• Elias N Baedorf-Kassis, Jakub Glowala, Károly Bence Póka, Federico Wadehn, Johannes Meyer, and Daniel Talmor.
• Division of Pulmonary and Critical Care Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Anesthesia, Critical Care and Pain, Beth Israel Deaconess Medical Center, Boston, MA, USA. Electronic address: enbaedor@bidmc.harvard.edu.
• J Crit Care. 2023 Jun 1; 75: 154256154256.

PurposeDyssynchrony may cause lung injury and is associated with worse outcomes in mechanically ventilated patients. Reverse triggering (RT) is a common type of dyssynchrony presenting with several phenotypes which may directly cause lung injury and be difficult to identify. Due to these challenges, automated software to assist in identification is needed.Materials And MethodsThis was a prospective observational study using a training set of 15 patients and a validation dataset of 13 patients. RT events were manually identified and compared with "rules-based" programs (with and without esophageal manometry and reverse triggering with breath stacking), and were used to train a neural network artificial intelligence (AI) program. RT phenotypes were identified using previously defined rules. Performance of the programs was compared via sensitivity, specificity, positive predictive value (PPV) and F1 score.Results33,244 breaths were manually analyzed, with 8718 manually identified as reverse-triggers. The rules-based and AI programs yielded excellent specificity (>95% in all programs) and F1 score (>75% in all programs). RT with breath stacking (24.4%) and mid-cycle RT (37.8%) were the most common phenotypes.ConclusionsAutomated detection of RT demonstrated good performance, with the potential application of these programs for research and clinical care.Copyright © 2023 Elsevier Inc. All rights reserved.

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