AUTHOR=Szklanna Paulina B. , Altaie Haidar , Comer Shane P. , Cullivan Sarah , Kelliher Sarah , Weiss Luisa , Curran John , Dowling Emmet , O'Reilly Katherine M. A. , Cotter Aoife G. , Marsh Brian , Gaine Sean , Power Nick , Lennon Áine , McCullagh Brian , Ní Áinle Fionnuala , Kevane Barry , Maguire Patricia B. TITLE=Routine Hematological Parameters May Be Predictors of COVID-19 Severity JOURNAL=Frontiers in Medicine VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.682843 DOI=10.3389/fmed.2021.682843 ISSN=2296-858X ABSTRACT=

To date, coronavirus disease 2019 (COVID-19) has affected over 100 million people globally. COVID-19 can present with a variety of different symptoms leading to manifestation of disease ranging from mild cases to a life-threatening condition requiring critical care-level support. At present, a rapid prediction of disease severity and critical care requirement in COVID-19 patients, in early stages of disease, remains an unmet challenge. Therefore, we assessed whether parameters from a routine clinical hematology workup, at the time of hospital admission, can be valuable predictors of COVID-19 severity and the requirement for critical care. Hematological data from the day of hospital admission (day of positive COVID-19 test) for patients with severe COVID-19 disease (requiring critical care during illness) and patients with non-severe disease (not requiring critical care) were acquired. The data were amalgamated and cleaned and modeling was performed. Using a decision tree model, we demonstrated that routine clinical hematology parameters are important predictors of COVID-19 severity. This proof-of-concept study shows that a combination of activated partial thromboplastin time, white cell count-to-neutrophil ratio, and platelet count can predict subsequent severity of COVID-19 with high sensitivity and specificity (area under ROC 0.9956) at the time of the patient's hospital admission. These data, pending further validation, indicate that a decision tree model with hematological parameters could potentially form the basis for a rapid risk stratification tool that predicts COVID-19 severity in hospitalized patients.