中国组织工程研究 ›› 2024, Vol. 28 ›› Issue (36): 5785-5792.doi: 10.12307/2024.681

• 数字化骨科Digital orthopedics • 上一篇    下一篇

老年髋部骨折术后并发肺部感染:影响因素及风险预测列线图模型构建

王浩阗1,吴  毛1,2,杨俊锋2,邵  阳2,李绍烁2,尹  恒1,2,於  浩2,汪国澎1,唐  志1,周铖炜1,王建伟1,2   

  1. 1南京中医药大学,江苏省南京市   210023;2南京中医药大学附属无锡市中医医院骨伤科,江苏省无锡市   214071
  • 收稿日期:2023-09-13 接受日期:2023-11-17 出版日期:2024-12-28 发布日期:2024-02-27
  • 通讯作者: 王建伟,博士,主任中医师,南京中医药大学,江苏省南京市 210023;南京中医药大学附属无锡市中医医院骨伤科,江苏省无锡市 214071
  • 作者简介:王浩阗,男,1997年生,山东省日照市人,汉族,南京中医药大学在读硕士,主要从事中医治疗骨关节病研究、中医药防治骨质疏松症方面的研究。
  • 基金资助:
    国家自然科学基金(82174400),项目负责人:吴毛;“双百”拔尖人才项目(BJ2020066),项目负责人:杨俊锋;江苏省中医药科技发展计划项目 (YB2020042),项目负责人:邵阳;无锡市科学技术局医疗卫生指导性项目 (SKJJZD19),项目负责人:邵阳;无锡市卫生健康委科研项目(Q201945),项目负责人:邵阳

Postoperative pulmonary infection in elderly patients with hip fracture: construction of a nomogram model for influencing factors and risk prediction

Wang Haotian1, Wu Mao1, 2, Yang Junfeng2, Shao Yang2, Li Shaoshuo2, Yin Heng1, 2, Yu Hao2, Wang Guopeng1, Tang Zhi1, Zhou Chengwei1, Wang Jianwei1, 2   

  1. 1Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China; 2Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, Jiangsu Province, China
  • Received:2023-09-13 Accepted:2023-11-17 Online:2024-12-28 Published:2024-02-27
  • Contact: Wang Jianwei, MD, Chief physician, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China; Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, Jiangsu Province, China
  • About author:Wang Haotian, Master candidate, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China
  • Supported by:
    National Natural Science Foundation of China, No. 82174400 (to WM); The “Double Hundred” Top Talent Project, No. BJ2020066 (to YJF); Jiangsu Traditional Chinese Medicine Science and Technology Development Plan Project, No. YB2020042 (to SY); Medical and Health Guidance Project of Wuxi Municipal Bureau of Science and Technology, No. SKJJZD19 (to SY); Scientific Research Project of Wuxi Municipal Health and Health Commission, No. Q201945 (to SY)

摘要:


文题释义:

列线图模型:是一种用于预测特定事件概率或风险的统计工具,结合多个影响因素,以提供个体化的估计。该模型采用图形方式呈现,包括直观线条、数字和标度,使医生和研究人员能够快速估算风险,无需复杂计算。在医学和生物统计学中广泛应用,用于预测疾病风险、治疗效果和生存率。通过综合考虑多个因素,列线图模型提供更准确、个性化的预测,帮助医生更好地理解患者的潜在风险,做出更明智的临床决策。
决策曲线:又称DCA决策曲线,是一种在医学和生物统计学中用于评估诊断或预测模型实用性的工具。该曲线显示了不同概率阈值下,使用模型进行决策相对于不进行决策或采用其他方法的净获益情况。DCA曲线分析有助于评估模型的临床实用性,帮助医生和研究人员确定在不同概率阈值下是否应用模型来做出决策。


背景:建立髋部骨折术后肺部感染列线图预测模型,采取早期干预措施,对于提高患者的生活质量及降低医疗成本至关重要。

目的:构建髋部骨折老年患者术后肺部感染的列线图风险预测模型,为可行性预防和早期干预提供理论依据。
方法:回顾性分析2020年1-10月于南京中医药大学附属无锡市中医医院行手术治疗的305例老年髋部骨折患者(训练集)的病例资料,采用单因素和多因素Logistics回归分析,Hosmer-Lemeshow拟合优度检验,通过受试者工作特征曲线分析各独立危险因素和联合模型对术后肺部感染的预测效能,运用R Studio软件中的glmnet、pROC、rms等工具构建了一个列线图模型,用于预测老年髋部骨折患者术后肺部感染的风险,并进一步绘制校准曲线,验证列线图模型的预测准确性。在对2022年11月至2023年3月同院行手术治疗的133例老年髋部骨折患者(验证集)进行受试者工作特征曲线、校准曲线以及决策曲线的分析后,进一步评估列线图模型的预测性能。 

