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.

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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.

Anthocyanin is a natural plant pigment and potent antioxidant. This study was designed to investigate the effects of anthocyanin extracted from black soybeans on a rat model of benign prostatic hyperplasia (BPH), a disease associated with the geriatric population. Thirty male rats were divided into five experimental groups: a control group, a BPH-induced group, and three BPH-induced groups that received oral doses of anthocyanin (40, 80, and 160 mg/kg). Prostate hyperplasia was induced by the administration of testosterone propionate for 4 weeks. Following BPH induction, the anthocyanin-treated groups received the compound for 4 weeks. After anthocyanin treatment, the prostates from the rats in all groups were removed, weighed, and subjected to histological examination. Apoptosis in the prostates was measured by the TUNEL assay. The mean prostate weight for the control animals was 674.17 ± 28.24 mg, whereas the BPH-induced rats had a mean prostate weight of 1098.33 ± 131.31 mg. The mean prostate weights for the rats receiving 40, 80, and 160 mg/kg anthocyanin were 323.00 ± 22.41, 324.00 ± 26.80, and 617.50 ± 31.08 mg, respectively. The average prostate weight in the BPH-induced group was significantly higher than in the control group (p < 0.05), whereas the prostate weights in the anthocyanin-administered groups were significantly lower than in the BPH-induced group (p < 0.05). Injected testosterone led to prostatic hyperplasia as observed histologically, but anthocyanin administration helped to prevent this change. Apoptotic body counts were significantly higher in groups receiving anthocyanin than in the BPH-induced group (p < 0.05). These results suggest that anthocyanin may be effective in decreasing the volume and suppressing the proliferation of the prostate. Further studies are needed to better understand the mechanisms and actions of anthocyanin, and these studies may lead to the clinical application of anthocyanin in treating BPH.

Purpose:There are few studies about clinical courses following acute bacterial prostatitis (ABP). We evaluated the progression rates of chronic bacterial prostatitis (CBP) and inflammatory chronic pelvic pain syndrome (CPPS) after ABP treatment. Also evaluated the characteristics of the patients who developed CBP and inflammatory CPPS after ABP treatment.Methods:Total 437 patients compatible with a confirmed diagnosis of ABP from 5 urological centers between 2001 and 2010 were enrolled to study. We defined chronic infection (CI) as a progression to CBP and inflammatory CPPS after treatment of ABP in admission periods when followed up at 3 months or more. Results were analyzed between two groups: recovered without CI (group A, n=385) and developed to CI (group B, n=52).Results:Of the 437 ABP patients, 1.3% (6/437) progressed to CBP and 10.5% (46/437) progressed to inflammatory CPPS. The progression rate of CI was 11.8% (52/437). The patients who developed to CI were higher in alcohol consumption rate, diabetes, voiding symptoms, prior manipulation rate, enlarged prostate volume, catheterization history rate and short duration of antibiotic treatment (P<0.05).Conclusions:The identification and characterization of these factors may accelerate the development of preventive, diagnostic and therapeutic strategies for the treatment of CI from ABP.

Seoritae is a type of black soybean that is known to have health-promoting effects due to its high isoflavone and anthocyanin contents. We evaluated whether Seoritae extract (SE) had beneficial effects on the reduction of prostate weight in a rat model of benign prostatic hyperplasia (BPH). BPH was induced by intramuscular injections of testosterone enanthate once a week for 5 weeks in Sprague-Dawley rats, and rats were treated with or without daily oral doses of SE during BPH induction. After 5 weeks, the oxidative stress (superoxide dismutase and 8-hydroxy-2-deoxyguanosine), apoptosis (caspase-3), and activity of 5-alpha reductase were evaluated in the serum and prostate. The SE treatment group showed a significant decrease in prostate weight, oxidative stress, apoptosis, and 5-alpha reductase activity compared to the nontreated BPH group. These results show that SE is effective in decreasing the weight and proliferation of the prostate, and suggest that SE may be an effective treatment for BPH.

Background Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. Objective This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak. Methods The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group. Results The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application. Conclusions We developed an expert-opinion–based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.

Many patients suffering from asthma or COPD have overlapping features of both diseases. However, a phenotypical approach for evaluating asthma–COPD overlap syndrome (ACOS) has not been established. In this report, we examined the phenotypes in patients with ACOS. Patients diagnosed with ACOS between 2011 and 2015 were identified and classified into four phenotype groups. Group A was composed of patients who smoked <10 pack years and had blood eosinophil counts ≥300. Group B was composed of patients who smoked <10 pack years and had blood eosinophil counts <300. Group C was composed of patients who smoked ≥10 pack years and had blood eosinophil counts ≥300. Group D was composed of patients who smoked <10 pack years and had blood eosinophil counts <300. Clinical characteristics were analyzed and compared among groups. Comparisons were made among 103 ACOS patients. Patients in group D were oldest, while patients in group A were youngest. There were relatively more female patients in groups A and B; the majority of patients in groups C and D were male. The degree of airflow obstruction was most severe in group C. The rate of being free of severe exacerbation was significantly lower in group C than in the other groups. In this study, each ACOS phenotype showed different characteristics. The proportion of patients free of severe exacerbation differed significantly among groups. At this time, further studies on the phenotypes of ACOS are required.