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Background: Coronavirus disease 2019 (COVID-19) vaccination is effective in preventing the disease transmission and progression. However, the relatively mild disease course of the omicron variant and the decrease in antibodies over time after vaccination raise questions about the effectiveness of vaccination, especially in young people. We compared the prevalence of pneumonia and chest X-ray severity score according to vaccination status among patients < 50 years old with COVID-19., Methods: From January 17 to March 17, 2022, 579 patients with COVID-19, who were < 50 years old and had a known vaccination history in our institution, were all included in this study. All patients underwent initial chest radiography, and follow-up chest radiographs were obtained every two days until discharge. Pneumonia was scored from the radiographs using the Brixia scoring system. The scores of the six lung zones were added for a total score ranging from 0 to 18. Patients were divided into four groups according to 10-year age intervals. Differences between groups were analyzed using the χ² or Fisher's exact tests for categorical variables and the Kruskal-Wallis test or analysis of variance for continuous variables., Results: Among patients aged 12-19 years, the prevalence of pneumonia did not differ depending on vaccination status (non-vaccinated vs. vaccinated, 1/47 [2.1%] vs. 1/18 [5.6%]; P = 0.577). Among patients in their 20s, the prevalence of pneumonia was significantly higher among non-vaccinated patients than among vaccinated patients (8/28, 28.6% vs. 7/138, 5.1%, P < 0.001), similar to patients in their 40s (32/52 [61.5%] vs. 18/138 [13.0%]; P < 0.001). The chest X-ray severity score was also significantly higher in non-vaccinated patients than that in vaccinated patients in their 20s to their 40s ( P < 0.001), but not among patients aged 12-19 years ( P = 0.678)., Conclusion: In patients aged 20-49 years, vaccinated patients had a significantly lower prevalence of pneumonia and chest X-ray severity score than non-vaccinated patients., Competing Interests: The authors have no potential conflicts of interest to disclose., (© 2024 The Korean Academy of Medical Sciences.)

Background: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability., Objective: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers., Methods: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods., Results: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910)., Conclusions: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice., (©Sangwon Baek, Yeon joo Jeong, Yun-Hyeon Kim, Jin Young Kim, Jin Hwan Kim, Eun Young Kim, Jae-Kwang Lim, Jungok Kim, Zero Kim, Kyunga Kim, Myung Jin Chung. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.01.2024.)

Background: The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients., Methods: From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1-2 but is indicated for groups 3-4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups., Results: Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3-4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1-4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3-4 than in groups 1-2 (group 1-4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events., Conclusion: The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities., Competing Interests: The authors have no potential conflicts of interest to disclose., (© 2023 The Korean Academy of Medical Sciences.)

Background: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19., Objective: We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19., Methods: This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration., Results: The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859)., Conclusions: The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19., (©Hyun Woo Lee, Hyun Jun Yang, Hyungjin Kim, Ue-Hwan Kim, Dong Hyun Kim, Soon Ho Yoon, Soo-Youn Ham, Bo Da Nam, Kum Ju Chae, Dabee Lee, Jin Young Yoo, So Hyeon Bak, Jin Young Kim, Jin Hwan Kim, Ki Beom Kim, Jung Im Jung, Jae-Kwang Lim, Jong Eun Lee, Myung Jin Chung, Young Kyung Lee, Young Seon Kim, Sang Min Lee, Woocheol Kwon, Chang Min Park, Yun-Hyeon Kim, Yeon Joo Jeong, Kwang Nam Jin, Jin Mo Goo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.02.2023.)

The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients’ initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696−0.788), 0.794 (0.745−0.843) and 0.770 (0.724−0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820−0.889) than that of all other models (p < 0.001, using DeLong’s test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model.

Background: As the COVID-19 (Coronavirus disease 2019) pandemic is prolonged, psychological responses to the pandemic have changed, and a new scale to reflect these changes needs to be developed. In this study, we attempt to develop and validate the COVID-19 Impact Scale (CIS) to measure the psychological stress responses of the COVID-19 pandemic, including emotional responses and difficulty with activities of daily living., Methods: We recruited 2152 participants. Participants completed the CIS, the Fear of COVID-19 Scale (FCV-19S), and other mental health related measures. The factor structure, reliability, and validity of the CIS were analyzed. In addition, the validity of the scale was confirmed by its relationships to the existing measures assessing fear of COVID-19, depression, anxiety, subjective well-being, and suicidal ideation., Results: Using exploratory factor analysis (N 1  = 1076), we derived a one-factor structure. In confirmatory factor analysis (N 2  = 1076), the one-factor model showed good to excellent fitness. The CIS was positively correlated with depression, anxiety, suicidal ideation, fear of COVID-19 and negatively correlated with subjective well-being. The FCV-19S did not show significant correlations with subjective well-being or suicidal ideation, and FCV-19S's explanatory powers on depression and anxiety were lower than those of the CIS., Conclusions: These results support that the CIS is a valid assessment of emotional problems and deterioration of the quality of life caused by the COVID-19 pandemic. Finally, the limitations of this study and future research directions are discussed., (© 2022. The Author(s).)

