Prediction of fatal outcome in patients with confirm COVID-19

Abstract

SARS-CoV-2, the new coronavirus underlying the development of the COVID-19 pandemic, has led to a sharp increase in the burden on healthcare systems, high mortality and significant difficulties in organizing medical care. The aim of the study was to conduct a systematic analysis of factors affecting the course of infectious disease in patients with diagnosed COVID-19 hospitalized. In order to predict the course of the disease and determine the indications for more aggressive treatment, many different clinical and biological markers have been proposed, however, clinical and laboratory assessment of the condition is not always simple and can clearly predict the development of a severe course. Technologies based on artificial intelligence (AI) have played a significant role in predicting the development of the disease. One of the main requirements during a pandemic is an accurate prediction of the required resources and likely outcomes. In the present study, a machine learning (ML) approach is proposed to predict the fatal outcome in patients with an established diagnosis of COVID-19 based on the patient’s medical history and clinical, laboratory and instrumental data obtained in the first 72 hours of the patient’s stay in the hospital. A machine learning algorithm for predicting the lethal outcome in patients with COVID-19 during 72 hours of hospitalization demonstrated high sensitivity (0.816) and specificity (0.865). Given the serious concerns about limited resources, including ventilators, during the COVID-19 pandemic, accurately predicting patients who are likely to require artificial ventilation can help provide important recommendations regarding patient triage and resource allocation among hospitalized patients. In addition, early detection of such persons may allow for routine ventilation procedures, reducing some of the known risks associated with emergency intubation. Thus, this algorithm can help improve patient care, reduce patient mortality and minimize the burden on doctors during the COVID-19 pandemic.