Developing a Time-Adaptive Prediction Model for Out-of-Hospital Cardiac Arrest: Nationwide Cohort Study in Korea

• Published in: Journal of medical Internet research Vol. 23; no. 7; p. e28361
• Main Authors: Kim, Ji Woong, Ha, Juhyung, Kim, Taerim, Yoon, Hee, Hwang, Sung Yeon, Jo, Ik Joon, Shin, Tae Gun, Sim, Min Seob, Kim, Kyunga, Cha, Won Chul
• Format: Journal Article
• Language: English
• Published: Toronto Gunther Eysenbach MD MPH, Associate Professor 05.07.2021; JMIR Publications
• Subjects: Algorithms; Arrests; Artificial intelligence; Cardiac arrest; Cardiopulmonary resuscitation; Clinical outcomes; Cohort analysis; CPR; Datasets; Decision making; Electrocardiography; Emergency services; Hospitals; Machine learning; Medical prognosis; Multimedia; Myocardial infarction; Neural networks; Original Paper; Patients; Performance evaluation; Prediction models; Public health; Termination; Validity
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/28361

Background Out-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation. Objective This study aimed to develop a time-adaptive conditional prediction model (TACOM) to predict clinical outcomes every minute. Methods We performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we excluded patients with trauma, those who experienced return of spontaneous circulation before arriving in the emergency department (ED), and those who did not receive cardiopulmonary resuscitation (CPR) in the ED. We selected patients who received CPR in the ED. To develop the time-adaptive prediction model, we organized the training data set as ongoing CPR patients by the minute. A total of 49,669 patients were divided into 39,602 subjects for training and 10,067 subjects for validation. We compared random forest, LightGBM, and artificial neural networks as the prediction model methods. Model performance was quantified using the prediction probability of the model, area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve. Results Among the three algorithms, LightGBM showed the best performance. From 0 to 30 min, the AUROC of the TACOM for predicting good neurological outcomes ranged from 0.910 (95% CI 0.910-0.911) to 0.869 (95% CI 0.865-0.871), whereas that for survival to hospital discharge ranged from 0.800 (95% CI 0.797-0.800) to 0.734 (95% CI 0.736-0.740). The prediction probability of the TACOM showed similar flow with cohort data based on a comparison with the conventional model’s prediction probability. Conclusions The TACOM predicted the clinical outcome of OHCA patients per minute. This model for predicting patient outcomes by the minute can assist clinicians in making rational decisions for OHCA patients.