TY - JOUR A1 - Peine, Arne A1 - Hallawa, Ahmed A1 - Bickenbach, Johannes A1 - Dartmann, Guido A1 - Begic Fazlic, Lejla A1 - Schmeink, Anke A1 - Ascheid, Gerd A1 - Thiemermann, Christoph A1 - Schuppert, Andreas A1 - Kindle, Ryan A1 - Celi, Leo A1 - Marx, Gernot A1 - Martin, Lukas T1 - Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care T2 - npj Digital Medicine N2 - 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. KW - Künstliche Beatmung KW - Maschinelles Lernen KW - critically-ill patient KW - ventilation KW - reinforcement learning algorithm KW - VentAI Y1 - 2021 UR - https://hst.opus.hbz-nrw.de/frontdoor/index/index/docId/171 UR - https://nbn-resolving.org/urn:nbn:de:hbz:tr5-1710 VL - 4 SP - 1 EP - 12 PB - Nature Portfolio ER -