Stop COVID Cohort: An Observational Study of 3480 Patients Admitted to the Sechenov University Hospital Network in Moscow City for Suspected Coronavirus Disease 2019 (COVID-19) Infection.

BACKGROUND: The epidemiology, clinical course, and outcomes of patients with coronavirus disease 2019 (COVID-19) in the Russian population are unknown. Information on the differences between laboratory-confirmed and clinically diagnosed COVID-19 in real-life settings is lacking. METHODS: We extracted data from the medical records of adult patients who were consecutively admitted for suspected COVID-19 infection in Moscow between 8 April and 28 May 2020. RESULTS: Of the 4261 patients hospitalized for suspected COVID-19, outcomes were available for 3480 patients (median age, 56 years; interquartile range, 45-66). The most common comorbidities were hypertension, obesity, chronic cardiovascular disease, and diabetes. Half of the patients (n = 1728) had a positive reverse transcriptase-polymerase chain reaction (RT-PCR), while 1748 had a negative RT-PCR but had clinical symptoms and characteristic computed tomography signs suggestive of COVID-19. No significant differences in frequency of symptoms, laboratory test results, and risk factors for in-hospital mortality were found between those exclusively clinically diagnosed or with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR. In a multivariable logistic regression model the following were associated with in-hospital mortality: older age (per 1-year increase; odds ratio, 1.05; 95% confidence interval, 1.03-1.06), male sex (1.71; 1.24-2.37), chronic kidney disease (2.99; 1.89-4.64), diabetes (2.1; 1.46-2.99), chronic cardiovascular disease (1.78; 1.24-2.57), and dementia (2.73; 1.34-5.47). CONCLUSIONS: Age, male sex, and chronic comorbidities were risk factors for in-hospital mortality. The combination of clinical features was sufficient to diagnose COVID-19 infection, indicating that laboratory testing is not critical in real-life clinical practice.

Three-dimensionally printed non-biological simulator for percutaneous nephrolithotomy training.

Objective: We sought to improve the educational and pre-operative training on various stages of percutaneous nephrolithotomy (PCNL) under fluoroscopic and ultrasound guidance. We developed a three-dimensional (3D) printed simulator (3D-printed PCNL model) for urological trainees.Methods: 40 s year urology residents were randomly assigned into two groups, completing PCNL surgical steps on a URO Mentor™ surgical simulator (Group A) or on our new 3D-printed PCNL model (Group B). Following the training, both groups completed a standardized questionnaire (Likert scale from 0 to 10) which we used to asses the learning curve associated with PCNL training.Results: The mean score of Group A was 65.2/80 while Group B was 76.1/80. Mann-Whitney U-test showed no significant difference between the groups (U = 16, p < 0.05).Conclusion: The 3D-printed PCNL model developed is a novel and highly effective tool that can facilitate enhanced endourological education and personalized pre-operative planning for urolithiasis cases. According to the criteria tested, residents who used our 3D-printed PCNL models performed better under all metrics.