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AuthorRahman, T.
AuthorKhandakar, A.
AuthorHoque, M.E.
AuthorIbtehaz, N.
AuthorKashem, S.B.
AuthorMasud, R.
AuthorShampa, L.
AuthorHasan, M.M.
AuthorIslam, M.T.
AuthorAl-Maadeed, S.
AuthorZughaier, S.M.
AuthorBadran, S.
AuthorDoi, S.A.R.
AuthorChowdhury, M.E.H.
Available date2022-04-18T08:10:54Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3105321
URIhttp://hdl.handle.net/10576/29969
AbstractThe coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.
SponsorThis work was supported by Qatar National Research Fund (QNRF) under Grant UREP28-144-3-046 and Qatar University Emergency Response Grant (QUERG-CENG-2020-1) through Qatar University. Open Access publication is funded by Qatar National Library (QNL).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBiomarkers
Cells
Decision trees
Forecasting
Hospitals
Logistic regression
Risks
Area under the curves
Clinical assessments
Complete blood counts
Hospital admissions
Multivariate logistic regressions
Prognostic model
Selection techniques
White blood cells
Blood
TitleDevelopment and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters
TypeArticle
Pagination120422-120441
Volume Number9
dc.accessType Abstract Only


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