Clinical Characteristics of COVID-19: Risk Factors for Early Oxygen Requirement after Hospitalization

Coronary heart disease as a risk factor for delirium in older COVID-19 patients

AIM

Delirium during acute infection is associated with poor outcomes, although research is limited due to the heterogenous nature of affected populations. Identifying risk factors for delirium can help differentiate high-risk patients.

METHODS

This retrospective study was carried out at a single COVID-19 designated hospital in Korea between February 2020 and May 2022. It analyzed the clinical and laboratory characteristics of COVID-19 patients aged ≥75 years. COVID-19, ranging from mild to severe, was diagnosed by polymerase chain reaction test, and delirium was diagnosed clinically. A binary logistic regression analysis was carried out using meaningful variables from descriptive analyses.

RESULTS

Among the 1166 patients in the study population, 53 (4.5%) experienced delirium during the admission period. Patients with delirium were more likely to have been transferred from another hospital (24.5% vs. 12.4%, P = 0.018) and had higher body temperatures (37.4°C vs 37.1°C, P = 0.008). They also had a history of coronary heart disease, chronic kidney disease more frequently and showed higher C-reactive protein (19.2% vs 7.1%, P = 0.003; 13.5% vs 4.5%, P = 0.009; 6.2 mg/dL vs 4.8 mg/dL, P = 0.026). Patients with delirium more frequently required oxygen support, mechanical ventilation and transfer to a higher-level hospital (54.7% vs 35.7%, P = 0.008; 13.2% vs 4.1%, P = 0.006; 17.0% vs 7.0%, P < 0.001). Coronary heart disease was associated with a significantly higher odds ratio of 2.898 (95% confidence interval 1.118-6.696, P = 0.0182) in a multiple regression model.

CONCLUSION

A history of coronary heart disease was associated with a high risk of developing delirium during hospitalization for COVID-19 in patients aged ≥75 years. Patients with delirium experienced worse COVID-19-related outcomes. Geriatr Gerontol Int 2025; 25: 560-564.

Hospitalised COVID-19 outcomes are predicted by hypoxaemia and pneumonia phenotype irrespective of the timing of their emergence

Despite the known clinical importance of hypoxemia and pneumonia, there is a paucity of evidence for these variables with respect to risk of mortality and short-term outcomes among those hospitalised with COVID-19.

OBJECTIVE

Describe the prevalence and clinical course of patients hospitalised with COVID-19 based on oxygenation and pneumonia status at presentation and determine the incidence of emergent hypoxaemia or radiographic pneumonia during admission.

METHODS

A retrospective study was conducted using a Canadian regional registry. Patients were stratified according to hypoxaemia/pneumonia phenotype and prevalence. Clinical parameters were compared between phenotypes using χ² and one-way Analysis of variance (ANOVA). Cox analysis estimated adjusted Hazard Ratios (HR) for associations between disease outcomes and phenotypes.

RESULTS

At emergency department (ED) admission, the prevalence of pneumonia and hypoxaemia was 43% and 50%, respectively, and when stratified to phenotypes: 28.2% hypoxaemia⁺/pneumonia⁺, 22.2% hypoxaemia⁺/pneumonia^-, 14.5% hypoxaemia^-/pneumonia⁺ and 35.1% hypoxaemia^-/pneumonia^-. Mortality was 31.1% in the hypoxaemia⁺/pneumonia^- group and 26.3% in the hypoxaemia⁺/pneumonia⁺ group. Hypoxaemia with pneumonia and without pneumonia predicted higher probability of death. Hypoxaemia either <24 hours or ≥24 hours after hospitalisation predicted higher mortality and need for home oxygen compared with those without hypoxaemia. Patients with early hypoxaemia had higher probability of Intensive care unit (ICU) admission compared with those with late hypoxaemia.

CONCLUSION

Mortality in COVID-19 infection is predicted by hypoxaemia with or without pneumonia and was greatest in patients who initially presented with hypoxaemia. The emergence of hypoxaemia was predicted by radiographic pneumonia. Patients with early and emergent hypoxaemia had similar mortality but were less likely to be admitted to ICU. There may be delayed identification of hypoxaemia, which prevents timely escalation of care.

Impact of Mediastinal Lymphadenopathy on the Severity of COVID-19 Pneumonia: A Nationwide Multicenter Cohort Study

BACKGROUND

We analyzed the differences between clinical characteristics and computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) to establish potential relationships with mediastinal lymphadenopathy and clinical outcomes.

METHODS

We compared the clinical characteristics and CT findings of COVID-19 patients from a nationwide multicenter cohort who were grouped based on the presence or absence of mediastinal lymphadenopathy. Differences between clinical characteristics and CT findings in these groups were analyzed. Univariate and multivariate analyses were performed to determine the impact of mediastinal lymphadenopathy on clinical outcomes.

RESULTS

Of the 344 patients included in this study, 53 (15.4%) presented with mediastinal lymphadenopathy. The rate of diffuse alveolar damage pattern pneumonia and the visual CT scores were significantly higher in patients with mediastinal lymphadenopathy than in those without (P < 0.05). A positive correlation between the number of enlarged mediastinal lymph nodes and visual CT scores was noted in patients with mediastinal lymphadenopathy (Spearman's ρ = 0.334, P < 0.001). Multivariate analysis showed that mediastinal lymphadenopathy was independently associated with a higher risk of intensive care unit (ICU) admission (odds ratio, 95% confidence interval; 3.25, 1.06-9.95) but was not significantly associated with an increased risk of in-hospital death in patients with COVID-19.

