Background: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization. Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge. Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3–4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty. Conclusion Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Background: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization. Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge. Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3–4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty. Conclusion Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Background: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization. Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge. Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3–4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty. Conclusion Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Background: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization. Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge. Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3–4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty. Conclusion Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Background: The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization. Methods: We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge. Results: Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3–4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty. Conclusion Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Background: Endothelial activation and stress index (EASIX) reflects endothelial dysfunction or damage. Because endothelial dysfunction is one of the key mechanisms, a few studies have shown the clinical usefulness of original and age-adjusted EASIX (age-EASIX) in patients with coronavirus disease 2019 (COVID-19). We aimed to evaluate the clinical utility of age-EASIX in predicting intensive care unit (ICU) mortality in critically ill patients with COVID-19 in South Korea. Methods: Secondary analysis was performed using clinical data retrospectively collected from 22 nationwide hospitals in South Korea between January 1, 2020, and August 31, 2021. Patients were at least 19 years old and admitted to the ICU for severe COVID-19, demanding at least high-flow nasal cannula oxygen therapy. EASIX [lactate dehydrogenase (U/L)×creatinine (mg/dL)/platelet count (109 cells/L)] and age-EASIX (EASIX×age) were calculated and log2-transformed. Results: The mean age of 908 critically ill patients with COVID-19 was 67.4 years with 59.7% male sex. The mean log2 age-EASIX was 7.38±1.45. Non-survivors (n=222, 24.4%) in the ICU had a significantly higher log2 age-EASIX than of survivors (8.2±1.52 vs. 7.1±1.32, p<0.001). log2 age-EASIX was significantly associated with ICU mortality (odds ratio, 1.541; 95% confidence interval, 1.322 to 1.796; p<0.001) and had a better area under the receiver operating characteristic curve than of the sequential organ failure assessment (SOFA) score in predicting ICU mortality (0.730 vs. 0.660, p=0.001). Conclusion Age-EASIX is significantly associated with ICU mortality and has better discriminatory ability than the SOFA score in predicting ICU mortality.

Background: The objective of this study was to investigate whether alcohol consumption might affect the quality of life (QOL), depressive mood, and metabolic syndrome in patients with obstructive lung disease (OLD). Methods: Data were obtained from the Korean National Health and Nutrition Examination Survey from 2014 and 2016. OLD was defined as spirometry of forced expiratory volume in 1 second/forced vital capacity <0.7 in those aged more than 40 years. QOL was evaluated using the European Quality of Life Questionnaire-5D (EQ-5D) index. Patient Health Questionnaire-9 (PHQ-9) was used to assess the severity of depressive mood. Alcohol consumption was based on a history of alcohol ingestion during the previous month. Results: A total of 984 participants with OLD (695 males, 289 females, age 65.8±9.7 years) were enrolled. The EQ-5D index was significantly higher in alcohol drinkers (n=525) than in non-alcohol drinkers (n=459) (0.94±0.11 vs. 0.91±0.13, p=0.002). PHQ- 9 scores were considerably lower in alcohol drinkers than in non-alcohol drinkers (2.15±3.57 vs. 2.78±4.13, p=0.013). However, multiple logistic regression analysis showed that alcohol consumption was not associated with EQ-5D index or PHQ-9 score. Body mass index ≥25 kg/m2, triglyceride ≥150 mg/dL, high-density lipoprotein <40 mg/dL in men and <50 mg/dL in women, and blood pressure ≥130/85 mm Hg were significantly more common in alcohol drinkers than in non-alcohol drinkers (all p<0.05). Conclusion Alcohol consumption did not change the QOL or depressive mood of OLD patients. However, metabolic syndrome-related factors were more common in alcohol drinkers than in non-alcohol drinkers.

Background: We investigated whether nutritional intake is associated with physical activity (PA) and handgrip strength (HGS) in individuals with airflow limitation. Methods: This study analyzed data from the 2014 and 2016 Korean National Health and Nutrition Examination Survey. We assessed total protein intake (g/day), caloric intake (kcal/day), and other nutritional intakes, using a 24-hour dietary recall questionnaire. HGS was measured three times for each hand using a digital grip strength dynamometer, and PA was assessed as health-enhancing PA. Airflow limitation was defined as a forced expiratory volume/forced vital capacity ratio of 0.7 in individuals over 40 years of age. Participants were categorized into groups based on their PA levels and HGS measurements: active aerobic PA vs. non-active aerobic PA, and normal HGS vs. low HGS. Results: Among the 622 individuals with airflow limitation, those involved in active aerobic PA and those with higher HGS had notably higher total food, calorie, water, protein, and lipid intake. The correlations between protein and caloric intake with HGS were strong (correlation coefficients=0.344 and 0.346, respectively). The forest plots show that higher intakes of food, water, calories, protein, and lipids are positively associated with active aerobic PA, while higher intakes of these nutrients are inversely associated with low HGS. However, in the multivariate logistic regression analysis, no significant associations were observed between nutritional intake and active aerobic PA or HGS. Conclusion Nutritional intake was found to not be an independent factor associated with PA and HGS. However, the observed correlations suggest potential indirect effects that warrant further investigation.

Background: We investigated whether nutritional intake is associated with physical activity (PA) and handgrip strength (HGS) in individuals with airflow limitation. Methods: This study analyzed data from the 2014 and 2016 Korean National Health and Nutrition Examination Survey. We assessed total protein intake (g/day), caloric intake (kcal/day), and other nutritional intakes, using a 24-hour dietary recall questionnaire. HGS was measured three times for each hand using a digital grip strength dynamometer, and PA was assessed as health-enhancing PA. Airflow limitation was defined as a forced expiratory volume/forced vital capacity ratio of 0.7 in individuals over 40 years of age. Participants were categorized into groups based on their PA levels and HGS measurements: active aerobic PA vs. non-active aerobic PA, and normal HGS vs. low HGS. Results: Among the 622 individuals with airflow limitation, those involved in active aerobic PA and those with higher HGS had notably higher total food, calorie, water, protein, and lipid intake. The correlations between protein and caloric intake with HGS were strong (correlation coefficients=0.344 and 0.346, respectively). The forest plots show that higher intakes of food, water, calories, protein, and lipids are positively associated with active aerobic PA, while higher intakes of these nutrients are inversely associated with low HGS. However, in the multivariate logistic regression analysis, no significant associations were observed between nutritional intake and active aerobic PA or HGS. Conclusion Nutritional intake was found to not be an independent factor associated with PA and HGS. However, the observed correlations suggest potential indirect effects that warrant further investigation.

Background: We investigated whether nutritional intake is associated with physical activity (PA) and handgrip strength (HGS) in individuals with airflow limitation. Methods: This study analyzed data from the 2014 and 2016 Korean National Health and Nutrition Examination Survey. We assessed total protein intake (g/day), caloric intake (kcal/day), and other nutritional intakes, using a 24-hour dietary recall questionnaire. HGS was measured three times for each hand using a digital grip strength dynamometer, and PA was assessed as health-enhancing PA. Airflow limitation was defined as a forced expiratory volume/forced vital capacity ratio of 0.7 in individuals over 40 years of age. Participants were categorized into groups based on their PA levels and HGS measurements: active aerobic PA vs. non-active aerobic PA, and normal HGS vs. low HGS. Results: Among the 622 individuals with airflow limitation, those involved in active aerobic PA and those with higher HGS had notably higher total food, calorie, water, protein, and lipid intake. The correlations between protein and caloric intake with HGS were strong (correlation coefficients=0.344 and 0.346, respectively). The forest plots show that higher intakes of food, water, calories, protein, and lipids are positively associated with active aerobic PA, while higher intakes of these nutrients are inversely associated with low HGS. However, in the multivariate logistic regression analysis, no significant associations were observed between nutritional intake and active aerobic PA or HGS. Conclusion Nutritional intake was found to not be an independent factor associated with PA and HGS. However, the observed correlations suggest potential indirect effects that warrant further investigation.