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.
Highlights
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the Coronavirus disease 2019 (COVID19), has affected over 100 million globally with over 2.5 million fatalities to date leading to a global pandemic of unprecedented size [1]
Anonymized data pertaining to 36 patients with COVID-19 requiring critical care level support from March to May 2020 at Mater Misericordiae University Hospital (MMUH), Dublin, Ireland, were collected
We have observed that the activated partial thromboplastin time (29.22 ± 1.99 s in the non-severe group; 33.08 ± 8.35 s in the severe group; p = 0.047) and D-dimer levels (1.01 ± 0.75 mg/L in the non-severe group; 4.95 ± 6.36 mg/L in the severe group; p = 0.0043) were significantly increased in our severe COVID19 cohort (Table 1)
Summary
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the Coronavirus disease 2019 (COVID19), has affected over 100 million globally with over 2.5 million fatalities to date leading to a global pandemic of unprecedented size [1]. COVID-19 symptoms can vary from person to person, leading to a clinical manifestation of disease ranging from asymptomatic to mild infections, through to serious, lifethreatening cases requiring admission to the intensive care unit (ICU) [2, 3]. Hospitalized COVID-19 patients receive various care regimens These regimens depend on the severity of COVID-19 and result in a varying rate of ICU admissions [7,8,9]. Several COVID-19 specific risk scores have been designed to support clinical decision making and facilitation of appropriate care. These utilize various combinations of patient characteristics, physiological parameters [14], biochemical parameters [15], and radiological features [16, 17]. The ability to quickly diagnose and identify patients who would benefit from early, invasive treatment measures at the time of hospital admission, for example, is of critical importance and would be of substantial value [18]
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