-item frailty index (mFI-5) and the preoperative C-reactive protein to albumin ratio (CAR) as significant predictors of severe complications. The nomogram model, derived from the multivariate analysis, exhibited strong discriminative ability, with an AUC of 0.963 (95% CI: 0.946-0.980) for the training cohort and 0.963 (95% CI: 0.938-0.988) for the validation cohort. Calibration plots demonstrated excellent agreement between the nomogram's predictions and actual outcomes. Decision curve analysis (DCA) indicated that the model provided clinical utility across all patient scenarios. These findings were consistent in the validation cohort.

CONCLUSIONS

Both the mFI-5 and CAR are predictive factors for severe postoperative complications in elderly patients undergoing hip fracture surgery.

摘要

目的

本研究旨在探讨老年髋部骨折患者术后严重并发症的相关危险因素,并建立基于列线图的风险预测模型。

方法

收集 2015 年 1 月至 2024 年 4 月在重庆医科大学附属永川医院治疗的 627 例老年髋部骨折患者。将 439 例患者分配到训练队列中进行模型开发,188 例分配到验证队列中进行模型评估。根据是否存在严重并发症对训练队列进行分层。采用 LASSO 回归、单因素和多因素 logistic 回归分析来识别显著因素。根据多变量回归的结果构建列线图。通过受试者工作特征曲线(ROC)下面积(AUC)评估模型的区分能力,通过校准图和决策曲线分析(DCA)评估其校准和稳定性。使用验证队列进行内部验证。

结果

627 例患者中,118 例(18.82%)发生术后严重并发症。LASSO 回归和多因素 logistic 分析均确定改良 5 项衰弱指数(mFI-5)和术前 C 反应蛋白与白蛋白比值(CAR)是严重并发症的显著预测因素。基于多变量分析的列线图模型具有较强的区分能力,在训练队列中的 AUC 为 0.963(95%CI:0.946-0.980),在验证队列中的 AUC 为 0.963(95%CI:0.938-0.988)。校准图显示,列线图预测结果与实际结果之间具有良好的一致性。决策曲线分析(DCA)表明,该模型在所有患者场景下均具有临床应用价值。这些结果在验证队列中也是一致的。

结论

mFI-5 和 CAR 都是老年髋部骨折患者术后严重并发症的预测因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be6c/11560009/272a685d0765/pone.0310416.g001.jpg

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