Dataset Information

Sex-specific difference of in-hospital mortality from COVID-19 in South Korea.

ABSTRACT: We sought to assess the impact of sex on in-hospital mortality of patients with COVID-19 infection in South Korea. The study recruited 5,628 prospective consecutive patients who were hospitalized in South Korea with COVID-19 infection, and enrolled in the Korea Centers for Disease Control and Prevention (KCDC) dataset between January 20, 2020, and April 30, 2020. The primary endpoint was in-hospital death from COVID-19. The cohort comprised of 3,308 women (59%) and 2,320 men (41%). In-hospital death was significantly lower in women than men (3.5% vs. 5.5%, hazard ratio (HR): 0.61; 95% confidence interval (CI): 0.47 to 0.79, p <0.001). Results were consistent after multivariable regression (HR: 0.59; 95% CI: 0.41 to 0.85, p = 0.023) and propensity score matching (HR: 0.51; 95% CI: 0.30 to 0.86, p = 0.012). In South Korea, women had a significantly lower risk of in-hospital death amongst those patients hospitalized with COVID-19 infection.

SUBMITTER: Her AY

PROVIDER: S-EPMC8786158 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sex-specific difference of in-hospital mortality from COVID-19 in South Korea.

Her Ae-Young AY Bhak Youngjune Y Jun Eun Jung EJ Yuan Song Lin SL Garg Scot S Lee Semin S Bhak Jong J Shin Eun-Seok ES

PloS one 20220124 1

We sought to assess the impact of sex on in-hospital mortality of patients with COVID-19 infection in South Korea. The study recruited 5,628 prospective consecutive patients who were hospitalized in South Korea with COVID-19 infection, and enrolled in the Korea Centers for Disease Control and Prevention (KCDC) dataset between January 20, 2020, and April 30, 2020. The primary endpoint was in-hospital death from COVID-19. The cohort comprised of 3,308 women (59%) and 2,320 men (41%). In-hospital d ...[more]

PMID: 35073365

Similar Datasets

Project description:BackgroundEarly identification of patients with coronavirus disease 2019 (COVID-19) who are at high risk of mortality is of vital importance for appropriate clinical decision making and delivering optimal treatment. We aimed to develop and validate a clinical risk score for predicting mortality at the time of admission of patients hospitalized with COVID-19.MethodsCollaborating with the Korea Centers for Disease Control and Prevention (KCDC), we established a prospective consecutive cohort of 5,628 patients with confirmed COVID-19 infection who were admitted to 120 hospitals in Korea between January 20, 2020, and April 30, 2020. The cohort was randomly divided using a 7:3 ratio into a development (n = 3,940) and validation (n = 1,688) set. Clinical information and complete blood count (CBC) detected at admission were investigated using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-Mortality Score). The discriminative power of the risk model was assessed by calculating the area under the curve (AUC) of the receiver operating characteristic curves.ResultsThe incidence of mortality was 4.3% in both the development and validation set. A COVID-Mortality Score consisting of age, sex, body mass index, combined comorbidity, clinical symptoms, and CBC was developed. AUCs of the scoring system were 0.96 (95% confidence interval [CI], 0.85-0.91) and 0.97 (95% CI, 0.84-0.93) in the development and validation set, respectively. If the model was optimized for > 90% sensitivity, accuracies were 81.0% and 80.2% with sensitivities of 91.7% and 86.1% in the development and validation set, respectively. The optimized scoring system has been applied to the public online risk calculator (https://www.diseaseriskscore.com).ConclusionThis clinically developed and validated COVID-Mortality Score, using clinical data available at the time of admission, will aid clinicians in predicting in-hospital mortality.

Project description:BackgroundThe first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves.MethodsIn this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression.FindingsPeak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240·4 cases per 100 000 people vs 136·0 cases per 100 000 people; admissions, 27·9 admissions per 100 000 people vs 16·1 admissions per 100 000 people; deaths, 8·3 deaths per 100 000 people vs 3·6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1·19, 95% CI 1·18-1·20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1·22, 95% CI 1·14-1·31), and older than 65 years (aOR 1·38, 1·25-1·52), compared with younger than 40 years; of Mixed race (aOR 1·21, 1·06-1·38) compared with White race; and admitted in the public sector (aOR 1·65, 1·41-1·92); and less likely to be Black (aOR 0·53, 0·47-0·60) and Indian (aOR 0·77, 0·66-0·91), compared with White; and have a comorbid condition (aOR 0·60, 0·55-0·67). For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1·31, 95% CI 1·28-1·35). In-hospital case-fatality risk increased from 17·7% in weeks of low admission (<3500 admissions) to 26·9% in weeks of very high admission (>8000 admissions; aOR 1·24, 1·17-1·32).InterpretationIn South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage.FundingDATCOV as a national surveillance system is funded by the National Institute for Communicable Diseases and the South African National Government.

Dataset Information

Sex-specific difference of in-hospital mortality from COVID-19 in South Korea.

Publications

Sex-specific difference of in-hospital mortality from COVID-19 in South Korea.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets