Alexandre Fabregat1, Mónica Magret2, Josep Anton Ferré3, Anton Vernet4, Neus Guasch5, Alejandro Rodríguez6, Josep Gómez7, María Bodí8. 1. Department of Mechanical Engineering, Universitat Rovira i Virgili. Av. Països Catalans, 26 (43007) Tarragona, Spain. Electronic address: alexandre.fabregat@urv.cat. 2. Hospital Universitari de Tarragona Joan XXIII Institut d'Investigaci, Sanitária Pere Virgili, Universitat Rovira i Virgili. C/. Dr. Mallafré Guasch, 4 (43005) Tarragona, Spain. Electronic address: mmagret@gmail.com. 3. Department of Mechanical Engineering, Universitat Rovira i Virgili. Av. Països Catalans, 26 (43007) Tarragona, Spain. Electronic address: josep.a.ferre@urv.cat. 4. Department of Mechanical Engineering, Universitat Rovira i Virgili. Av. Països Catalans, 26 (43007) Tarragona, Spain. Electronic address: anton.vernet@urv.cat. 5. Hospital Universitari de Tarragona Joan XXIII Institut d'Investigaci, Sanitária Pere Virgili, Universitat Rovira i Virgili. C/. Dr. Mallafré Guasch, 4 (43005) Tarragona, Spain. Electronic address: guaschboque@gmail.com. 6. Hospital Universitari de Tarragona Joan XXIII Institut d'Investigaci, Sanitária Pere Virgili, Universitat Rovira i Virgili. C/. Dr. Mallafré Guasch, 4 (43005) Tarragona, Spain. Electronic address: arodri.hj23.ics@gencat.cat. 7. Hospital Universitari de Tarragona Joan XXIII Institut d'Investigaci, Sanitária Pere Virgili, Universitat Rovira i Virgili. C/. Dr. Mallafré Guasch, 4 (43005) Tarragona, Spain. Electronic address: josep.goal@gmail.com. 8. Hospital Universitari de Tarragona Joan XXIII Institut d'Investigaci, Sanitária Pere Virgili, Universitat Rovira i Virgili. C/. Dr. Mallafré Guasch, 4 (43005) Tarragona, Spain. Electronic address: mbodi.hj23.ics@gencat.cat.
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.
BACKGROUND AND OBJECTIVE: To increase the success rate of invasive mechanical ventilation weaning in critically illpatients 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 illpatients 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.
Authors: Lucas M Fleuren; Tariq A Dam; Michele Tonutti; Daan P de Bruin; Robbert C A Lalisang; Diederik Gommers; Olaf L Cremer; Rob J Bosman; Sander Rigter; Evert-Jan Wils; Tim Frenzel; Dave A Dongelmans; Remko de Jong; Marco Peters; Marlijn J A Kamps; Dharmanand Ramnarain; Ralph Nowitzky; Fleur G C A Nooteboom; Wouter de Ruijter; Louise C Urlings-Strop; Ellen G M Smit; D Jannet Mehagnoul-Schipper; Tom Dormans; Cornelis P C de Jager; Stefaan H A Hendriks; Sefanja Achterberg; Evelien Oostdijk; Auke C Reidinga; Barbara Festen-Spanjer; Gert B Brunnekreef; Alexander D Cornet; Walter van den Tempel; Age D Boelens; Peter Koetsier; Judith Lens; Harald J Faber; A Karakus; Robert Entjes; Paul de Jong; Thijs C D Rettig; Sesmu Arbous; Sebastiaan J J Vonk; Mattia Fornasa; Tomas Machado; Taco Houwert; Hidde Hovenkamp; Roberto Noorduijn Londono; Davide Quintarelli; Martijn G Scholtemeijer; Aletta A de Beer; Giovanni Cinà; Adam Kantorik; Tom de Ruijter; Willem E Herter; Martijn Beudel; Armand R J Girbes; Mark Hoogendoorn; Patrick J Thoral; Paul W G Elbers Journal: Crit Care Date: 2021-12-27 Impact factor: 9.097