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中华疝和腹壁外科杂志(电子版)

中华疝和腹壁外科杂志(电子版) ›› 2023, Vol. 17 ›› Issue (01) : 15 -19. doi: 10.3877/cma.j.issn.1674-392X.2023.01.005

临床论著

腹壁切口疝修补术后补片感染列线图预测模型构建
邢晓伟1, 张荣杰2, 陈杰1,†()   
  1. 1. 100043　首都医科大学附属北京朝阳医院疝和腹壁外科
    2. 100043　首都医科大学附属北京朝阳医院泌尿外科
  • 收稿日期:2022-05-09 出版日期:2023-02-18
  • 通信作者: 陈杰

A predictive nomogram for the risk of mesh infection after incisional hernia repair

Xiaowei Xing1, Rongjie Zhang2, Jie Chen1,†()   

  1. 1. Department of Hernia and Abdominal Wall Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100043, China
    2. Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100043, China
  • Received:2022-05-09 Published:2023-02-18
  • Corresponding author: Jie Chen
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邢晓伟, 张荣杰, 陈杰. 腹壁切口疝修补术后补片感染列线图预测模型构建[J]. 中华疝和腹壁外科杂志(电子版), 2023, 17(01): 15-19.

Xiaowei Xing, Rongjie Zhang, Jie Chen. A predictive nomogram for the risk of mesh infection after incisional hernia repair[J]. Chinese Journal of Hernia and Abdominal Wall Surgery(Electronic Edition), 2023, 17(01): 15-19.

目的

分析腹壁切口疝修补术后补片感染的危险因素，建立切口疝患者补片感染的预测模型，为临床预测切口疝修补术后发生补片感染提供一种可视化评价工具。

方法

回顾性分析2016年1月至2018年12月在首都医科大学附属北京朝阳医院就诊的475例切口疝患者的临床资料，收集患者的一般资料、手术资料、术后恢复情况，随访补片感染情况。使用Lasso回归筛选预测因子，在此基础上通过多因素Logistic回归进一步分析并建立列线图预测模型，采用受试者工作特征曲线下面积评估模型的预测效力。

结果

475例接受切口疝修补手术的患者中有11例出现补片感染，发生率为2.3%。Lasso回归结合多因素Logistic回归分析结果显示，体质量指数（OR=1.206，95% CI 1.034~1.407）、糖尿病史（OR=6.484，95% CI 1.233~34.108）、术后外科手术部位感染（OR=37.095，95% CI 4.253~323.532）是切口疝患者发生补片感染的影响因素（P<0.05），利用上述变量建立列线图预测模型，列线图预测模型预测补片感染发生AUC为0.880（95% CI 0.785~0.975）。

结论

本研究成功建立一种具有良好预测效力的列线图预测模型，有助于提高对补片感染高危切口疝患者的早期鉴别能力，为改善切口疝患者预后提供帮助。

关键词: 切口疝, 补片, 感染, 列线图, 预测
Objective

To explore the risk factors of mesh infection after incisional hernia repair, and to develop a predictive nomogram as visualized tool for clinical prediction of mesh infection.

Methods

475 patients with incisional hernias were retrospectively selected from Beijing Chaoyang Hospital of Capital Medical University from January of 2016 to December of 2018. Patients' basic information, surgery information and recovery information after surgery were collected, and mesh infection information was followed up. Predictors of mesh infection were screened by Lasso regression analysis, and further analyzed by multivariate logistic regression analysis. Then the final determined ones were applied to develop a predictive nomogram. The area under the ROC curve (AUC) was used to evaluate the predictive utility of the nomogram.

Results

Among the 475 cases, 11 developed a mesh infection; the incidence was 2.3%. The findings of Lasso regression with multivariate logistic analysis demonstrated that body mass index (BMI) [OR=1.206, 95% CI (1.034, 1.407)]、diabetes [OR=6.484, 95% CI (1.233, 34.108)] and surgical site infection (SSI) [OR=37.095, 95% CI (4.253, 323.532)] were associated with mesh infection. The predictive nomogram was established using the above-mentioned variables. The AUC of the nomogram was 0.880 [95% CI (0.785, 0.975)].