结果与结论:①此组髋部骨折老年患者术后肺部感染率为9.18%(28/305);②单因素和多因素分析、森林图显示,术前住院天数、白细胞值、超敏C-反应蛋白、血清钠水平是独立危险因素(P < 0.05),Hosmer-Lemeshow拟合优度检验示拟合良好(χ2=4.57、P=0.803);对以上各独立危险因素及其联合模型进行受试者工作特征曲线分析,各独立危险因素、联合模型均区分度良好,有统计学意义(P < 0.05);③图形校准法、C指数、决策曲线验证列线图预测模型,预测校准曲线位于标准曲线和可接受线之间,该列线图模型预测风险与实际发生风险一致性良好;④验证集运用受试者工作特征曲线、图形校准法、决策曲线验证预测模型,显示预测结果与临床实际相比有良好的一致性,表明该模型的拟合度较好;构建的髋部骨折老年患者术后肺部感染的列线图风险预测模型具有较好的预测效能,利用列线图风险预测模型可筛选出高危人群,为早期干预提供理论依据。

https://orcid.org/0009-0000-0765-0506 (王浩阗) 

中国组织工程研究杂志出版内容重点:人工关节;骨植入物;脊柱;骨折;内固定;数字化骨科;组织工程

关键词: 髋部骨折, 肺部感染, 预测模型, 列线图, 危险因素, R语言

Abstract: BACKGROUND: Establishing a nomogram prediction model for postoperative pulmonary infection in hip fractures and taking early intervention measures is crucial for improving patients’ quality of life and reducing medical costs. 
OBJECTIVE: To construct a nomogram risk prediction model of postoperative pulmonary infection in elderly patients with hip fracture, and provide theoretical basis for feasible prevention and early intervention. 
METHODS: Case data of 305 elderly patients with hip fractures who underwent surgical treatment at Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine between January and October 2020 (training set) were retrospectively analyzed. Using univariate and multivariate logistic regression analysis and Hosmer-Lemeshow goodness of fit test, receiver operating characteristic curve was utilized to analyze the diagnostic predictive efficacy of independent risk factors and joint models for postoperative pulmonary infections. Tools glmnet, pROC, and rms in R Studio software were applied to construct a nomogram model for predicting the risk of postoperative pulmonary infection in elderly patients with hip fractures, and calibration curves were further drawn to verify the predictive ability of the nomogram model. Receiver operating characteristic curves, calibration curves, and decision curves were analyzed for 133 elderly patients with hip fractures (validation set) receiving surgery at the same hospital from November 2022 to March 2023 to further predict the predictive ability of the nomogram model. 
RESULTS AND CONCLUSION: (1) The postoperative pulmonary infection rate in elderly patients with hip fractures in this group was 9.18% (28/305). (2) Single factor and multivariate analysis, as well as forest plots, showed that preoperative hospitalization days, leukocyte count, hypersensitive C-reactive protein, and serum sodium levels were independent risk factors (P < 0.05). The Hosmer-Lemeshow goodness of fit test showed good fit (χ2=4.57, P=0.803). Receiver operating characteristic curve analysis was conducted on the independent risk factors and their joint models mentioned above, and the differentiation of each independent risk factor and joint model was good, with statistical significance (P < 0.05). (3) The graphical calibration method, C-index, and decision curve were used to validate the nomogram prediction model. The predicted calibration curve was located between the standard curve and the acceptable line, and the predicted risk of the nomogram model was consistent with the actual risk. (4) The validation set used receiver operating characteristic curve, graphic calibration method, and decision curve to validate the prediction model. The results showed good consistency with clinical practice, indicating that the model had a good fit. The nomogram risk prediction model constructed for postoperative pulmonary infection in elderly patients with hip fractures has good predictive performance. The use of the nomogram risk prediction model can screen high-risk populations and provide a theoretical basis for early intervention.

Key words: hip fracture, lung infection, prediction model, nomogram, risk factor, R programming language

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