A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases

Invasive mechanical ventilation plays an important role in the prognosis of patients with sepsis. However, there are, currently, no tools specifically designed to assess weaning from invasive mechanical ventilation in patients with sepsis. The aim of our study was to develop a practical model to pre...

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Published in:Frontiers in medicine Vol. 8; p. 814566
Main Authors: Liu, Wanjun, Tao, Gan, Zhang, Yijun, Xiao, Wenyan, Zhang, Jin, Liu, Yu, Lu, Zongqing, Hua, Tianfeng, Yang, Min
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media SA 18.01.2022
Frontiers Media S.A
Subjects:
Algorithms
Antibiotics
Blood
Collaboration
Comorbidity
Creatinine
Datasets
Disease
Heart rate
Infections
Intensive care
invasive mechanical ventilation
Medical prognosis
Medicine
Mortality
Patient admissions
Sepsis
simple prediction model
Success
Variables
Ventilators
Weaning
XGBoost
simple prediction model
invasive mechanical ventilation
XGBoost
sepsis
weaning
ISSN:2296-858X, 2296-858X
Online Access:Get full text
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Summary:Invasive mechanical ventilation plays an important role in the prognosis of patients with sepsis. However, there are, currently, no tools specifically designed to assess weaning from invasive mechanical ventilation in patients with sepsis. The aim of our study was to develop a practical model to predict weaning in patients with sepsis. We extracted patient information from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Kaplan-Meier curves were plotted to compare the 28-day mortality between patients who successfully weaned and those who failed to wean. Subsequently, MIMIC-IV was divided into a training set and an internal verification set, and the eICU-CRD was designated as the external verification set. We selected the best model to simplify the internal and external validation sets based on the performance of the model. A total of 5020 and 7081 sepsis patients with invasive mechanical ventilation in MIMIC-IV and eICU-CRD were included, respectively. After matching, weaning was independently associated with 28-day mortality and length of ICU stay ( < 0.001 and = 0.002, respectively). After comparison, 35 clinical variables were extracted to build weaning models. XGBoost performed the best discrimination among the models in the internal and external validation sets (AUROC: 0.80 and 0.86, respectively). Finally, a simplified model was developed based on XGBoost, which included only four variables. The simplified model also had good predictive performance (AUROC:0.75 and 0.78 in internal and external validation sets, respectively) and was developed into a web-based tool for further review. Weaning success is independently related to short-term mortality in patients with sepsis. The simplified model based on the XGBoost algorithm provides good predictive performance and great clinical applicablity for weaning, and a web-based tool was developed for better clinical application.
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Edited by: Zhongheng Zhang, Sir Run Run Shaw Hospital, China
This article was submitted to Intensive Care Medicine and Anesthesiology, a section of the journal Frontiers in Medicine
Reviewed by: Songqiao Liu, Southeast University, China; Kay Choong See, National University Hospital, Singapore
ISSN:2296-858X
2296-858X
DOI:10.3389/fmed.2021.814566