临床外科杂志 ›› 2022, Vol. 30 ›› Issue (12): 1183-1186.doi: 10.3969/j.issn.1005-6483.2022.12.024

• 论著 • 上一篇    下一篇

多发性肋骨骨折病人术后肺通气障碍风险预测模型的建立

  

  1. 422800 湖南省邵阳市中心医院胸心外科 
  • 收稿日期:2022-01-07 接受日期:2022-01-07 出版日期:2022-12-20 发布日期:2023-01-20
  • 通讯作者: 刘晓飞,Email:liuxiaofei912@163.com

Establishment of risk prediction model for postoperative lung ventilation disorders in patients with multiple rib fractures

  1. YAO Fa,HE Shushuai,YANG Wensheng,et al
  • Received:2022-01-07 Accepted:2022-01-07 Online:2022-12-20 Published:2023-01-20

摘要: 目的 建立多发性肋骨骨折病人术后发生肺通气障碍风险预测模型。方法 2019年3月~2021年3月我院收治的多发性肋骨骨折病人150例,根据术后是否发生肺通气障碍将其分为两组,对照组52例,未发生通气障碍;研究组98例,发生通气障碍。收集两组病人就诊时间、糖尿病史、咳痰情况、肋骨骨折数量、吸烟史、肺部基础疾病、年龄、性别等临床资料进行单因素分析,根据单因素分析结果行多因素Logistic回归分析,并以R软件构建多发性肋骨骨折病人术后通气障碍风险预测模型,以ROC曲线及H-L拟合度检验评估模型。结果 单因素分析显示,多发性肋骨骨折病人术后发生肺通气障碍与年龄、肺部基础疾病、咳痰情况、就诊时间、糖尿病史、吸烟史、手术时间、骨折数量、身体质量指数(BMI)有关(P<0.05);进一步行多元Logistic回归分析显示,年龄>60岁、合并肺部基础疾病、咳痰差、吸烟史、就诊时间超过24小时、合并糖尿病、手术时间≥120分钟、骨折数量≥6根、BMI≥24kg/m2为影响多发性肋骨骨折术后肺通气障碍的独立危险因素(P<0.05)。在此基础上使用R软件构建多发性肋骨骨折病人术后发生肺通气障碍的风险预测模型,以ROC曲线下面积及HL偏差度检验模型,其中ROC曲线下面积为0.908,灵敏度、特异度分别为80.77、86.73。H-L偏差度结果为χ2=3.758,P=0.512。结论 年龄>60岁、合并肺部基础疾病、咳痰差、吸烟史、就诊时间超过24小时、合并糖尿病、手术时间≥120分钟、骨折数量≥6根、BMI≥24kg/m2为影响多发性肋骨骨折术后肺通气障碍的独立危险因素,以此建立的风险预测模型区分度及准确度较高,可为临床早期识别术后发生肺通气障碍风险提供有效依据。

关键词: 肺通气障碍, 多发性肋骨骨折, 风险预测模型, 影响因素

Abstract: Objective To establish a risk prediction model for postoperative pulmonary ventilation disorder in patients with multiple rib fractures.Methods From March 2019 to March 2021,a total of 150 patients with multiple rib fractures admitted to our hospital were selected and divided into a control group(patients without ventilatory disorders,52 cases) and a study group(patients with ventilatory disorders,98 cases) according to whether or not pulmonary ventilation disorders occurred after the operation.The clinical data of patients,such as visit time,diabetes,sputum expectoration,the number of rib fractures,smoking history,underlying lung diseases,age,gender and other clinical data,were collected for univariate analysis,multi-factor Logistic regression analysis was performed according to the results of single factor analysis,and R software was used to construct a risk prediction model for postoperative ventilatory disorder in patients with multiple rib fractures,the ROC curve and H-L fit degree were used to test and evaluate the model.Results Univariate analysis,the results showed that the occurrence of postoperative pulmonary ventilation disorder in patients with multiple rib fractures was related with age,underlying lung diseases,sputum expectoration,visit time,diabetes,smoking history,operation time,number of fractures,and body mass index(P<0.05);the multivariate Logistic regression analysis model,and the results showed that the age older than 60 years old,combined with underlying lung diseases,poor sputum expectoration,smoking history,visit time more than 24 h,combined with diabetes,operation time ≥120min,number of fracture ≥6,and body mass index ≥24kg/m2 were independent risk factors for lung ventilation disorder after multiple rib fracture operation(P<0.05).On this basis,R software was used to construct a risk prediction model for postoperative lung ventilation disorders in patients with multiple rib fractures,the area under the ROC curve and the HL deviation were used to test the model,the area under the ROC curve was 0.908,and the sensitivity and specificity were 80.77,86.73 respectively.The result of H-L deviation degree was  χ2=3.758,P=0.512.Conclusion Age older than 60 years old,combined with underlying lung diseases,poor sputum expectoration,smoking history,visit time more than 24 h,combined with diabetes,operation time ≥120 min,number of fractures ≥6,and body mass index ≥24 kg/m2 are independent risk factors that affect lung ventilation disorder after multiple rib fractures operation,the risk prediction model established on this basis has high discrimination and accuracy,which can provide an effective basis for early clinical identification of the risk of pulmonary ventilation disorder after operation.

Key words: pulmonary ventilation disorder, multiple rib fractures, risk prediction model, influencing factors 

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