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基于机器学习模型的新型肺炎评分,用于预测肺炎患者入住重症监护病房时的死亡率。

Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit.

作者信息

Wang Bin, Li Yuanxiao, Tian Ying, Ju Changxi, Xu Xiaonan, Pei Shufen

机构信息

Department of Infectious Diseases, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Department of Pediatric Gastroenterology, Lanzhou University Second Hospital, Lanzhou, China.

出版信息

Respir Med. 2023 Oct;217:107363. doi: 10.1016/j.rmed.2023.107363. Epub 2023 Jul 13.

Abstract

BACKGROUND

Scores for predicting the long-term mortality of severe pneumonia are lacking. The purpose of this study is to use machine learning methods to develop new pneumonia scores to predict the 1-year mortality and hospital mortality of pneumonia patients on admission to the intensive care unit (ICU).

METHODS

The study population was screened from the MIMIC-IV and eICU databases. The main outcomes evaluated were 1-year mortality and hospital mortality in the MIMIC-IV database and hospital mortality in the eICU database. From the full data set, we separated patients diagnosed with community-acquired pneumonia (CAP) and ventilator-associated pneumonia (VAP) for subgroup analysis. We used common shallow machine learning algorithms, including logistic regression, decision tree, random forest, multilayer perceptron and XGBoost.

RESULTS

The full data set of the MIMIC-IV database contained 4697 patients, while that of the eICU database contained 13760 patients. We defined a new pneumonia score, the "Integrated CCI-APS", using a multivariate logistic regression model including six variables: metastatic solid tumor, Charlson Comorbidity Index, readmission, congestive heart failure, age, and Acute Physiology Score III. The area under the curve (AUC) and accuracy of the integrated CCI-APS were assessed in three data sets (full, CAP, and VAP) using both the test set derived from the MIMIC-IV database and the external validation set derived from the eICU database. The AUC value ranges in predicting 1-year and hospital mortality were 0.784-0.797 and 0.691-0.780, respectively, and the corresponding accuracy ranges were 0.723-0.725 and 0.641-0.718, respectively.

CONCLUSIONS

The main contribution of this study was a benchmark for using machine learning models to build pneumonia scores. Based on the idea of integrated learning, we propose a new integrated CCI-APS score for severe pneumonia. In the prediction of 1-year mortality and hospital mortality, our new pneumonia score outperformed the existing score.

摘要

背景

目前缺乏预测重症肺炎长期死亡率的评分系统。本研究旨在使用机器学习方法开发新的肺炎评分系统,以预测入住重症监护病房(ICU)的肺炎患者的1年死亡率和院内死亡率。

方法

从MIMIC-IV和eICU数据库中筛选研究人群。评估的主要结局为MIMIC-IV数据库中的1年死亡率和院内死亡率以及eICU数据库中的院内死亡率。从完整数据集中,我们分离出诊断为社区获得性肺炎(CAP)和呼吸机相关性肺炎(VAP)的患者进行亚组分析。我们使用了常见的浅层机器学习算法,包括逻辑回归、决策树、随机森林、多层感知器和XGBoost。

结果

MIMIC-IV数据库的完整数据集包含4697例患者,而eICU数据库的完整数据集包含13760例患者。我们使用包含六个变量的多变量逻辑回归模型定义了一个新的肺炎评分“综合CCI-APS”,这六个变量为转移性实体瘤、Charlson合并症指数、再次入院、充血性心力衰竭、年龄和急性生理学评分III。使用来自MIMIC-IV数据库的测试集和来自eICU数据库的外部验证集,在三个数据集(完整、CAP和VAP)中评估综合CCI-APS的曲线下面积(AUC)和准确性。预测1年和院内死亡率的AUC值范围分别为0.784-0.797和0.691-0.780,相应的准确性范围分别为0.723-0.725和0.641-0.718。

结论

本研究的主要贡献是为使用机器学习模型构建肺炎评分提供了一个基准。基于集成学习的理念,我们提出了一种用于重症肺炎的新的综合CCI-APS评分。在预测1年死亡率和院内死亡率方面,我们的新肺炎评分优于现有评分。

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