Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

• Published in: Disease markers Vol. 2021; pp. 8863053 - 7
• Main Authors: Azzolina, Danila, Hayden, Eyal, Gaidano, Gianluca, Pirisi, Mario, Angilletta, Roberto, Arioli, Roberto, Avino, Gianluca, Balbo, Piero Emilio, Baldon, Giulia, Baorda, Francesca, Barbero, Emanuela, Baricich, Alessio, Barone-Adesi, Francesco, Battistini, Sofia, Beltrame, Michela, Bertolin, Stephanie, Bertolotti, Marinella, Betti, Marta, Bobbio, Flavio, Boffano, Paolo, Boglione, Lucio, Borrè, Silvio, Cammarata, Edoardo, Cantaluppi, Vincenzo, Capponi, Andrea, Carriero, Alessandro, Casciaro, Giuseppe Francesco, Castello, Luigi Mario, Ceruti, Federico, Chichino, Guido, Cittone, Micol Giulia, Colombo, Crizia, Comi, Cristoforo, Croce, Eleonora, Daffara, Tommaso, Della Corte, Francesco, De Vecchi, Simona, Dianzani, Umberto, Esposto, Elia, Faggiano, Fabrizio, Falaschi, Zeno, Ferrante, Daniela, Ferrero, Alice, Gagliardi, Ileana, Galbiati, Alessandra, Gallo, Silvia, Garavelli, Pietro Luigi, Gardino, Clara Ada, Garzaro, Massimiliano, Gavelli, Francesco, Giacomini, Greta Maria, Giacone, Irene, Giai Via, Valentina, Gironi, Laura Cristina, Gramaglia, Carla, Grisafi, Leonardo, Inserra, Ilaria, Invernizzi, Marco, Krengli, Marco, Labella, Emanuela, Landi, Irene Cecilia, Landi, Raffaella, Leone, Ilaria, Lorenzini, Luca, Maconi, Antonio, Manfredi, Giulia Francesca, Martelli, Maria, Marzari, Letizia, Marzullo, Paolo, Mennuni, Marco, Mussa, Marco, Nerici, Ilaria, Nuzzo, Alessandro, Padelli, Samuel Alberto, Panella, Massimiliano, Parisini, Andrea, Paschè, Alessio, Patrucco, Filippo, Patti, Giuseppe, Percivale, Ilaria, Ragazzoni, Luca, Re, Roberta, Rigamonti, Cristina, Rognoni, Andrea, Roveta, Annalisa, Santagostino, Matteo, Saraceno, Massimo, Savoia, Paola, Sciarra, Marco, Scotti, Lorenza, Spinoni, Enrico, Tarantino, Vanessa, Tillio, Paolo Amedeo, Tonello, Stelvio, Vaschetto, Rosanna, Vassia, Veronica, Zagaria, Domenico, Zavattaro, Elisa, Zeppegno, Patrizia, Sainaghi, Pier Paolo
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; Hindawi Limited
• Subjects: Adult; Aged; Aged, 80 and over; Blood Cell Count; Blood cells; Chi-square test; Clinical Decision Rules; Coronaviruses; COVID-19; COVID-19 - blood; COVID-19 - diagnosis; COVID-19 - mortality; Decisions; Fatalities; Female; Hemoglobin; Hospital Mortality; Humans; Italy - epidemiology; Leukocytes (neutrophilic); Lymphocytes; Male; Materials selection; Middle Aged; Monitoring; Mortality; Multivariate Analysis; Parameters; Patients; Platelets; Prognosis; Retrospective Studies; Severity of Illness Index; Telemedicine; Viral diseases; Italy
• ISSN: 0278-0240; 1875-8630
• DOI: 10.1155/2021/8863053

Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ2 10.4; p<0.001), neutrophil-to-lymphocyte (NL) ratio (χ2 7.6; p=0.006), and platelet count (χ2 5.39; p=0.02), along with age (χ2 87.6; p<0.001) and gender (χ2 17.3; p<0.001), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL ratio>4.68 was characterized by an odds ratio for in-hospital mortality OR=3.40 (2.40-4.82), while the OR for a RDW>13.7% was 4.09 (2.87-5.83); a platelet count>166,000/μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.