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中华实验和临床感染病杂志(电子版) ›› 2024, Vol. 18 ›› Issue (03) : 149 -155. doi: 10.3877/cma.j.issn.1674-1358.2024.03.004

论著

后路腰椎椎间融合术后手术部位感染风险预测模型的构建
童芷芹1, 刘艳1, 潘亚娟1,()   
  1. 1. 226611 海安市,海安市人民医院手术室
  • 收稿日期:2023-08-15 出版日期:2024-06-15
  • 通信作者: 潘亚娟
  • 基金资助:
    江苏省"六大人才高峰"高层次人才选拔培养资助项目(No. WSW-277)

Construction of a risk prediction model for surgical site infection after posterior lumbar interbody fusion

Zhiqin Tong1, Yan Liu1, Yajuan Pan1,()   

  1. 1. Department of Operation Room, Hai’an People’s Hospital, Hai’an 226611, China
  • Received:2023-08-15 Published:2024-06-15
  • Corresponding author: Yajuan Pan
引用本文:

童芷芹, 刘艳, 潘亚娟. 后路腰椎椎间融合术后手术部位感染风险预测模型的构建[J]. 中华实验和临床感染病杂志(电子版), 2024, 18(03): 149-155.

Zhiqin Tong, Yan Liu, Yajuan Pan. Construction of a risk prediction model for surgical site infection after posterior lumbar interbody fusion[J]. Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Edition), 2024, 18(03): 149-155.

目的

分析后路腰椎椎间融合术(PLIF)后手术部位感染(SSI)的危险因素,并构建定量风险预测模型指导临床应用。

方法

回顾性总结2019年1月至2023年1月入海安市人民医院择期行PLIF患者共1 333例为研究对象,根据术后30 d内是否发生SSI将其分为SSI组(44例)和无SSI组(1 289例);收集SSI组患者临床标本进行病原菌分离和鉴定,分别采用单因素和多因素Logistic回归分析筛选SSI的危险因素,并构建列线图模型;采用受试者工作曲线(ROC)分析预测效能。

结果

入组研究患者中SSI的发生率为3.3%(44/1 333);共分离病原菌38株,主要为肺炎克雷伯菌16株、大肠埃希菌13株、铜绿假单胞菌7株以及其他2株。与无SSI组相比,SSI组患者年龄增大,尿路感染和多节段手术(≥ 2个节段)增多,皮下脂肪厚度、腰多裂肌(LMM)脂肪浸润、手术时间、引流时间和引流管数量增加,差异均有统计学意义(P均< 0.05)。单因素Logistic回归分析显示,年龄≥ 60岁、多节段手术(≥ 2个节段)、皮下脂肪厚度(≥ 14.0 mm)、LMM脂肪浸润(≥ 22.5%)、手术时间(≥ 190.0 min)和引流时间(≥ 2 d)均为SSI的风险因素(P均< 0.05);多因素Logistic回归分析显示,年龄≥ 60岁、多节段手术(≥ 2个节段)、皮下脂肪厚度(≥ 14.0 mm)、LMM脂肪浸润(≥ 22.5%)、手术时间(≥ 190 min)和引流时间(≥ 2 d)均为SSI的危险因素(P均< 0.05)。ROC分析显示,列线图预测SSI的AUC值显著高于Logistic回归模型(0.845 vs. 0.769:Z = 6.325、P < 0.001)。

结论

PLIF有一定的SSI发生风险,体重分布指标如皮下脂肪厚度和LMM脂肪浸润有可能是影响SSI发生的重要潜在危险因素,通过构建定量列线图预测模型有助于指导临床早期识别SSI高危人群,并进行针对性干预以改善临床预后。

Objective

To investigate the risk factors of surgical site infection (SSI) after posterior lumbar interbody fusion (PLIF), and construct a quantitative risk prediction model to guide clinical application.

Methods

Total of 1 333 patients admitted to Haian People’s Hospital from January 2019 to January 2023 for elective PLIF were selected, and were divided into SSI group (44 cases) and non SSI group (1 289 cases) according to whether SSI occurred within 30 days after surgery. Clinical samples of patients in SSI group were collected for pathogen isolation and identification, and the risk factors of SSI were screened by univariate and multivariate Logistic regression analysis, respectively, and a nomogram model was constructed. The predictive efficiency was analyzed by receiver operating curve (ROC).

Results

The incidence of SSI was 3.3% (44/1 333). Total of 38 strains of pathogens were isolated, including 16 strains of Klebsiella pneumoniae, 13 strains of Escherichia coli, 7 strains of Pseudomonas aeruginosa and 2 other strains. In SSI group, the age of patients was older, urinary tract infection and multi-segment surgery (≥ 2 segments) were more, subcutaneous fat thickness, lumbar multifidus muscle (LMM) fat infiltration, operation time, drainage time were longer and the number of drainage tubes were larger, with significant differences (all P < 0.05). Univariate Logistic regression analysis showed that SSI was associated with age ≥ 60 years old, multi-segment surgery (≥ 2 segments), subcutaneous fat thickness (≥ 14.0 mm), LMM fat infiltration (≥ 22.5 %), operation time (≥ 190.0 min) and drainage time (≥ 2.0 d) (all P < 0.05). Multivariate Logistic regression analysis showed that age ≥ 60 years old, multi-segment surgery (≥ 2 segments), subcutaneous fat thickness (≥ 14.0 mm), LMM fat infiltration (≥ 22.5 %), operation time (≥ 190.0 min) and drainage time (≥ 2.0 d) were all risk factors for SSI (all P < 0.05). ROC analysis showed that AUC value of nomogram in predicting SSI was significantly higher than that of Logistic regression model (0.845 vs. 0.769: Z = 6.325, P < 0.001).

Conclusions

PLIF has a certain risk of SSI, body weight distribution indicators such as subcutaneous fat thickness and LMM fat infiltration may be important potential risk factors for SSI. The establishment of quantitative nomogram prediction model is helpful to guide clinical for early identification of SSI high-risk groups, and carry out targeted intervention to improve clinical prognosis.

图1 MRI测量LMM脂肪浸润注:腰椎横截面,T2WI相显示LMM,计算脂肪浸润横截面积值
表1 SSI组与无SSI组患者的临床资料
表2 PLIF后发生SSI的单因素Logistic回归分析
表3 PLIF后发生SSI影响因素的多因素Logistic回归模型赋值
表4 PLIF后发生SSI危险因素的多因素Logistic回归分析
图2 预测PLIF后发生SSI的列线图模型
图3 ROC分析模型的预测效能
表5 PLIF后发生SSI的列线图模型预测效能
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