Combinations of nerve blocks in surgery for post COVID-19 pulmonary sequelae patient: A case report and review of literature.

BACKGROUND: Regional anesthesia is a promising method in patients with post coronavirus disease 2019 (COVID-19) pulmonary sequelae for preserving pulmonary function and preventing postoperative pulmonary complications, compared with general anesthesia. CASE SUMMARY: We provided surgical anesthesia and analgesia suitable for breast surgery by performing pectoral nerve block type II (PECS-II), parasternal, and intercostobrachial nerve blocks with intravenous dexmedetomidine administration in a 61-year-old female patient with severe pulmonary sequelae after COVID-19 infection. CONCLUSION: Sufficient analgesia for 7 h was provided via PECS-II, parasternal, and intercostobrachial blocks perioperatively.

Role of Daegu Medical Association in the infection control of the COVID-19 outbreak

In the mass outbreak of the 2019 novel coronavirus disease pandemic, the Daegu Medical Association managed to control and reduce the number of victims in Daegu successfully. More than 6,000 people were diagnosed in this city within a four-week period, and both medical system breakdown and increasing mortality were imminent. However, we minimized fatalities despite this explosive outbreak in a short time. The collaboration between the Daegu Medical Association and the local government may provide a reference model for overcoming regional outbreaks in the global pandemic era. We can prevent the shortage of medical resources by recruiting volunteer doctors and nurses early through public awareness campaigns. We can overcome the first massive outbreak using several new diagnostic and therapeutic systems such as the drive-through diagnosis system, telephonic counseling for self-quarantine patients by volunteer doctors, and therapeutic living centers. Both sharing the process of collaboration with the public system and summarizing the factors can provide useful information for building effective response systems to cope with the ongoing local outbreak of COVID-19 and any future epidemics of infectious diseases.

Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea.

BACKGROUND: Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage. METHODS: This is a retrospective study using cohort data from the Korean Centers for Disease Control and Prevention that included all admitted patients with COVID-19 between January 19 and June 3, 2020, in South Korea. The primary outcome was patients requiring intensive care defined as actual admission to the intensive care unit; at any time use of an extracorporeal life support device, mechanical ventilation, or vasopressors; and death. Patients admitted until March 20 were included for the training dataset to develop the prediction models and externally validated for the patients admitted afterward. Two logistic regression models were developed with different predictors and the predictive performance was compared: one with patient-provided variables and the other with added radiologic and laboratory variables. An integer-based scoring system was developed based on the developed logistic regression model. RESULTS: A total of 5193 patients were considered, with 4663 patients included after excluding patients with age under 18 or insufficient data. For the training dataset, 3238 patients were included. Of the included patients, 444 (9.5%) patients required intensive care. The model developed with only the clinical variables showed an area under the curve of 0.884 for the validation set. The performance did not differ when radiologic and laboratory variables were added. Seven variables were selected for developing an integer-based scoring system: age, sex, initial body temperature, dyspnea, hemoptysis, history of chronic kidney disease, and activities of daily living. The area under the curve of the scoring system was 0.880. CONCLUSIONS: An integer-based scoring system was developed for predicting patients with COVID-19 requiring intensive care, with high performance. This system may aid decision support for prioritizing the patient for hospitalization and intensive care, particularly in a situation with limited medical resources.

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. OBJECTIVE: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics-baseline demographics, comorbidities, and symptoms. METHODS: A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. CONCLUSIONS: We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

Diagnosis of Coronavirus Disease 2019

The first massive outbreak of coronavirus disease 2019 (COVID-19) in Korea was controlled by fast diagnosis, isolation and triage systems. The development of therapeutic agents and vaccinations are going on, but many studies clarified the nature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we will discuss the characteristics and spreading of SARS-CoV-2, and prognostic factors and diagnosis of COVID-19.