The Model for Early COvid-19 Recognition (MECOR) Score: A Proof-of-Concept for a Simple and Low-Cost Tool to Recognize a Possible Viral Etiology in Community-Acquired Pneumonia Patients during COVID-19 Outbreak

Abstract

This study aims to assess the peripheral blood cell count “signature” of Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) to discriminate promptly between COronaVIrus Disease 19 (COVID-19) and community-acquired pneumonia (CAP). We designed a retrospective case-control study, enrolling 525 patients (283 COVID-19 and 242 with CAP). All patients had a fever and at least one of the following signs: cough, chest pain, or dyspnea. We excluded patients treated with immunosuppressants, steroids, or affected by diseases known to modify blood cell count. COVID-19 patients showed a significant reduction in white blood cells (neutrophils, lymphocytes, monocytes, eosinophils) and platelets. We studied these parameters univariately, combined the significant ones in a multivariate model (AUROC 0.86, Nagelkerke PSEUDO-R2 0.5, Hosmer–Lemeshow p-value 0.9) and examined its discriminative performance in an internally-randomized validation cohort (AUROC 0.84). The cut-off selected according to Youden’s Index (−0.13) showed a sensitivity of 84% and a specificity of 72% in the training cohort, and a sensitivity of 88% and a specificity of 73% in the validation cohort. In addition, we determined the probability of having COVID-19 pneumonia for each Model for possible Early COvid-19 Recognition (MECOR) Score value. In conclusion, our model could provide a simple, rapid, and cheap tool for prompt COVID-19 diagnostic triage in patients with CAP. The actual effectiveness should be evaluated in further, prospective studies also involving COVID-19 patients with negative nasopharyngeal swabs.