Rapid outbreak of coronavirus disease 2019 (Covid-19) raised major concern regarding medical resource constraints. We constructed and validated a scoring system for early prediction of progression to severe pneumonia in patients with Covid-19. A total of 561 patients from a Covid-19 designated hospital in Daegu, South Korea were randomly divided into two cohorts: development cohort (N = 421) and validation cohort (N = 140). We used multivariate logistic regression to identify four independent risk predictors for progression to severe pneumonia and constructed a risk scoring system by giving each factor a number of scores corresponding to its regression coefficient. We calculated risk scores for each patient and defined two groups: low risk (0 to 8 points) and high risk (9 to 20 points). In the development cohort, the sensitivity and specificity were 83.8% and 78.9%. In the validation cohort, the sensitivity and specificity were 70.8% and 79.3%, respectively. The C-statistics was 0.884 (95% CI 0.833-0.934) in the development cohort and 0.828 (95% CI 0.733-0.923) in the validation cohort. This risk scoring system is useful to identify high-risk group for progression to severe pneumonia in Covid-19 patients and can prevent unnecessary overuse of medical care in limited-resource settings., (© 2022. The Author(s).)

Purpose: This retrospective study aimed to evaluate the baseline characteristics of asymptomatic patients with coronavirus disease 2019 at admission and to follow-up their clinical manifestations and radiological findings during hospitalization., Methods: Patients with coronavirus disease 2019 who were asymptomatic at admission were divided into two groups-those with no symptoms until discharge (group A) and those who developed symptoms after admission (group B). Patients who could not express their own symptoms were excluded., Results: Overall, 127 patients were enrolled in the study, of whom 19 and 108 were assigned to groups A and B, respectively. The mean age and median C-reactive protein level were higher in group B than in group A. All patients in group A and one-third of patients in group B had normal initial chest radiographs; 15.8% and 48.1% of patients in groups A and B, respectively, had pneumonia during hospitalization. One patient in group B, whose condition was not severe at the time of admission, deteriorated due to aggravated pneumonia and was transferred to a tertiary hospital., Conclusion: We summarize the clinical characteristics during hospitalization of patients with coronavirus disease 2019 who were purely asymptomatic at the time of admission. The majority of asymptomatic patients with coronavirus disease 2019 were discharged without significant events during hospitalization. However, it may be difficult to predict subsequent events from initial chest radiographs or oxygen saturation at admission., (© 2021. The Author(s).)

Objective: This study aimed to stratify the early pneumonia trajectory on chest radiographs and compare patient characteristics in dyspneic patients with coronavirus disease 2019 (COVID-19)., Materials and Methods: We retrospectively included 139 COVID-19 patients with dyspnea (87 men, 62.7±16.3 years) and serial chest radiographs from January to September 2020. Radiographic pneumonia extent was quantified as a percentage using a previously-developed deep learning algorithm. A group-based trajectory model was used to categorize the pneumonia trajectory after symptom onset during hospitalization. Clinical findings, and outcomes were compared, and Cox regression was performed for survival analysis., Results: Radiographic pneumonia trajectories were categorized into four groups. Group 1 (n = 83, 59.7%) had negligible pneumonia, and group 2 (n = 29, 20.9%) had mild pneumonia. Group 3 (n = 13, 9.4%) and group 4 (n = 14, 10.1%) showed similar considerable pneumonia extents at baseline, but group 3 had decreasing pneumonia extent at 1-2 weeks, while group 4 had increasing pneumonia extent. Intensive care unit admission and mortality were significantly more frequent in groups 3 and 4 than in groups 1 and 2 (P < .05). Groups 3 and 4 shared similar clinical and laboratory findings, but thrombocytopenia (<150×103/μL) was exclusively observed in group 4 (P = .016). When compared to groups 1 and 2, group 4 (hazard ratio, 63.3; 95% confidence interval, 7.9-504.9) had a two-fold higher risk for mortality than group 3 (hazard ratio, 31.2; 95% confidence interval, 3.5-280.2), and this elevated risk was maintained after adjusting confounders., Conclusion: Monitoring the early radiologic trajectory beyond baseline further prognosticated at-risk COVID-19 patients, who potentially had thrombo-inflammatory responses., Competing Interests: Soon Ho Yoon has read the journal’s policy and the authors of this manuscript have the following competing interests: employment as a chief medical officer in MEDICALIP. Other authors do not have any conflict of interest to declare.

