The Impact of the COVID-19 Pandemic on General Surgery Acute Admissions and Urgent Operations: A Comparative Prospective Study.

BACKGROUND: As part of the effort to control the coronavirus disease-19 (COVID-19) outbreak, strict emergency measures, including prolonged national curfews, have been imposed. Even in countries where healthcare systems still functioned, patients avoided visiting emergency departments (EDs) because of fears of exposure to COVID-19. OBJECTIVES: To describe the effects of the COVID-19 outbreak on admissions of surgical patients from the ED and characteristics of urgent operations performed. METHODS: A prospective registry study comparing all patients admitted for acute surgical and trauma care between 15 March and 14 April 2020 (COVID-19) with patients admitted in the parallel time a year previously (control) was conducted. RESULTS: The combined cohort included 606 patients. There were 25% fewer admissions during the COVID-19 period (P < 0.0001). The COVID-19 cohort had a longer time interval from onset of symptoms (P < 0.001) and presented in a worse clinical condition as expressed by accelerated heart rate (P = 0.023), leukocyte count disturbances (P = 0.005), higher creatinine, and CRP levels (P < 0.001) compared with the control cohort. More COVID-19 patients required urgent surgery (P = 0.03) and length of ED stay was longer (P = 0.003). CONCLUSIONS: During the COVID-19 epidemic, fewer patients presented to the ED requiring acute surgical care. Those who did, often did so in a delayed fashion and in worse clinical condition. More patients required urgent surgical interventions compared to the control period. Governments and healthcare systems should emphasize to the public not to delay seeking medical attention, even in times of crises.

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.