用于预测重症监护病房拔管后低氧血症的机器学习模型的开发与验证

Development and validation of a machine-learning model for prediction of hypoxemia after extubation in intensive care units.

作者信息

Xia Ming, Jin Chenyu, Cao Shuang, Pei Bei, Wang Jie, Xu Tianyi, Jiang Hong

机构信息

Department of Anesthesiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Ann Transl Med. 2022 May;10(10):577. doi: 10.21037/atm-22-2118.

DOI:10.21037/atm-22-2118

PMID:35722375

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9201189/

Abstract

BACKGROUND

Extubation is the process of removing tracheal tubes so that patients maintain oxygenation while they start to breathe spontaneously. However, hypoxemia after extubation is an important issue for critical care doctors and is associated with patients' oxygenation, circulation, recovery, and incidence of postoperative complications. Accuracy and specificity of most related conventional models remain unsatisfactory. We conducted a predictive analysis based on a supervised machine-learning algorithm for the precise prediction of hypoxemia after extubation in intensive care units (ICUs).

METHODS

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database for patients over age 18 who underwent mechanical ventilation in the ICU. The primary outcome was hypoxemia after extubation, and it was defined as a partial pressure of oxygen <60 mmHg after extubation. Variables and individuals with missing values greater than 20% were excluded, and the remaining missing values were filled in using multiple imputation. The dataset was split into a training set (80%) and final test set (20%). All related clinical and laboratory variables were extracted, and logistics stepwise regression was performed to screen out the key features. Six different advanced machine-learning models, including logistics regression (LOG), random forest (RF), K-nearest neighbors (KNN), support-vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were introduced for modelling. The best performance model in the first cross-validated dataset was further fine-tuned, and the final performance was assessed using the final test set.

RESULTS

A total of 14,777 patients were included in the study, and 1,864 of the patients' experienced hypoxemia after extubation. After training, the RF and LightGBM models were the strongest initial performers, and the area under the curve (AUC) using RF was 0.780 [95% confidence interval (CI), 0.755-0.805] and using LightGBM was 0.779 (95% CI, 0.752-0.806). The final AUC using RF was 0.792 (95% CI, 0.771-0.814) and using LightGBM was 0.792 (95% CI, 0.770-0.815).

CONCLUSIONS

Our machine learning models have considerable potential for predicting hypoxemia after extubation, which help to reduce ICU morbidity and mortality.

摘要

背景

拔管是移除气管导管的过程，目的是让患者在开始自主呼吸时维持氧合。然而，拔管后低氧血症是重症监护医生面临的一个重要问题，与患者的氧合、循环、恢复以及术后并发症发生率相关。大多数相关传统模型的准确性和特异性仍不尽人意。我们基于监督式机器学习算法进行了一项预测分析，以精确预测重症监护病房（ICU）患者拔管后的低氧血症。

方法

从重症监护医学信息数据库（MIMIC）-IV中提取18岁以上在ICU接受机械通气患者的数据。主要结局是拔管后低氧血症，定义为拔管后氧分压<60 mmHg。排除缺失值大于20%的变量和个体，其余缺失值采用多重填补法进行填补。将数据集分为训练集（80%）和最终测试集（20%）。提取所有相关临床和实验室变量，并进行逻辑逐步回归以筛选出关键特征。引入六种不同的先进机器学习模型进行建模，包括逻辑回归（LOG）、随机森林（RF）、K近邻（KNN）、支持向量机（SVM）、极端梯度提升（XGBoost）和轻量级梯度提升机（LightGBM）。对第一个交叉验证数据集中表现最佳的模型进行进一步微调，并使用最终测试集评估最终性能。

结果

本研究共纳入14777例患者，其中1864例患者拔管后发生低氧血症。训练后，RF和LightGBM模型是最初表现最强的模型，RF模型的曲线下面积（AUC）为0.780 [95%置信区间（CI），0.755 - 0.805]，LightGBM模型的AUC为0.779（95% CI，0.752 - 0.806）。RF模型最终的AUC为0.792（95% CI，0.771 - 0.814），LightGBM模型最终的AUC为0.792（95% CI，(0.770 - 0.815)）。