The Evaluation of Web-Based Communication Interventions to Support Decisions About COVID-19 Vaccination Among Patients With Underlying Medical Conditions: Protocol for a Randomized Controlled Trial.

BACKGROUND: The timeliness of raising vaccine acceptance and uptake among the public is essential to overcoming COVID-19; however, the decision-making process among patients with underlying medical conditions is complex, leading individuals to vaccine hesitancy because of their health status. Although vaccine implementation is more effective when deployed as soon as possible, vaccine hesitancy is a significant threat to the success of vaccination programs. OBJECTIVE: This study aims to evaluate the effectiveness of a communication tool for patients with underlying medical conditions who should decide whether to receive a COVID-19 vaccine. METHODS: This 3-arm prospective randomized controlled trial will test the effect of the developed communication intervention, which is fully automated, patient decision aid (SMART-DA), and user-centered information (SMART-DA-α). The web-based intervention was developed to help decision-making regarding COVID-19 vaccination among patients with underlying medical conditions. Over 450 patients will be enrolled on the web from a closed panel access website and randomly assigned to 1 of 3 equal groups stratified by their underlying disease, sex, age, and willingness to receive a COVID-19 vaccine. SMART-DA-α provides additional information targeted at helping patients' decision-making regarding COVID-19 vaccination. Implementation outcomes are COVID-19 vaccination intention, vaccine knowledge, decisional conflict, stress related to decision-making, and attitudes toward vaccination, and was self-assessed through questionnaires. RESULTS: This study was funded in 2020 and approved by the Clinical Research Information Service, Republic of Korea. Data were collected from December 2021 to January 2022. This paper was initially submitted before data analysis. The results are expected to be published in the winter of 2023. CONCLUSIONS: We believe that the outcomes of this study will provide valuable new insights into the potential of decision aids for supporting informed decision-making regarding COVID-19 vaccination and discovering the barriers to making informed decisions regarding COVID-19 vaccination, especially among patients with underlying medical conditions. This study will provide knowledge about the common needs, fears, and perceptions concerning vaccines among patients, which can help tailor information for individuals and develop policies to support them. TRIAL REGISTRATION: Korea Clinical Information Service KCT0006945; https://cris.nih.go.kr/cris/search/detailSearch.do/20965. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42837.

Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record.

Background: As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient. Objective: This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms. Methods: The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups (i.e., 4-group classification) and using two groups after regrouping the four severity level into two (i.e., binary classification). Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE. Results: Of the four maximum severity groups, the moderate group had the highest percentage (1,115 patients; 49.5%). As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (http://statgen.snu.ac.kr/software/nomogramDaeguCovid/). Conclusions: We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field.

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study.

BACKGROUND: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. OBJECTIVE: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. METHODS: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. RESULTS: Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. CONCLUSIONS: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Possible aerosol transmission of COVID-19 associated with an outbreak in an apartment in Seoul, South Korea, 2020.

BACKGROUND: Scientists have strongly implied that aerosols could be the plausible cause of coronavirus disease-2019 (COVID-19) transmission; however, aerosol transmission remains controversial. THE STUDY: We investigated the epidemiological relationship among infected cases on a recent cluster infection of COVID-19 in an apartment building in Seoul, South Korea. All infected cases were found along two vertical lines of the building, and each line was connected through a single air duct in the bathroom for natural ventilation. Our investigation found no other possible contact between the cases than the airborne infection through a single air duct in the bathroom. The virus from the first infected case can be spread to upstairs and downstairs through the air duct by the (reverse) stack effect, which explains the air movement in a vertical shaft. CONCLUSIONS: This study suggests aerosol transmission, particularly indoors with insufficient ventilation, which is underappreciated.