Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters
<p>The 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 s...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , , , , , , , |
| منشور في: |
2021
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إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
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| _version_ | 1864513560207425536 |
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| author | Tawsifur Rahman (14150523) |
| author2 | Amith Khandakar (14151981) Md Enamul Hoque (5807120) Nabil Ibtehaz (16888773) Saad Bin Kashem (16888776) Reehum Masud (16888779) Lutfunnahar Shampa (16888782) Mohammad Mehedi Hasan (7326065) Mohammad Tariqul Islam (7854059) Somaya Al-Maadeed (5178131) Susu M. Zughaier (14151987) Saif Badran (16888785) Suhail A. R. Doi (7490777) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author author author author author author author |
| author_facet | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Md Enamul Hoque (5807120) Nabil Ibtehaz (16888773) Saad Bin Kashem (16888776) Reehum Masud (16888779) Lutfunnahar Shampa (16888782) Mohammad Mehedi Hasan (7326065) Mohammad Tariqul Islam (7854059) Somaya Al-Maadeed (5178131) Susu M. Zughaier (14151987) Saif Badran (16888785) Suhail A. R. Doi (7490777) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Md Enamul Hoque (5807120) Nabil Ibtehaz (16888773) Saad Bin Kashem (16888776) Reehum Masud (16888779) Lutfunnahar Shampa (16888782) Mohammad Mehedi Hasan (7326065) Mohammad Tariqul Islam (7854059) Somaya Al-Maadeed (5178131) Susu M. Zughaier (14151987) Saif Badran (16888785) Suhail A. R. Doi (7490777) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2021-08-16T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3105321 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Development_and_Validation_of_an_Early_Scoring_System_for_Prediction_of_Disease_Severity_in_COVID-19_Using_Complete_Blood_Count_Parameters/24038964 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Epidemiology Health services and systems Information and computing sciences Machine learning COVID-19 Hospitals Biomarkers Machine learning Biological system modeling Pulmonary diseases Predictive models Complete blood count Prognostic model Early prediction of mortality risk Faculty of Robotics and Advanced Computing |
| dc.title.none.fl_str_mv | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3105321" target="_blank">https://dx.doi.org/10.1109/access.2021.3105321</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_23bde840bcd2c8d57642ef8e2b2d01e3 |
| identifier_str_mv | 10.1109/access.2021.3105321 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24038964 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count ParametersTawsifur Rahman (14150523)Amith Khandakar (14151981)Md Enamul Hoque (5807120)Nabil Ibtehaz (16888773)Saad Bin Kashem (16888776)Reehum Masud (16888779)Lutfunnahar Shampa (16888782)Mohammad Mehedi Hasan (7326065)Mohammad Tariqul Islam (7854059)Somaya Al-Maadeed (5178131)Susu M. Zughaier (14151987)Saif Badran (16888785)Suhail A. R. Doi (7490777)Muhammad E. H. Chowdhury (14150526)Biomedical and clinical sciencesCardiovascular medicine and haematologyHealth sciencesEpidemiologyHealth services and systemsInformation and computing sciencesMachine learningCOVID-19HospitalsBiomarkersMachine learningBiological system modelingPulmonary diseasesPredictive modelsComplete blood countPrognostic modelEarly prediction of mortality riskFaculty of Robotics and Advanced Computing<p>The 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3105321" target="_blank">https://dx.doi.org/10.1109/access.2021.3105321</a></p>2021-08-16T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3105321https://figshare.com/articles/journal_contribution/Development_and_Validation_of_an_Early_Scoring_System_for_Prediction_of_Disease_Severity_in_COVID-19_Using_Complete_Blood_Count_Parameters/24038964CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240389642021-08-16T00:00:00Z |
| spellingShingle | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters Tawsifur Rahman (14150523) Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Epidemiology Health services and systems Information and computing sciences Machine learning COVID-19 Hospitals Biomarkers Machine learning Biological system modeling Pulmonary diseases Predictive models Complete blood count Prognostic model Early prediction of mortality risk Faculty of Robotics and Advanced Computing |
| status_str | publishedVersion |
| title | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| title_full | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| title_fullStr | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| title_full_unstemmed | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| title_short | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| title_sort | Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Epidemiology Health services and systems Information and computing sciences Machine learning COVID-19 Hospitals Biomarkers Machine learning Biological system modeling Pulmonary diseases Predictive models Complete blood count Prognostic model Early prediction of mortality risk Faculty of Robotics and Advanced Computing |