Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

Abstract The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mech...

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Autores principales:	Arne Peine, Ahmed Hallawa, Johannes Bickenbach, Guido Dartmann, Lejla Begic Fazlic, Anke Schmeink, Gerd Ascheid, Christoph Thiemermann, Andreas Schuppert, Ryan Kindle, Leo Celi, Gernot Marx, Lukas Martin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/cbf3e67d243d4b4c9450953527ffd35b
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spelling	oai:doaj.org-article:cbf3e67d243d4b4c9450953527ffd35b2021-12-02T10:54:07ZDevelopment and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care10.1038/s41746-021-00388-62398-6352https://doaj.org/article/cbf3e67d243d4b4c9450953527ffd35b2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00388-6https://doaj.org/toc/2398-6352Abstract The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.Arne PeineAhmed HallawaJohannes BickenbachGuido DartmannLejla Begic FazlicAnke SchmeinkGerd AscheidChristoph ThiemermannAndreas SchuppertRyan KindleLeo CeliGernot MarxLukas MartinNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Arne Peine Ahmed Hallawa Johannes Bickenbach Guido Dartmann Lejla Begic Fazlic Anke Schmeink Gerd Ascheid Christoph Thiemermann Andreas Schuppert Ryan Kindle Leo Celi Gernot Marx Lukas Martin Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
description	Abstract The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
format	article
author	Arne Peine Ahmed Hallawa Johannes Bickenbach Guido Dartmann Lejla Begic Fazlic Anke Schmeink Gerd Ascheid Christoph Thiemermann Andreas Schuppert Ryan Kindle Leo Celi Gernot Marx Lukas Martin
author_facet	Arne Peine Ahmed Hallawa Johannes Bickenbach Guido Dartmann Lejla Begic Fazlic Anke Schmeink Gerd Ascheid Christoph Thiemermann Andreas Schuppert Ryan Kindle Leo Celi Gernot Marx Lukas Martin
author_sort	Arne Peine
title	Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
title_short	Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
title_full	Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
title_fullStr	Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
title_full_unstemmed	Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
title_sort	development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cbf3e67d243d4b4c9450953527ffd35b
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Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

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