Internal and emergency medicine
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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. ⋯ 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.
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The correlation between myocardial injury and clinical outcome in COVID-19 patients is gaining attention in the literature. The aim of the present study was to evaluate the role of cardiac involvement and of respiratory failure in a cohort of COVID-19 patients hospitalized in an academic hospital in Lombardy, one of the most affected Italian (and worldwide) regions by the epidemic. The study included 405 consecutive patients with confirmed COVID-19 admitted to a medical ward from February 25th to March 31st, 2020. ⋯ At multivariable analysis, older age, P/F ratio < 200 (both p < 0.001) and hs-TnI plasma levels were independent predictors of death. However, it must be emphasized that the median values of hs-TnI were within normal range in non-survivors. Cardiac involvement at presentation was associated with poor prognosis in COVID-19 patients, but, even in a population of COVID-19 patients who did not require invasive ventilation at hospital admission, mortality was mainly driven by older age and respiratory failure.