Routine Hematological Parameters May Be Predictors of COVID-19 Severity - 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; et al. (Frontiers in Medicine, 2021)
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 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 routine clinical haematology workup, at the time of hospital admission, can be valuable predictors of severity of COVID-19 and the requirement for critical care. Haematological 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) was acquired. The data was amalgamated, cleaned and modelling performed. Using a decision tree model, we demonstrated that routine clinical haematology 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 patient’s hospital admission. These data, pending further validation, indicates that a decision tree model with haematological parameters could potentially form a basis for a rapid risk stratification tool that predicts COVID-19 severity in hospitalised patients.