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Routine Hematological Parameters May Be Predictors of COVID-19 Severity
Paulina B Szklanna1,2, Haidar Altaie3, Shane P Comer1,2
1Conway SPHERE Research Group, Conway Institute, University College Dublin, Dublin, Ireland.
Insights
Routine hematology parameters can predict COVID-19 severity. A decision tree model using activated partial thromboplastin time, white cell count-to-neutrophil ratio, and platelet count accurately identifies patients needing critical care upon hospital admission.
Area of Science:
- Hematology
- Infectious Diseases
- Critical Care Medicine
Background:
- Coronavirus disease 2019 (COVID-19) has caused a global pandemic, with disease severity varying widely.
- Predicting COVID-19 severity and critical care needs early in hospitalization is a significant clinical challenge.
- Routine hematological parameters are readily available at hospital admission and may offer predictive value.
Purpose of the Study:
- To evaluate the utility of routine clinical hematology parameters in predicting COVID-19 severity.
- To determine if specific hematological markers can identify patients at risk for critical care.
Main Methods:
- Hematological data from hospitalized patients with severe (requiring critical care) and non-severe COVID-19 were collected on admission.
- A decision tree model was developed using parameters such as activated partial thromboplastin time, white cell count-to-neutrophil ratio, and platelet count.
- Model performance was assessed using receiver operating characteristic (ROC) curve analysis.
Main Results:
- Routine clinical hematology parameters were identified as significant predictors of COVID-19 severity.
- A combination of activated partial thromboplastin time, white cell count-to-neutrophil ratio, and platelet count accurately predicted disease severity.
- The decision tree model achieved high sensitivity and specificity (Area Under ROC: 0.9956) in predicting critical care requirement.
Conclusions:
- Hematological parameters obtained during routine workup can serve as valuable early predictors of COVID-19 severity.
- A decision tree model incorporating these parameters offers a potential tool for rapid risk stratification of hospitalized COVID-19 patients.
- Further validation is warranted to implement this model for clinical decision-making.
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.
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