Routine Hematological Parameters May Be Predictors of COVID-19 Severity

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.

Two Different Clinical Presentations of Acute Limb Ischemia Caused by Acute Thrombotic Events in COVID-19

“…Some studies have shown the presence of antiphospholipid antibodies, which may cause endothelial dysfunction in COVID-19 patients, who manifested cerebral artery obstruction and limb artery obstruction [9,13]. Hypercoagulable state due to inflammation, endothelial cell dysfunction due to endotheliitis, and prolonged immobilization during the illness are consistent with Virchow's triad [11,12]. Abnormal coagulation may be a sign of clinical decline and has been linked to poor prognosis in previous studies [2,9].…”

“…CAC has been recently described in COVID-19 patients and is found in 25-55% of severe cases [2,7]. CAC is related to the immunothrombosis process where acute inflammation strongly correlates with a hypercoagulable state [2,10,11]. Interleukin (IL)-6, IL-2, interferon (IFN)-g, tumor necrosis factor (TNF)-a, and neutrophil extracellular traps (NET) play a role in CAC.…”

Alteraciones hematológicas como consecuencia de COVID-19 y sus vacunas

“…Otra de las alternativas que diferentes investigadores han buscado en su afán de clasificar los pacientes durante el triaje, es la combinación de varios parámetros. Como ejemplo está el estudio de Szklanna y colaboradores [14], quienes lograron un AUROC de 0,9956 en la capacidad para predecir los pacientes que serían ingresados a UCI, al combinar tres parámetros: 1) tiempo de tromboplastina parcial activado >31,58 segundos (valor de referencia de 25 a 36,5 segundos); 2) relación leucocitos/ neutrófilos <1,09; y, 3) recuento de plaquetas <108.830 células/μL (valor de referencia de 150.000 a 400.000 células/μL). Desafortunadamente, el estudio solo contó con 54 pacientes, por lo tanto, se requieren más estudios que validen estos resultados.…”

An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms

“…As a result, methods based on this imaging technology are inappropriate for screening. Although various clinical studies (11)(12)(13)(14)(15) have emphasized the usefulness of blood test-based diagnoses in providing an effective and lowcost alternative for the early detection of COVID-19, relatively few ML models are based on hematological parameters.…”