Prediction of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery

Xin SHU; Haoyang LI; Yujie LI; Ailin SONG; Xiaoyan HU; Yuwen CHEN; Ju ZHANG; Bin YI

doi:10.16016/j.2097-0927.202212045

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Prediction of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery

Monographic Reports: Governance and artificial intelligence algorithms of big data during perioperative period | 更新时间：2026-01-09

- Prediction of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery
- Journal of Army Medical University Vol. 45, Issue 8, Pages: 732-738(2023)
- 作者机构：
  
  1.400038 重庆, 陆军军医大学(第三军医大学)第一附属医院麻醉科
  2.400038 重庆, 陆军军医大学(第三军医大学)基础医学院学员五大队
  3.400714 重庆, 中国科学院重庆绿色智能技术研究院
- 作者简介：
- 基金信息：
  
  the National Key Research and Development Program(2018YFC0116702);the "Lump Sum Contract" Project of Chongqing Talent Program(CQYC202103080)
- DOI：10.16016/j.2097-0927.202212045
  CLC： R319;R619.3;R656.07
- Received：06 December 2022，
  
  Revised：2023-01-09，
  
  Published：30 April 2023
- 稿件说明：
移动端阅览
Xin SHU, Haoyang LI, Yujie LI, et al. Prediction of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery[J]. Journal of Army Medical University, 2023, 45(8): 732-738.
DOI：

Xin SHU, Haoyang LI, Yujie LI, et al. Prediction of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery[J]. Journal of Army Medical University, 2023, 45(8): 732-738. DOI： 10.16016/j.2097-0927.202212045.

摘要

目的

探讨机器学习算法构建腹部手术术后脓毒症患者死亡风险预测模型的可行性。

方法

采用病例-对照研究设计方案，从公共重症监护医学信息数据库(Medical Information Mart for Intensive Care Ⅳ，MIMIC-Ⅳ v1.0)中筛选出行腹部手术后发生脓毒症的患者，研究终点事件定义为患者入院后90 d内死亡。根据死亡与否将数据集随机拆分为训练数据集(70%)与测试数据集(30%)，在训练数据集上基于Logistic回归(logistic regression，LR)、梯度提升树(gradient boosting decision tree，GBDT)、随机森林(random forest，RF)、支持向量机(support vector machine，SVM)和自适应提升(adaptive boosting，AdaBoost)算法构建预测死亡风险模型；在测试数据集上通过受试者工作曲线(receiver operating characteristic curve，ROC)和曲线下面积(area under the ROC curve，AUC)、敏感性、特异性、阳性预测值、阴性预测值、F1分数和准确率来评估模型效能。

结果

最终986例患者纳入本研究，其中251例(25.5%)患者入院后90 d内死亡，LR、GBDT、RF、SVM及AdaBoost模型的AUC依次为0.852、0.903、0.921、0.940和0.906，其中SVM的AUC最高，预测性能更好，而LR模型效能最差。

结论

基于GBDT、RF、SVM及AdaBoost这4种算法建立的腹部手术术后脓毒症死亡率预测模型的效能优于传统的LR模型，可能有助于临床决策，改善不良结局。

Abstract

Objective

To explore the feasibility of constructing prediction models of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery.

Methods

A case-control trial was designed and conducted on the patients diagnosed with sepsis after abdominal surgery from Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database

and 90-day mortality was defined as the primary endpoint event after hospitalization. The dataset was ramdomly split into training (70%) and test (30%) datasets according to wether diagnosed with postopertive sepsis or not. On the training dataset

logistic regression (LR)

gradient boosting decision tree (GBDT)

random forest (RF)

support vector machine (SVM) and adaptive boosting (AdaBoost) were used to develop the prediction model for death. The area under the receiver operating characteristic curve (AUC)

sensitivity

specificity

positive predictive value

negative predictive value

accuracy and F1 score were used for model evaluation on the test dataset.

Results

A total of 986 patients were finally analyzed

of whom 251 patients (25.5%) died within 90 d after hospitalization. The AUC values of LR

GBDT

SVM and AdaBoost prediction models were 0.852

0.903

0.921

0.940 and 0.906

respectively. The model based on SVM yielded the best AUC value

higher differentiation and better prediction performance

while LR performed the worst among them.

Conclusion

The performances of the prediction model of postoperative sepsis mortality based on GBDTT

SVM and AdaBoost are all better than that of traditional LR model

which may help to assist clinical decision making and improve adverse outcomes.

关键词

Keywords

references

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