Abstract Body: Introduction: Successful resuscitation of ventricular fibrillation (VF) out-of-hospital cardiac arrest (OHCA) relies on timely defibrillation and minimally-interrupted CPR. Defibrillator shock decision analysis has traditionally required CPR interruption because CPR causes electrical artifacts in the ECG signal. Recent emerging defibrillator algorithms have been proposed to reduce or eliminate CPR interruption for shock decision analysis. However, these methods are challenged by lower sensitivity, a high proportion of indeterminates, or requirement for CPR-free rhythm confirmation.
Aim: We sought to determine whether a deep learning algorithm can accurately detect shockable rhythms during CPR.
Methods: We performed a retrospective cohort study of adult VF-OHCA cases in King County WA from 2006-2021. Patients were randomized into training (60%), validation (20%), and test (20%) groups. We annotated the entirety of defibrillator paddle ECG recordings from cohort patients as non-shockable (Asystole, Organized Rhythms) or shockable (VF, Ventricular Tachycardia). ECGs were segmented into non-overlapping 2.5-s clips. The presence of CPR was confirmed by review of thoracic impedance. The algorithm comprised two steps: (1) A deep convolutional neural network predicted individual clip classes based on ECG scalogram images, and (2) a deep long short-term memory recurrent network incorporated the sequence of prior clip predictions to inform each clip’s current-time prediction.
Results: Of 2682 eligible patients, N=2011 (75%) with available defibrillator files were included in the cohort; 1207, 402, and 402 patients were used for algorithm training, validation, and test, respectively. A total of 1047601 2.5-s ECG clips were collected from the cohort, with 604484 (58%) collected during CPR. During CPR, algorithm sensitivity/specificity for detecting shockable rhythms in training, validation, and test data were 99.0%/99.0%, 97.4%/98.9%, and 99.1%/98.7% respectively (Table 1). Of the CPR clips, 99628 (16.5%) were predicted as indeterminate by the algorithm and not scored. When indeterminate decisions were disallowed, algorithm sensitivity/specificity values in training, validation, and test groups were 92.7%/98.7%, 90.8%/97.6%, and 91.8%/97.9%, respectively.
Conclusions: A deep learning algorithm developed using >1 million ECG segments can accurately detect shockable rhythms during CPR, suggesting potential to reduce CPR interruption and improve VF-OHCA resuscitation.