切换至 "中华医学电子期刊资源库"

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/OL]. 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/OL]. 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
[1]
中华医学会骨科学分会. 腰椎间盘突出症诊疗指南[J]. 中华骨科杂志,2020,10(08):477-487.
[2]
Lurie JD, Henderson ER, Mcdonough CM, et al. The effect of expectations on treatment outcome for lumbar intervertebral disc herniation[J]. Spine,2016,41(9):803-809.
[3]
郭旭锋, 佘江. 骨科切口感染者创面病原菌分布及自噬相关蛋白表达[J/CD]. 中华实验和临床感染病杂志(电子版),2020,14(1):63-68.
[4]
Liu JM, Deng HL, Chen XY, et al. Risk factors for surgical site infection after posterior lumbar spinal surgery[J]. Spine,2018,43(10):732-737.
[5]
Lian J, Wang Y, Yan X, et al. Development and validation of a nomogram to predict the risk of surgical site infection within 1 month after transforaminal lumbar interbody fusion[J]. J Orthop Surg Res,2023,18(1):105-119.
[6]
Mangram AJ, Horan TC, Pearson ML, et al. Guideline for prevention of surgical site infection, 1999. Hospital Infection Control Practices Advisory Committee[J]. Inf Cont Hosp Epidermiol,1999,20(4):250-278.
[7]
叶应妩, 王毓三主编. 全国临床检验操作规程[M]. 2版. 南京: 东南大学出版社,2006:736-883.
[8]
Singh R, Yadav SK, Sood S, et al. Magnetic resonance imaging of lumbar trunk parameters in chronic low backache patients and healthy population: a comparative study[J]. Eur Spine J,2016,25(9):2864-2872.
[9]
Fei Q, Li J, Lin J, et al. Risk factors for surgical site infection after spinal surgery: a meta-analysis[J]. World Neurosurg,2016,95(7):507-515.
[10]
杨明, 张文涛, 柯嵩, 等. 腰椎后路融合术后手术部位感染的相关危险因素分析[J]. 中国骨与关节杂志,2021,10(4):248-254.
[11]
王成伟. 腰椎椎旁肌横截面积和多裂肌脂肪浸润及不对称性对退行性腰椎滑脱的影响[J]. 感染·炎症·修复,2020,21(3):177-181.
[12]
李陈, 董斌, 戴大飞. 多裂肌脂肪浸润与腰椎融合术后感染的相关性研究[J]. 中国医刊,2021,56(11):1239-1242.
[13]
Chen X, Hodges PW, James G, et al. Do markers of inflammation and/or muscle regeneration in lumbar multifidus muscle and fat differ between individuals with good or poor outcome following microdiscectomy for lumbar disc Herniation[J]. Spine,2020,12(4):1562-1564.
[14]
魏巍, 王天昊, 刘建恒, 等. 多裂肌损伤对腰椎后路椎间融合术后邻近节段生物力学的影响[J]. 中国脊柱脊髓杂志,2022,32(5):440-447.
[15]
Teichtahl AJ, Urquhart DM, Wang Y, et al. Fat infiltration of paraspinal muscles is associated with low back pain, disability, and structural abnormalities in community-based adults[J]. Spine J,2015,15(7):1593-1601.
[16]
李雅萍, 刘红, 黄武杰, 等. 慢性非特异性腰痛患者腰椎椎旁肌脂肪浸润比与腰椎-骨盆参数的相关性[J]. 中国脊柱脊髓杂志,2021,31(9):825-832.
[17]
Godeneche A, Elia F, Kempf JF, et al. Fatty infiltration of stage 1 or higher significantly compromises long-term healing of supraspinatus repairs[J]. J Shoulder Elb Surg,2017,26(10):1818-1825.
[18]
潘富伟, 郎珈望, 张旻, 等. 非特异性腰痛患者椎旁多裂肌脂肪浸润程度与性别,年龄,影像学参数的相关性分析[J]. 中华中医药杂志,2023,38(3):1274-1280.
[19]
陈威烨, 王宽, 元唯安, 等. 腰骶部多裂肌与腰椎间盘突出症关系的研究进展[J]. 中国骨伤,2016,29(6):581-584.
[20]
彭明学, 王自鸿, 张桂通, 等. 椎旁肌群变化与腰椎间盘突出程度的相关性分析[J]. 颈腰痛杂志,2019,40(4):538-540.
