Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

医学 重症监护 机器学习 呼吸频率 机械通风 特征工程 急诊医学 计算机科学 人工智能 重症监护医学 心率 血压 深度学习 内科学
作者
Qinyu Zhao,Huan Wang,Jing-Chao Luo,Minghao Luo,Leping Liu,Shen-Ji Yu,Kai Liu,Qian Zhang,Peng Sun,Guo-Wei Tu,Zhe Luo
出处
期刊:Frontiers in Medicine [Frontiers Media]
卷期号:8 被引量:50
标识
DOI:10.3389/fmed.2021.676343
摘要

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs). Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model. Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction. Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
自觉的凌青完成签到,获得积分10
刚刚
2秒前
4秒前
畅快芝麻发布了新的文献求助10
4秒前
zriverm发布了新的文献求助10
6秒前
干煸鸡发布了新的文献求助10
7秒前
8秒前
9秒前
9秒前
10秒前
量子星尘发布了新的文献求助10
10秒前
呸呸晓鹏发布了新的文献求助10
14秒前
枫之林发布了新的文献求助10
15秒前
小蘑菇应助zriverm采纳,获得10
17秒前
17秒前
17秒前
SciGPT应助小鱼采纳,获得10
18秒前
学术渣渣发布了新的文献求助30
18秒前
渡劫完成签到,获得积分10
19秒前
19秒前
22秒前
靓丽雨梅完成签到 ,获得积分10
22秒前
等待的花生完成签到,获得积分10
22秒前
24秒前
Mangues发布了新的文献求助30
24秒前
呸呸晓鹏完成签到,获得积分20
24秒前
搜集达人应助xuxu采纳,获得10
25秒前
111111关注了科研通微信公众号
26秒前
26秒前
26秒前
小唐尼发布了新的文献求助30
30秒前
30秒前
34秒前
彭于晏应助gewenxue采纳,获得10
35秒前
幸福大白发布了新的文献求助10
37秒前
yyyy完成签到,获得积分10
38秒前
zx完成签到,获得积分10
38秒前
39秒前
肥猫完成签到,获得积分10
41秒前
Aixia发布了新的文献求助10
42秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989069
求助须知:如何正确求助?哪些是违规求助? 3531351
关于积分的说明 11253589
捐赠科研通 3269939
什么是DOI,文献DOI怎么找? 1804851
邀请新用户注册赠送积分活动 882074
科研通“疑难数据库(出版商)”最低求助积分说明 809073