开放性手外伤术后感染的危险因素分析及其列线图预测模型构建-东南大学学报医学版

开放性手外伤术后感染的危险因素分析及其列线图预测模型构建

单位：秦皇岛市第一医院手足外科, 河北秦皇岛 066000

分类号：R658.2

出版年·卷·期（页码）：2025·44·第四期（548-555）

摘要：

目的:构建开放性手外伤术后感染的列线图预测模型。方法:收集2018年3月至2022年3月于本院术后感染的154例开放性手外伤患者的资料作为感染组,同时收集同期154例开放性手外伤术后无感染者的资料作为无感染组,比较两组差异并建立模型;另外收集2022年4月至2024年4月66例术后感染与66例无术后感染的开放性手外伤患者的资料对模型进行外部验证。从电子病历系统查找资料,多因素Logistic回归模型分析影响因素;R3.3.3及rms构建模型。受试者工作特征(ROC)曲线、校准曲线验证模型实用性。结果:感染组年龄、吸烟比例、糖尿病比例、外伤严重程度Ⅲ~Ⅳ级比例、VAS评分、受伤至手术时间、创面污染比例均大或高或长于未感染组,应用抗生素、负压引流比例均低于未感染组(P<0.05)。Logistic分析显示,年龄、吸烟、糖尿病、受伤至手术时间、创面污染、负压引流均是感染的影响因素(P＜0.05)。将Logistic分析筛选出的年龄、吸烟、糖尿病、受伤至手术时间、创面污染、负压引流引入R软件,建立预测感染的列线图模型。内部及外部验证结果均提示模型一致性、区分度较好。结论:年龄、吸烟、糖尿病、受伤至手术时间、创面污染、负压引流均是开放性手外伤患者术后感染的影响因素,以此6项因素为基础建立的列线图模型预测价值、实用性均较高。

Objective: To construct a column chart prediction model for postoperative infection in open hand trauma. Methods: Data from 154 patients with open hand trauma who were infected after surgery in our hospital between March 2018 and March 2022 were collected as infection group, and data from 154 patients without infection after open hand trauma were collected as the non-infection group. The differences between the two groups were compared and a model was established. In addition, data from 66 patients with postoperative infections and 66 patients with open hand trauma without postoperative infections between April 2022 and April 2024 were collected for external validation of the model. The data were retrieved from the electronic medical record system, and multiple factor Logistic regression model was used to analyze the influencing factors; R3.3.3 and rms were used to construct the model. Receiver operating characteristic(ROC) and calibration curve were used to validate the practicality of the model. Results: The infection group had higher age, proportion of smoking, proportion of diabetes, proportion of trauma severity grade Ⅲ-Ⅳ, VAS score, time from trauma to surgery, proportion of wound contamination, and lower antibiotics, and proportion of negative pressure drainage than non-infection group did(P<0.05). Logistic analysis showed that age, smoking, diabetes, the time from trauma to surgery, wound contamination, and negative pressure drainage were all the influencing factors of infection(P<0.05). Age, smoking, diabetes, time from trauma to surgery, wound contamination, negative pressure drainage screened by Logistic analysis were introduced into R software to establish a nomogram model to predict infection. Both internal and external validation results indicated good model consistency and discriminability. Conclusion: Age, smoking, diabetes, the time from trauma to surgery, wound contamination, and negative pressure drainage are all the influencing factors of postoperative infection in patients with open hand trauma. The nomogram model based on these six factors in this study has high predictive value and practicability.

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