Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study.
10.1016/j.cjtee.2024.08.012
- Author:
Qing-Bo ZENG
1
;
En-Lan PENG
2
;
Ye ZHOU
3
;
Qing-Wei LIN
3
;
Lin-Cui ZHONG
3
;
Long-Ping HE
3
;
Nian-Qing ZHANG
4
;
Jing-Chun SONG
5
,
6
Author Information
1. Intensive Care Unit, The 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, 330002, China; Intensive Care Unit, Nanchang Hongdu Hospital of Traditional Chinese Medicine, Nanchang, 330038, China.
2. Department of Critical Care Medicine, Changcheng Hospital Affiliated to Nanchang University, Nanchang, 330002, China.
3. Intensive Care Unit, The 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, 330002, China; Department of Critical Care Medicine, Changcheng Hospital Affiliated to Nanchang University, Nanchang, 330002, China.
4. Intensive Care Unit, Nanchang Hongdu Hospital of Traditional Chinese Medicine, Nanchang, 330038, China.
5. Intensive Care Unit, The 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, 330002, China; Department of Critical Care Medicine, Changcheng Hospital Affiliated to Nanchang University, Nanchang, 330002, China. Electronic address: songjingchun@
6. com.
- Publication Type:Multicenter Study
- Keywords:
Machine learning;
Predictive model;
SHapley Additive exPlanation;
Sepsis;
Shock;
Tissue plasminogen activator-inhibitor complex
- MeSH:
Humans;
Shock, Septic/blood*;
Machine Learning;
Male;
Female;
Retrospective Studies;
Middle Aged;
Aged;
Sepsis/complications*;
ROC Curve;
Cohort Studies;
Adult;
Intensive Care Units;
Algorithms;
Blood Coagulation;
Critical Illness
- From:
Chinese Journal of Traumatology
2025;28(6):404-411
- CountryChina
- Language:English
-
Abstract:
PURPOSE:Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis.
METHODS:It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm.
RESULTS:Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis.
CONCLUSION:The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