Background: The prevalence of abnormal cardiovascular magnetic resonance (CMR) findings in recovered coronavirus disease 2019 (COVID-19) patients is unclear. This study aimed to investigate the prevalence of abnormal CMR findings in recovered COVID-19 patients., Methods: A systematic literature search was performed to identify studies that report the prevalence of abnormal CMR findings in recovered COVID-19 patients. The number of patients with abnormal CMR findings and diagnosis of myocarditis on CMR (based on the Lake Louise criteria) and each abnormal CMR parameter were extracted. Subgroup analyses were performed according to patient characteristics (athletes vs. non-athletes and normal vs. undetermined cardiac enzyme levels). The pooled prevalence and 95% confidence interval (CI) of each CMR finding were calculated. Study heterogeneity was assessed, and meta-regression analysis was performed to investigate factors associated with heterogeneity., Results: In total, 890 patients from 16 studies were included in the analysis. The pooled prevalence of one or more abnormal CMR findings in recovered COVID-19 patients was 46.4% (95% CI 43.2%-49.7%). The pooled prevalence of myocarditis and late gadolinium enhancement (LGE) was 14.0% (95% CI 11.6%-16.8%) and 20.5% (95% CI 17.7%-23.6%), respectively. Further, heterogeneity was observed (I 2  > 50%, p < 0.1). In the subgroup analysis, the pooled prevalence of abnormal CMR findings and myocarditis was higher in non-athletes than in athletes (62.5% vs. 17.1% and 23.9% vs. 2.5%, respectively). Similarly, the pooled prevalence of abnormal CMR findings and LGE was higher in the undetermined than in the normal cardiac enzyme level subgroup (59.4% vs. 35.9% and 45.5% vs. 8.3%, respectively). Being an athlete was a significant independent factor related to heterogeneity in multivariate meta-regression analysis (p < 0.05)., Conclusions: Nearly half of recovered COVID-19 patients exhibited one or more abnormal CMR findings. Athletes and patients with normal cardiac enzyme levels showed a lower prevalence of abnormal CMR findings than non-athletes and patients with undetermined cardiac enzyme levels. Trial registration The study protocol was registered in the PROSPERO database (registration number: CRD42020225234)., (© 2021. The Author(s).)

To curb the COVID-19 pandemic, isolation measures are required. Shared room occupancy is recommended when isolation rooms are insufficient. However, there is little evidence of the applicability of shared and single room occupancy for patients with COVID-19 to determine whether shared room occupancy is feasible. COVID-19-infected patients admitted to the Daegu Dongsan Hospital of Keimyung University from 21 February 2020 to 20 April 2020 were enrolled in the study and randomly assigned to hospital rooms. Clinical symptoms, underlying diseases and epidemiological data of patients were analysed after dividing participants into a shared room occupancy group (group A) and a single room occupancy group (group B). Outcomes analysed included microbiological cure rates, time to clinical symptom improvement, time to defervescence and negative-to-positive conversion rates of polymerase chain reaction (PCR) results during hospitalization. A total of 666 patients were included in this study, 535 and 131 patients in groups A and B, respectively. Group B included more underlying conditions, such as pregnancy and solid organ transplantation, and was more closely associated with severe pneumonia during hospitalization. Besides, no statistically significant differences between the two groups in terms of negative PCR rates at HD 7 and 14, conversion rates of PCR results from negative-to-positive, as well as time to the improvement of clinical symptoms, and time to defervescence were observed. Our results suggest that the shared room occupancy of patients with mild symptoms could be an alternative to single room occupancy during the COVID-19 pandemic., (© 2020 Wiley-VCH GmbH.)

Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss' kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Chang Min Park holds stock of Promedius and stock options of Lunit Inc. and Coreline Soft. Eui Jin Hwang, Hyungjin Kim, and Chang Min Park received research grants from Lunit Inc, outside the present paper. We checked all authors’ competing interests and confirmed that our statement does not alter the adherence to PLOS ONE policy.

