A Machine Learning decision-making tool for extubation in Intensive Care Unit patients. (March 2021)

• Record Type: Journal Article
• Authors: Fabregat, Alexandre; Magret, Mónica; Ferré, Josep Anton; Vernet, Anton; Guasch, Neus; Rodríguez, Alejandro; Gómez, Josep; Bodí, María
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Invasive mechanical ventilation -- Extubation -- Reintubation -- Machine Learning -- Gradient Boosting -- Support Vector Machine -- Clinical decision support tool; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105869
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Machine Learning models are shown to potentially reduce unsuccessful extubation rate. Monitor signals, patient admission data and medical records are used as predictors. Support Vector Machines exhibit 92% accuracy in predicting extubation outcome. Risks associated to prolonged invasive mechanical ventilation can be minimized. Pre-processing challenges stress need for better data quality and curation protocols. Abstract: Background and Objective : 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. Methods : 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. Results : 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. Conclusions : 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.