[21]
Colakoglu B, Alis D. Evaluation of lumbar multifidus muscle in patients with lumbar disc herniation: are complex quantitative MRI measurements needed?[J]. J Int Med Res,2019,47(8):3590-3600.
[22]
王鑫强, 贾瑞钢, 陈彦影, 等. 退变性腰椎滑脱患者腰部多裂肌退变与腰背痛的相关性研究[J]. 实用骨科杂志,2017,23(9):777-780.
[23]
Jermy JE, Copley PC, Poon MTC, et al. Does pre-operative multifidus morphology on MRI predict clinical outcomes in adults following surgical treatment for degenerative lumbar spine disease? A systematic review[J]. Eur Spine J,2020,29(6):1318-1327.
[24]
Evrim EE, Hülya KY, Harun M, et al. Age and sex-based distribution of lumbar multifidus muscle atrophy and coexistence of disc hernia: an MRI study of 2 028 patients[J]. Diagn Interv Radiol,2016,22(3):273-276.
[25]
田亚豪, 吴巍, 李锋. 多裂肌脂肪浸润与退变性腰椎滑脱的发生关系的研究[J]. 生物骨科材料与临床研究,2022,19(5):27-31.
[1] 明昊, 肖迎聪, 巨艳, 宋宏萍. 乳腺癌风险预测模型的研究现状[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(05): 287-291.
[2] 蒲彦婷, 吴翠先, 兰玉梅. 类风湿关节炎患者骨质疏松症风险预测列线图模型构建[J/OL]. 中华关节外科杂志(电子版), 2024, 18(05): 596-603.
[3] 庄燕, 戴林峰, 张海东, 陈秋华, 聂清芳. 脓毒症患者早期生存影响因素及Cox 风险预测模型构建[J/OL]. 中华危重症医学杂志(电子版), 2024, 17(05): 372-378.
[4] 黄鸿初, 黄美容, 温丽红. 血液系统恶性肿瘤患者化疗后粒细胞缺乏感染的危险因素和风险预测模型[J/OL]. 中华实验和临床感染病杂志(电子版), 2024, 18(05): 285-292.
[5] 罗文斌, 韩玮. 胰腺癌患者首次化疗后中重度骨髓抑制的相关危险因素分析及预测模型构建[J/OL]. 中华普通外科学文献(电子版), 2024, 18(05): 357-362.
[6] 贺斌, 马晋峰. 胃癌脾门淋巴结转移危险因素[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 694-699.
[7] 屈勤芳, 束方莲. 盆腔器官脱垂患者盆底重建手术后压力性尿失禁发生的影响因素及列线图预测模型构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 606-612.
[8] 林凯, 潘勇, 赵高平, 杨春. 造口还纳术后切口疝的危险因素分析与预防策略[J/OL]. 中华疝和腹壁外科杂志(电子版), 2024, 18(06): 634-638.
[9] 杨闯, 马雪. 腹壁疝术后感染的危险因素分析[J/OL]. 中华疝和腹壁外科杂志(电子版), 2024, 18(06): 693-696.
[10] 张伟伟, 陈启, 翁和语, 黄亮. 随机森林模型预测T1 期结直肠癌淋巴结转移的初步研究[J/OL]. 中华结直肠疾病电子杂志, 2024, 13(05): 389-393.
[11] 司楠, 孙洪涛. 创伤性脑损伤后肾功能障碍危险因素的研究进展[J/OL]. 中华脑科疾病与康复杂志(电子版), 2024, 14(05): 300-305.
[12] 韦巧玲, 黄妍, 赵昌, 宋庆峰, 陈祖毅, 黄莹, 蒙嫦, 黄靖. 肝癌微波消融术后中重度疼痛风险预测列线图模型构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 715-721.
[13] 颜世锐, 熊辉. 感染性心内膜炎合并急性肾损伤患者的危险因素探索及死亡风险预测[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 618-624.
[14] 刘志超, 胡风云, 温春丽. 山西省脑卒中危险因素与地域的相关性分析[J/OL]. 中华脑血管病杂志(电子版), 2024, 18(05): 424-433.
[15] 曹亚丽, 高雨萌, 张英谦, 李博, 杜军保, 金红芳. 儿童坐位不耐受的临床进展[J/OL]. 中华脑血管病杂志(电子版), 2024, 18(05): 510-515.
阅读次数
全文


摘要