Background: The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19., Methods: The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital., Results: The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11-97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs., Conclusion: The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19., Competing Interests: Soon Ho Yoon is the chief medical officer of MEDICALIP, Co. Ltd. Other authors have no potential conflicts of interest to disclose., (© 2020 The Korean Academy of Medical Sciences.)

This study proposes a model that links travel constraints with subjective norm as an additional constraint, negotiation, and learned helplessness to the desire under the pandemic and pent-up intention. Extending the constraint-negotiation model, this study analyzed 488 survey data using partial least square structural equation modeling. The results show that pent-up intention for international travel is positively affected by the desire to travel under the pandemic, which can be built up from the individuals' negotiation strategies under the constraints. These findings elucidates the underlying mechanisms contributing to the rebound effect in international travel. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is considered a risk factor for poor outcomes in patients with coronavirus disease 2019 (COVID-19). However, data on the prognostic impact of radiological emphysema extent on patients with COVID-19 are limited. Thus, this study aimed to examine whether computed tomography (CT)-quantified emphysema score is associated with a worse clinical outcome in patients with COVID-19. Methods: Volumetric quantitative analyses of CT images were performed to obtain emphysema scores in COVID-19 patients admitted to four tertiary referral hospitals in Daegu, South Korea, between February 18 and March 25, 2020. Patients were divided into three groups according to emphysema score (emphysema score ≤ 1%, 1%< emphysema score ≤ 5%, and emphysema score > 5%). Results: A total of 146 patients with confirmed SARS-CoV-2 infection were included. The median emphysema score was 1.0% (interquartile range, 0.5– 1.8%). Eight patients (6%) had a previous COPD diagnosis. Eighty (55%), 55 (38%), and 11 (8%) patients had emphysema scores ≤ 1%, between 1% and 5%, and > 5%, respectively. The number of patients who received oxygen therapy two weeks after admission was significantly higher in the group with emphysema scores > 5% than in other groups (p=0.025). The frequency of deaths was three (27%) in the group with emphysema scores > 5% and tended to be higher than that in other groups. Multivariate analysis revealed that age, COPD, and serum lactate dehydrogenase levels were associated with a greater risk of in-hospital mortality in patients with COVID-19. Conclusion: The current study demonstrated that patients with CT-quantified emphysema scores > 5% tended to progress to severe disease over time; however, they did not exhibit an increased risk of mortality in our COVID-19 cohort. Further studies with consideration of both emphysema extent and airflow limitation degree are warranted. [ABSTRACT FROM AUTHOR]

To compare online learning with traditional face-to-face and blended learning, based on randomized controlled trials, to determine the impact of online learning on nursing students' learning outcomes. A systematic review and meta-analysis. A systematic search was conducted via English (PubMed, ERIC, Embase, CENTRAL, and CINAHL) and Korean databases (RISS, DBpia, and KISS). Studies published up to the first week of April 2022 were reviewed with a focus on the participants, intervention, comparison, outcome, and study design format. Following a primary screening of titles and abstracts, and secondary screening of full texts, 10 randomized controlled trial studies were selected, of which eight were included in the meta-analysis. Two researchers independently reviewed the literature, and the final selection was made in consensus. Online learning had a statistically significant positive effect on nursing students' knowledge, compared with no educational intervention (standardized mean difference (SMD) = 1.63; 95 % confidence interval (CI): 1.31 to 1.95). However, there was no significant difference in the impact of online learning on knowledge compared with blended learning (SMD = −0.14; 95 % CI: −0.70 to 0.41) and face-to-face learning (SMD = 0.37; 95 % CI: −0.32 to 1.06). Furthermore, compared with blended learning (SMD = −0.18; 95 % CI: −0.43 to 0.06) and face-to-face learning (SMD = 0.05; 95 % CI: −0.31 to 0.41), there was no significant difference in the impact of online learning on attitudes toward learning. Online learning in nursing education is not significantly different from blended or face-to-face learning in terms of its impact on knowledge acquisition and attitudes toward learning. The results of this review and meta-analysis highlight the need for selective application of learning methods, taking into account learning environments as well as curricular subjects and topics. • Online learning had a positive effect on nursing students' knowledge and attitude toward learning. • Depending on the situation, a strategy for applying learning methods is needed. [ABSTRACT FROM AUTHOR]