Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.
Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.
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DOI：
10.1038/s41746-021-00388-6
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发表时间：
2021-02-19
影响因子：
15.2
通讯作者：
Martin L
中科院分区：
文献类型：
--
作者：
Peine A;Hallawa A;Bickenbach J;Dartmann G;Fazlic LB;Schmeink A;Ascheid G;Thiemermann C;Schuppert A;Kindle R;Celi L;Marx G;Martin L
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.
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影响因子：
38.9
作者：
Bein, Thomas;Weber-Carstens, Steffen;Goldmann, Anton;Mueller, Thomas;Staudinger, Thomas;Brederlau, Joerg;Muellenbach, Ralf;Dembinski, Rolf;Graf, Bernhard M.;Wewalka, Marlene;Philipp, Alois;Wernecke, Klaus-Dieter;Lubnow, Matthias;Slutsky, Arthur S.
通讯作者：
Slutsky, Arthur S.
影响因子：
120.7
作者：
Mercat, Alain;Richard, Jean-Christophe M.;Brochard, Laurent
通讯作者：
Brochard, Laurent
影响因子：
158.5
作者：
Amato, MBP;Barbas, CSV;Carvalho, CRR
通讯作者：
Carvalho, CRR
影响因子：
9.8
作者：
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者：
Mark RG
影响因子：
9.8
作者：
Pollard TJ;Johnson AEW;Raffa JD;Celi LA;Mark RG;Badawi O
通讯作者：
Badawi O