Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths.

Korea Seroprevalence Study of Monitoring of SARS-COV-2 Antibody Retention and Transmission (K-SEROSMART): findings from national representative sample.

OBJECTIVES: We estimated the population prevalence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including unreported infections, through a Korea Seroprevalence Study of Monitoring of SARS-CoV-2 Antibody Retention and Transmission (K-SEROSMART) in 258 communities throughout Korea. METHODS: In August 2022, a survey was conducted among 10,000 household members aged 5 years and older, in households selected through two stage probability random sampling. During face-to-face household interviews, participants self-reported their health status, COVID-19 diagnosis and vaccination history, and general characteristics. Subsequently, participants visited a community health center or medical clinic for blood sampling. Blood samples were analyzed for the presence of antibodies to spike proteins (anti-S) and antibodies to nucleocapsid proteins (anti-N) SARS-CoV-2 proteins using an electrochemiluminescence immunoassay. To estimate the population prevalence, the PROC SURVEYMEANS statistical procedure was employed, with weighting to reflect demographic data from July 2022. RESULTS: In total, 9,945 individuals from 5,041 households were surveyed across 258 communities, representing all basic local governments in Korea. The overall population-adjusted prevalence rates of anti-S and anti-N were 97.6% and 57.1%, respectively. Since the Korea Disease Control and Prevention Agency has reported a cumulative incidence of confirmed cases of 37.8% through July 31, 2022, the proportion of unreported infections among all COVID-19 infection was suggested to be 33.9%. CONCLUSIONS: The K-SEROSMART represents the first nationwide, community-based seroepidemiologic survey of COVID-19, confirming that most individuals possess antibodies to SARS-CoV-2 and that a significant number of unreported cases existed. Furthermore, this study lays the foundation for a surveillance system to continuously monitor transmission at the community level and the response to COVID-19.

Classification of Type 2 Diabetes Incidence Risk and the Health Behavior of the 30-50-Year-Old Korean Adults: Latent Class Analysis.

This study aimed to categorize the risk of type 2 diabetes mellitus development (T2DD) in the 30-50-year-old (3050) Korean adults and establish a baseline framework of customized management to prevent the progression to diabetes. A total of 9515 participants were enrolled in the Korea National Health and Nutrition Examination Survey (KNHANES) 2016-2019. Latent class analysis (LCA) was performed based on the health behaviors that were obtained from the secondary data source and were considered to affect T2DD. The major results were compared by latent class, multinomial regression analysis was performed, and the predicted risk of T2DD was evaluated using a self-assessment tool for Korean adults. Data analysis was performed using SPSS (ver. 25.0) and Mplus (ver. 8.6). The latent classes were divided into four categories: negative abdominal obesity and high-risk health behavior (Class A) (28.2%), negative abdominal obesity and low-risk health behavior (Class B) (37.1%), positive abdominal obesity and high-risk health behavior (Class C) (10.7%), and positive abdominal obesity and low-risk health behavior (Class D) (23.9%). The predicted risk scores for T2DD were 6.27 (Class C), 4.50 (Class D), 3.58 (Class A), and 2.16 (Class B), with a higher score indicating a worse state. Significant differences were observed in the predicted risk of T2DD between the latent classes, and abdominal obesity increased the risk. When managing the 30s-50s Korean generation physical activity and abdominal obesity control are strongly recommended.