CONCLUSION

COVID-19 patients with mediastinal lymphadenopathy had a larger extent of pneumonia than those without. Multivariate analysis adjusted for clinical characteristics and CT findings revealed that the presence of mediastinal lymphadenopathy was significantly associated with ICU admission.

Clinical Characteristics and Risk Factors for Severe Disease of Coronavirus Disease 2019 in a Low Case Fatality Rate Region in Korea

Background

Jeju island had the seventh highest incidence rate of coronavirus disease 2019 (COVID-19) but showed the lowest case fatality rate among 17 provinces of Korea, which may be associated with comorbidities and geographic differences. This study aimed to analyze the epidemiological and clinical characteristics of patients with COVID-19 and evaluate the risk factors for severe COVID-19 in Jeju island, Korea.

Materials and Methods

All patients with COVID-19 admitted between February 20, 2020, and June 19, 2021, at a single center were retrospectively enrolled in this study. The severity of illness was defined using five categories (asymptomatic, mild, moderate, severe, and critical) according to the National Institute of Health criteria. Then, patients with severe and critical illness were grouped into a severe group, whereas patients with asymptomatic, mild, and moderate illness were grouped into a non-severe group. Multivariate logistic regression analysis was performed using risk factors that were found to be significantly associated with the severe group.

Results

This study included 348 patients with a median age was 57 years, and 37.5% were aged 60 or older. Among them, 43.4% were male and 10.9% were asymptomatic, whereas 41.4%, 33.9%, 12.9%, and 1.1% had mild, moderate, severe, and critical illness. The all-cause mortality of patients with COVID-19 was 0.28% (1/348). Among confirmed patients with COVID-19, exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was mainly within Jeju island (75.9%). The mean body mass index was 24.09 ± 4.04 kg/m², the median comorbidity index score was low in each group (0 in asymptomatic; 1 in mild; 1 in moderate; 1 in severe; and 2 in critical group, P <0.548). In the multivariable analysis, male sex [odds ratio (OR), 6.37; 95% confidence interval (CI), 2.69 – 15.13; P <0.001], ≥65 years of age (OR, 2.68; 95% CI, 1.18 – 6.10; P <0.019), chronic pulmonary disease (OR, 6.10; 95% CI, 1.40 – 26.61; P = 0.016), and length of fever duration (OR, 1.33; 95% CI, 1.19 – 1.49; P <0.001) were independently associated with severe COVID-19.

Conclusion

The most relevant risk factors of COVID-19 severity were male sex, older age, underlying chronic lung diseases, and duration of fever during hospitalization. The risk factors for severe COVID-19 were not significantly different from those reported in other studies. However, a lower proportion of the older population among confirmed SARS-CoV-2 cases might contribute to the lower fatality rate than the national rate.

Effectiveness of Regdanvimab Treatment in High-Risk COVID-19 Patients to Prevent Progression to Severe Disease

Objective

To evaluate clinical effectiveness of regdanvimab, a monoclonal antibody agent for treating coronavirus 2019 (COVID-19).

Methods

A retrospective cohort study was conducted at two general hospitals during the study period of December 2020 to May 2021. Mild COVID-19 patients with risk factors for disease progression admitted to the hospitals within seven days of symptom onset were enrolled and followed until discharge or referral. Multivariate analyses for disease progression were conducted in the total and propensity score (PS)-matched cohorts.

Results

A total of 778 mild COVID-19 patients were included and classified as the regdanvimab (n = 234) and supportive care (n = 544) groups. Significantly fewer patients required O₂ supplementation via nasal prong in the regdanvimab group (8.1%) than in the supportive care group (18.4%, P < 0.001). The decreased risk for O₂ support by regdanvimab treatment was noticed in the multivariate analysis of the total cohort (HR 0.570, 95% CI 0.343-0.946, P = 0.030), but it was not statistically significant in the PS-matched cohort (P = 0.057). Progression to severe disease was also significantly lower in the regdanvimab group (2.1%) than in the supportive care group (9.6%, P < 0.001). The significantly reduced risk for progression to severe disease by regdanvimab treatment was observed in the analysis of both the total cohort (HR 0.262, 95% CI 0.103-0.667, P = 0.005) and PS-matched cohort (HR 0.176, 95% CI 0.060-0.516, P = 0.002). Potential risk factors for progression were investigated in the supportive care group and SpO₂ < 97% and CRP elevation >1.5 mg/dL were common risk factors for O₂ support and progression to severe disease. Among the patients with any of these factors, regdanvimab treatment was associated with decreased risk for progression to severe disease with slightly lower HR (HR 0.202, 95% CI 0.062-0.657, P = 0.008) than that of the total cohort.

Conclusion

Regdanvimab treatment was associated with a decreased risk of progression to severe disease.

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

BACKGROUND

Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases.

METHODS

This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set.

RESULTS

Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/.

CONCLUSION

The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.