Shin Seung Jae, Bae Hee Sun, Moon Hyung Jun, Kim Gi Woon, Cho Young Soon, Lee Dong Wook, Jeong Dong Kil, Kim Hyun Joon, Lee Hyun Jung
Department of Industrial and System Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea.
Department of Emergency Medicine, College of Medicine, Soonchunhyang University, Republic of Korea.
Am J Emerg Med. 2023 Jan;63:29-37. doi: 10.1016/j.ajem.2022.10.011. Epub 2022 Oct 14.
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).
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.
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.
The CAPD exhibits potential and effectiveness in identifying patients with ROSC and favorable neurological outcomes for prehospital resuscitation.
本研究旨在开发一种使用深度学习（CAPD）算法的心脏骤停预测模型，并通过评估院外心脏骤停患者预后随现场时间间隔（STI）增加的变化来验证所开发的算法。
我们使用国家急救中心在2016年1月至2019年12月收集的智能高级生命支持试验数据进行了一项回顾性队列研究。智能高级生命支持数据被随机分为推导数据集和验证数据集。将使用患者的年龄、性别、事件目击者、旁观者心肺复苏（CPR）、肾上腺素给药、初始可电击心律、院前除颤、高级生命支持的提供、响应时间间隔和STI作为预测变量来预测患者预后的CAPD模型的性能与传统机器学习方法进行比较。在固定输入数据的其他值后，观察患者预后随STI增加的变化。
本研究共纳入16992例患者。预测院前自主循环恢复（ROSC）和良好神经功能结局的受试者工作特征曲线下面积值分别为0.828（95%置信区间0.826 - 0.830）和0.907（0.914 - 0.910）。我们的算法显著优于其他人工智能算法和传统方法。当STI为28分钟时，神经恢复率预计降至心肺复苏开始时的1/3，当STI为30分钟时，院前ROSC预计降至其初始水平的1/2。
CAPD在识别院前复苏中有ROSC和良好神经功能结局的患者方面具有潜力和有效性。
