Development and validation of a nomogram model for predicting postoperative nonunion in femoral shaft fractures.

IF 2.4 3区医学 Q2 ORTHOPEDICS

BMC Musculoskeletal Disorders Pub Date : 2025-09-30 DOI:10.1186/s12891-025-09064-2

Zhilong Hao, Yefan Zhang, Jiahao Zeng, Chao Yang, Haifeng Dang, Donglin Li, Junjun Fan

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引用次数: 0

Abstract

Objective: To identify independent risk factors for nonunion following femoral shaft fracture surgery and develop a clinically applicable nomogram model for personalized risk prediction.

Methods: A retrospective cohort study included 804 patients with femoral shaft fractures treated at Xijing Hospital (2014-2020). Patients were divided into development (n = 561) and validation (n = 243) cohorts. Variables were screened via LASSO regression, and a nomogram was constructed using multivariate logistic regression. Model performance was assessed using ROC curves, calibration plots, Hosmer-Lemeshow tests, and decision curve analysis (DCA).

Results: Five independent predictors of nonunion were identified: smoking. (OR = 3.094, 95% CI:1.790-5.350), high-energy injury (OR = 2.454, 95% CI:1.167-5.159), multiple injuries (OR = 2.897, 95% CI:1.580-5.312), internal fixation method (OR = 1), and fixation failure (OR = 3.437, 95% CI:1.51'. 9-7.778). The nomogram demonstrated excellent discrimination. (AUC = 0.828 in development, 0.835 in validation cohorts) and calibration (Hosmer-Lemeshow P = 0.463 and P = 0.858, respectively). DCA confirmed clinical utility at threshold probabilities > 15%.

Conclusion: This nomogram provides a practical tool for predicting nonunion risk in femoral shaft fractures, enabling early intervention for high-risk patients.

Abstract Image

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预测股骨干骨折术后骨不连的nomogram模型的建立与验证。

目的：探讨股骨干骨折术后骨不连的独立危险因素，建立临床适用的nomogram风险预测模型。方法：对西京医院2014-2020年收治的股骨干骨折患者804例进行回顾性队列研究。患者被分为发展组（n = 561）和验证组（n = 243）。采用LASSO回归筛选变量，采用多元逻辑回归构建nomogram。采用ROC曲线、校正图、Hosmer-Lemeshow检验和决策曲线分析（DCA）评估模型性能。结果：确定了五个独立的骨不连预测因素：吸烟。（OR = 3.094, 95% CI:1.790-5.350）、高能损伤（OR = 2.454, 95% CI:1.167-5.159）、多发损伤（OR = 2.897, 95% CI:1.580-5.312）、内固定方法（OR = 1）和固定失败（OR = 3.437, 95% CI:1.51）。9 - 7.778)。图显示出很好的辨别能力。（开发队列的AUC = 0.828，验证队列的AUC = 0.835）和校准（Hosmer-Lemeshow P = 0.463和P = 0.858）。DCA在阈值概率> 15%下确认临床效用。结论：该图为预测股骨干骨折不愈合风险提供了实用工具，有助于对高危患者进行早期干预。

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来源期刊

BMC Musculoskeletal Disorders 医学-风湿病学

CiteScore

3.80

自引率

8.70%

发文量

1017

审稿时长

3-6 weeks

期刊介绍： BMC Musculoskeletal Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of musculoskeletal disorders, as well as related molecular genetics, pathophysiology, and epidemiology. The scope of the Journal covers research into rheumatic diseases where the primary focus relates specifically to a component(s) of the musculoskeletal system.