Fabregat Alexandre, Magret Mónica, Ferré Josep Anton, Vernet Anton, Guasch Neus, Rodríguez Alejandro, Gómez Josep, Bodí María
Department of Mechanical Engineering, Universitat Rovira i Virgili. Av. Països Catalans, 26 (43007) Tarragona, Spain.
Hospital Universitari de Tarragona Joan XXIII Institut d'Investigaci, Sanitária Pere Virgili, Universitat Rovira i Virgili. C/. Dr. Mallafré Guasch, 4 (43005) Tarragona, Spain.
Comput Methods Programs Biomed. 2021 Mar;200:105869. doi: 10.1016/j.cmpb.2020.105869. Epub 2020 Nov 24.
To increase the success rate of invasive mechanical ventilation weaning in critically ill patients using Machine Learning models capable of accurately predicting the outcome of programmed extubations.
The study population was adult patients admitted to the Intensive Care Unit. Target events were programmed extubations, both successful and failed. The working dataset is assembled by combining heterogeneous data including time series from Clinical Information Systems, patient demographics, medical records and respiratory event logs. Three classification learners have been compared: Logistic Discriminant Analysis, Gradient Boosting Method and Support Vector Machines. Standard methodologies have been used for preprocessing, hyperparameter tuning and resampling.
The Support Vector Machine classifier is found to correctly predict the outcome of an extubation with a 94.6% accuracy. Contrary to current decision-making criteria for extubation based on Spontaneous Breathing Trials, the classifier predictors only require monitor data, medical entry records and patient demographics.
Machine Learning-based tools have been found to accurately predict the extubation outcome in critical patients with invasive mechanical ventilation. The use of this important predictive capability to assess the extubation decision could potentially reduce the rate of extubation failure, currently at 9%. With about 40% of critically ill patients eventually receiving invasive mechanical ventilation during their stay and given the serious potential complications associated to reintubation, the excellent predictive ability of the model presented here suggests that Machine Learning techniques could significantly improve the clinical outcomes of critical patients.
使用能够准确预测计划性拔管结果的机器学习模型，提高重症患者有创机械通气撤机的成功率。
研究人群为入住重症监护病房的成年患者。目标事件为计划性拔管，包括成功和失败的情况。工作数据集通过整合异构数据组装而成，这些数据包括临床信息系统的时间序列、患者人口统计学信息、病历和呼吸事件日志。比较了三种分类学习器：逻辑判别分析、梯度提升法和支持向量机。使用标准方法进行预处理、超参数调整和重采样。
发现支持向量机分类器能够以94.6%的准确率正确预测拔管结果。与当前基于自主呼吸试验的拔管决策标准相反，分类器预测因子仅需要监测数据、医疗录入记录和患者人口统计学信息。
已发现基于机器学习的工具能够准确预测有创机械通气重症患者的拔管结果。利用这一重要的预测能力来评估拔管决策可能会降低目前为9%的拔管失败率。鉴于约40%的重症患者在住院期间最终接受有创机械通气，且再次插管存在严重的潜在并发症，本文提出的模型具有出色的预测能力，这表明机器学习技术可显著改善重症患者的临床结局。
