Predicting factors associated with prolonged intensive care unit stay of patients with COVID-19.

BACKGROUND: Predicting the length of stay (LOS) for coronavirus disease 2019 (COVID-19) patients in the intensive care unit (ICU) is essential for efficient use of ICU resources. We analyzed the clinical characteristics of patients with severe COVID-19 based on their clinical care and determined the predictive factors associated with prolonged LOS. METHODS: We included 96 COVID-19 patients who received oxygen therapy at a high-flow nasal cannula level or above after ICU admission during March 2021 to February 2022. The demographic characteristics at the time of ICU admission and results of severity analysis (Sequential Organ Failure Assessment [SOFA], Acute Physiology and Chronic Health Evaluation [APACHE] II), blood tests, and ICU treatments were analyzed using a logistic regression model. Additionally, blood tests (C-reactive protein, D-dimer, and the PaO2 to FiO2 ratio [P/F ratio]) were performed on days 3 and 5 of ICU admission to identify factors associated with prolonged LOS. RESULTS: Univariable analyses showed statistically significant results for SOFA score at the time of ICU admission, C-reactive protein level, high-dose steroids, mechanical ventilation (MV) care, continuous renal replacement therapy, extracorporeal membrane oxygenation, and prone position. Multivariable analysis showed that MV care and P/F ratio on hospital day 5 were independent factors for prolonged ICU LOS. For D-dimer, no significant variation was observed at admission; however, after days 3 and 5 days of admission, significant between-group variation was detected. CONCLUSIONS: MV care and P/F ratio on hospital day 5 are independent factors that can predict prolonged LOS for COVID-19 patients.

Hormone Therapy in the Era of the COVID-19 Pandemic: A Review.

The coronavirus disease 2019 (COVID-19) pandemic has impacted the medical, social, and reproductive health of millions of people since its outbreak. The causative virus transmits, reproduces, and manifests through the respiratory tract. COVID-19 can invade any system of the body, including the cardiovascular and endocrine systems, through a secondary immune response. In particular, because the fatality rate is high in those over the age of 50 years, special attention is required during the medical care of this population. However, considering the benefit of therapy and the risk of COVID-19, high-quality evidence regarding individualized management in relation to hormone therapy is still insufficient in the field of gynecology. Furthermore, this review aims to serve as a reference for clinical application by analyzing and summarizing the results of studies reported to date regarding female hormone therapy in the context of the COVID-19 pandemic.

Simultaneous Nosocomial SARS-CoV-2 Outbreaks with Different Patterns of Occurrence in Two General Wards; Experience in a Tertiary Care Hospital in Korea.

A nosocomial outbreak of severe acute respiratory syndrome-coronavirus-2 occurred in two general wards. One outbreak had pre-symptomatic transmission and was linked epidemiologically with an index case. The other outbreak was associated with early detection failure in a crowded room. Notably, adherence to appropriate mask wearing was not confirmed in the hospital outbreak. Confirmed cases were moved into isolation rooms, and contacts were quarantined. Quarantined cases were tested regularly and facilitated early termination of the hospital outbreak.

Importance of wearing a mask continuously and appropriately regardless of the COVID 19 symptoms. Lessons from different mask-wearing styles among two healthcare personnel.

The anecdotal report describes two examples of COVID-19-positive healthcare professionals (HCPs) who had worked as nurses during pre-symptomatic period and subsequently presented a mild clinical course of COVID-19. The nurses' responsibilities were almost the same and worked in the general ward with no aerosol generating medical procedures. Two HCPs were expected to have similar infectiousness, but the number of secondary transmission by each HCP were different. Eleven close contacts from HCP1 were notified, and all of them tested negative for SARS-CoV-2. However, 13 of 35 close contacts of HCP2 tested positive for SARS-CoV-2. While working, mask-wearing style differed between the two HCPs. HCP1 wore a KF94 mask appropriately and kept wearing it while working. HCP2 wore a surgical mask while working, but often pulled it down to her chin or removed it. It was strongly suspected that the difference of mask wearing contributed to the SARS-CoV-2 transmission. However, other factors such as talkative behavior, exposure time, ventilation in rooms, and hand hygiene performance rates, could also have affected the transmission of COVID-19. It is crucial that healthcare workers wear a mask adequately and continuously, and they maintain proper hand hygiene while working during the COVID-19 pandemic.

Usefulness of Mobile Computed Tomography in Patients with Coronavirus Disease 2019 Pneumonia: A Case Series.

The coronavirus disease (COVID-19) outbreak has reached global pandemic status as announced by the World Health Organization, which currently recommends reverse transcription polymerase chain reaction (RT-PCR) as the standard diagnostic tool. However, although the RT-PCR test results may be found negative, there are cases that are found positive for COVID-19 pneumonia on computed tomography (CT) scan. CT is also useful in assessing the severity of COVID-19 pneumonia. When clinicians desire a CT scan of a patient with COVID-19 to monitor treatment response, a safe method for patient transport is necessary. To address the engagement of medical resources necessary to transport a patient with COVID-19, our institution has implemented the use of mobile CT. Therefore, we report two cases of COVID-19 pneumonia evaluated by using mobile cone-beam CT. Although mobile cone-beam CT had some limitations regarding its image quality such as scatter noise, motion and streak artifacts, and limited field of view compared with conventional multi-detector CT, both cases had acceptable image quality to establish the diagnosis of COVID-19 pneumonia. We report the usefulness of mobile cone-beam CT in patients with COVID-19 pneumonia.

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non-COVID-19 pneumonia and nonpneumonia diseases. METHODS: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Clinical Course and Outcomes of Patients with Severe Acute Respiratory Syndrome Coronavirus 2 Infection: a Preliminary Report of the First 28 Patients from the Korean Cohort Study on COVID-19.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected pneumonia emerged in Wuhan, China in December 2019. In this retrospective multicenter study, we investigated the clinical course and outcomes of novel coronavirus disease 2019 (COVID-19) from early cases in Republic of Korea. METHODS: All of the cases confirmed by real time polymerase chain reaction were enrolled from the 1st to the 28th patient nationwide. Clinical data were collected and analyzed for changes in clinical severity including laboratory, radiological, and virologic dynamics during the progression of illness. RESULTS: The median age was 40 years (range, 20-73 years) and 15 (53.6%) patients were male. The most common symptoms were cough (28.6%) and sore throat (28.6%), followed by fever (25.0%). Diarrhea was not common (10.7%). Two patients had no symptoms. Initial chest X-ray (CXR) showed infiltration in 46.4% of the patients, but computed tomography scan confirmed pneumonia in 88.9% (16/18) of the patients. Six patients (21.4%) required supplemental oxygen therapy, but no one needed mechanical ventilation. Lymphopenia was more common in severe cases. Higher level of C-reactive protein and worsening of chest radiographic score was observed during the 5-7 day period after symptom onset. Viral shedding was high from day 1 of illness, especially from the upper respiratory tract (URT). CONCLUSION: The prodromal symptoms of COVID-19 were mild and most patients did not have limitations of daily activity. Viral shedding from URT was high from the prodromal phase. Radiological pneumonia was common from the early days of illness, but it was frequently not evident in simple CXR. These findings could be plausible explanations for the easy and rapid spread of SARS-CoV-2 in the community.