Conclusion

We successfully established a predictive nomogram with high accuracy, which may be used to improve the early identification of mesh infection and the clinical outcome of patients with incisional hernias.

Key words: Incisional hernia, Mesh, Infections, Nomogram, Prediction
表1 无补片感染患者与补片感染患者一般资料比较[例（%）]
组别 例数 性别 年龄(岁,±s) 体质量指数(kg/m2,±s) 吸烟史 糖尿病史 既往腹壁感染
男 女 是 否 是 否 是 否
无补片感染 464 173(37.3) 291(62.7) 63.1±13.6 25.6±4.1 117(25.2) 347(74.8) 54(11.6) 410(88.4) 4(0.9) 460(99.1)
补片感染 11 7(63.6) 4(36.4) 64.1±11.5 29.2±3.7 3(27.3) 8(72.7) 5(45.5) 6(54.5) 0 11(100.0)
χ2/t   3.17 0.24 2.95 0.02 11.30 0.10
P值   0.075 0.811 0.003 0.877 0.001 0.757
组别 例数 住院时间(d,±s) 疝位置 缺损面积(cm2,±s) 手术方式 手术时间(min,±s) 出血量(ml,±s) 补片类型
中腹部 侧腹部 腹腔镜手术 开放手术 合成补片 生物补片
无补片感染 464 16.3± 8.1 154(33.2) 310(66.8) 76.7±98.0 238(51.3) 226(48.7) 84.0±40.0 13.5±25.0 442(95.3) 22(4.7)
补片感染 11 30.5±20.1 4(36.4) 7(63.6) 143.4±167.5 5(45.5) 6(54.5) 133.1±55.6 51.1±111.1 11(100.0) 0
χ2/t   7.35 0.05 2.18 0.15 3.97 4.11 0.55
P值   <0.001 0.825 0.030 0.702 <0.001 <0.001 0.460
组别 例数 术中肠切除 术中肠修补 术后外科手术部位感染 血肿 血清肿
是 否 是 否 是 否 是 否 是 否
无补片感染 464 5(1.1) 459(98.9) 6(1.3) 458(98.7) 5(1.1) 459(98.9) 8(1.7) 456(98.3) 11(2.4) 453(97.6)
补片感染 11 3(27.3) 8(72.7) 1(9.1) 10(90.9) 4(36.4) 7(63.6) 0 11(100.0) 1(9.1) 10(90.9)
χ2/t   44.53 4.50 71.80 0.19 1.97
P值   <0.001 0.034 <0.001 0.661 0.160
图1 Lasso回归进行临床特征筛选注：A为18个临床特征的系数曲线；B为Lasso回归选择最适合的临床特征，最小λ值为0.005 72，此时模型最优，筛选的7个预测变量为：体质量指数、糖尿病史、住院时间、手术时间、术中肠切除、术中肠修补以及术后手术部位感染情况
表2 变量赋值表
补片感染 性别 吸烟史 糖尿病史 腹壁感染史 疝位置 手术方式 术中肠切除 术中肠修补 术后外科手术部位感染 术后血肿 术后血清肿
是 否 男 女 是 否 是 否 是 否 中腹部 侧腹部 腔镜 开放 是 否 是 否 是 否 是 否 是 否
1 0 1 2 1 0 1 0 1 0 1 2 1 2 1 0 1 0 1 0 1 0 1 0
表3 切口疝患者发生补片感染的多因素Logistic回归分析
变量 β值 标准误 Wald OR值 95% CI P值
BMI 0.188 0.079 5.708 1.206 1.034~1.407 0.017
糖尿病史 1.869 0.847 4.870 6.484 1.233~34.108 0.027
术后SSI 3.613 1.105 10.693 37.095 4.253~323.532 0.001
图2 切口疝患者发生补片感染的风险预测列线图注：根据变量进行赋分，每个变量的赋值点分别对应相应的得分，将各个变量的得分相加，得到总分，即可得出补片感染的预测概率。BMI体质量指数；SSI外科手术部位感染
图3 列线图预测模型预测切口疝患者发生补片感染风险的受试者工作特征曲线注：ROC受试者工作特征；AUC曲线下面积
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