COVID-19, maternal, and neonatal outcomes: National Mother-Child Cohort (NMCC) of K-COV-N cohort in South Korea.

Throughout the COVID-19 pandemic, pregnant women have been classified as a vulnerable population. However, the evidence on the effect of infection during pregnancy on maternal and neonatal outcomes is still uncertain, and related research comprising a large population of pregnant women in Asian countries is limited. We constructed a national cohort including mothers and children (369,887 pairs) registered in the Prevention Agency-COVID-19-National Health Insurance Service (COV-N), from January 1, 2020 to March 31, 2022. We performed propensity score matchings and generalized estimation equation models to estimate the effect of COVID-19 on maternal and neonatal outcomes. In summary, we found little evidence of the effect of COVID-19 infection during pregnancy on maternal and neonatal outcomes; however, a relationship between COVID-19 infection in the second trimester and postpartum hemorrhages was discovered (Odds ratio (OR) of Delta period: 2.26, 95% Confidence intervals (CI): 1.26, 4.05). In addition, neonatal intensive care unit (NICU) admissions increased due to COVID-19 infection (pre-Delta period: 2.31, 95% CI: 1.31, 4.10; Delta period: 1.99, 95% CI: 1.47, 2.69; Omicron period: 2.36, 95% CI: 1.75, 3.18). Based on the national retrospective cohort study data, this study investigated the effects of COVID-19 infection on maternal and neonatal outcomes in Korea from the pre-Delta to the initial Omicron epidemic periods. Our evidence suggests that the timely and successful policies of the government and academia in response to COVID-19 infections in newborns in Korea may cause an increase in NICU admissions, but nonetheless, they prevent adverse maternal and neonatal outcomes simultaneously.

A COVID-19 mortality prediction model for Korean patients using nationwide Korean disease control and prevention agency database.

The experience of the early nationwide COVID-19 pandemic in South Korea led to an early shortage of medical resources. For efficient resource allocation, accurate prediction of the prognosis or mortality of confirmed patients is essential. Therefore, the aim of this study was to develop an accurate model for predicting COVID-19 mortality using epidemiolocal and clinical variables and for identifying a high-risk group of confirmed patients. Clinical and epidemiolocal variables of 4049 patients with confirmed COVID-19 between January 20, 2020 and April 30, 2020 collected by the Korean Disease Control and Prevention Agency were used. Among the 4049 total confirmed patients, 223 patients died, while 3826 patients were released from isolation. Patients who had the following risk factors showed significantly higher risk scores: age over 60 years, male sex, difficulty breathing, diabetes, cancer, dementia, change of consciousness, and hospitalization in the intensive care unit. High accuracy was shown for both the development set (n = 2467) and the validation set (n = 1582), with AUCs of 0.96 and 0.97, respectively. The prediction model developed in this study based on clinical features and epidemiological factors could be used for screening high-risk groups of patients and for evidence-based allocation of medical resources.