Evaluation of optimal scene time interval for out-of-hospital cardiac arrest using a deep neural network.

AIM: This study aims to develop a cardiac arrest prediction model using deep learning (CAPD) algorithm and to validate the developed algorithm by evaluating the change in out-of-hospital cardiac arrest patient prognosis according to the increase in scene time interval (STI). METHODS: We conducted a retrospective cohort study using smart advanced life support trial data collected by the National Emergency Center from January 2016 to December 2019. The smart advanced life support data were randomly partitioned into derivation and validation datasets. The performance of the CAPD model using the patient's age, sex, event witness, bystander cardiopulmonary resuscitation (CPR), administration of epinephrine, initial shockable rhythm, prehospital defibrillation, provision of advanced life support, response time interval, and STI as prediction variables for prediction of a patient's prognosis was compared with conventional machine learning methods. After fixing other values of the input data, the changes in prognosis of the patient with respect to the increase in STI was observed. RESULTS: A total of 16,992 patients were included in this study. The area under the receiver operating characteristic curve values for predicting prehospital return of spontaneous circulation (ROSC) and favorable neurological outcomes were 0.828 (95% confidence interval 0.826-0.830) and 0.907 (0.914-0.910), respectively. Our algorithm significantly outperformed other artificial intelligence algorithms and conventional methods. The neurological recovery rate was predicted to decrease to 1/3 of that at the beginning of cardiopulmonary resuscitation when the STI was 28 min, and the prehospital ROSC was predicted to decrease to 1/2 of its initial level when the STI was 30 min. CONCLUSION: The CAPD exhibits potential and effectiveness in identifying patients with ROSC and favorable neurological outcomes for prehospital resuscitation.

Effects of Smart Advanced Life Support protocol implementation including CPR coaching during out-of-hospital cardiac arrest.

PURPOSE: The aim of this study was to compare out-of-hospital cardiac arrest (OHCA) outcomes before and after implementation of Smart Advanced Life Support (SALS) protocol incorporating changes in cardiopulmonary resuscitation (CPR) assistance and coaching by physicians via real-time video calls. METHODS: A prospective before-and-after multi-regional observational study was conducted between January 2014 and December 2018. In January 2016, emergency medical service (EMS) providers adopted an integrated CPR coaching by physicians via real-time video call via SALS to treat patients with OHCA focusing on high-quality cardiopulmonary resuscitation. Propensity score matching was performed to match patients. Patients' outcomes using conventional protocol were then compared with those of patients using the SALS protocol. RESULTS: Among 26,349 OHCA cases, 2351 patients and 7261 patients were enrolled during the pre-intervention and the post-intervention periods, respectively. Multivariate analysis showed that SALS was independently associated with favorable neurological outcomes [odds ratio (OR): 2.20; 95% confidence interval (CI): 1.62-2.99]. A total of 2096 patients were propensity score-matched and the two groups were well balanced. In the matched cohort, the use of SALS protocol was still associated with increased prehospital return of spontaneous circulation (ROSC) (OR: 3.83, 95% CI: 2.80-5.26), survival to discharge (OR: 1.68; 95% CI: 1.20-2.34), and favorable neurological outcomes (OR: 1.83; 95% CI: 1.19-2.82). CONCLUSION: A multidisciplinary SALS protocol for the resuscitation of patients with OHCA was associated with increased prehospital ROSC, survival to discharge, and good neurologic outcomes compared with traditional resuscitation protocol.