A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem.

Abstract

Coronavirus Disease 2019 (COVID-19) pandemic has implacably stricken on the wellness of many countries and their health-care systems. The aim of the present study is to analyze the clinical characteristics of the initial wave of patients with COVID-19 attended in our center, and to identify the key variables predicting the development of respiratory failure. Prospective design study with concurrent data retrieval from automated medical records of all hospitalized adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rRT-PCR assay performed on respiratory samples from March 2nd to 18th, 2020. Patients were followed up to May 1st, 2020 or death. Respiratory failure was defined as a PaO2/FiO2 ratio ≤ 200 mm Hg or the need for mechanical ventilation (either non-invasive positive pressure ventilation or invasive mechanical ventilation). We included 521 patients of whom 416 (81%) had abnormal Chest X-ray on admission. Median age was 64.6 ± 18.2 years. One hundred eighty-one (34.7%) developed respiratory failure after a median time from onset of symptoms of 9 days (IQR 6-11). In-hospital mortality was 23.8% (124/521). The modeling process concluded into a logistic regression multivariable analysis and a predictive score at admission. Age, peripheral pulse oximetry, lymphocyte count, lactate dehydrogenase and C-reactive protein were the selected variables. The model has a good discriminative capacity with an area under the ROC curve of 0.85 (0.82-0.88). The application of a simple and reliable score at admission seems to be a useful tool to predict respiratory failure in hospitalized COVID-19 patients.