ABSTRACT
INTRODUCTION AND OBJECTIVE: While subject to frequent speculation, the actual impact of the COVID-19 pandemic on urologic operative practice is unknown. Understanding the consequences of the pandemic will teach invaluable lessons for future preparedness and provide useful context for individual practices attempting to understand changes in operative volume. We analyzed populationlevel changes in operative practice since the onset of the COVID-19 pandemic to contextualize observations made by individual practices and optimize future responses. METHODS: We used Premier Perspectives Database to investigate changes in operative volume through March 2020. Baseline operative volume for urologic surgery was calculated using data from the preceding 12 months and compared on a total and by procedure basis. Multivariable linear regression was used to identify hospital-level predictors of change in response to the pandemic. Our primary outcome of interest was the change in operative volume in March 2020 relative to baseline. Total operative volume, and volume by procedure and procedure-based groupings were investigated. RESULTS: At baseline, we captured 23,788 urologic procedural encounters per month as compared with 19,071 during March 2020e a 19.9% decrease. Urologic oncology-related cases were relatively preserved as compared to others (average change in March 2020: =1.1% versus -32.2%). Northeastern (b=-5.66, 95% confidence interval [CI]: -10.2 to -1.18, p=.013) and Midwestern hospitals (b=-4.17, 95% CI: -7.89 to -0.45, p=.027;both with South as reference region), and those with an increasing percentage of patients insured by Medicaid (b= -.17 per percentage point, 95% CI: -.33 to -.01, p=.04) experienced a significantly larger decrease in volume. CONCLUSIONS: There was a 20% decline in urologic operative volume in March 2020, compared with baseline, that preferentially affected hospitals serving Medicaid patients, and those in the Northeast and Midwest. In the face of varying mandates on elective surgery, widespread declines in operative volume may also represent hesitancy on behalf of patients to interface with healthcare during the pandemic. Long-term follow-up will be necessary to determine COVID-19's final toll on urology.
ABSTRACT
Objective: To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients. Methods: Based on the patients' admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model. Results: In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94%, peripheral white blood cells count >8.0×10(9), change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7%, and the missed diagnosis rate was 38.3%. The prediction sensitivity of the model based on the test set was 75.0%, and the missed diagnosis rate was 25.0%. Conclusions: Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model's prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.