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  1. Article ; Online: Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients.

    Liu, Hui / Chen, Jing / Yang, Qin / Lei, Fang / Zhang, Changjiang / Qin, Juan-Juan / Chen, Ze / Zhu, Lihua / Song, Xiaohui / Bai, Liangjie / Huang, Xuewei / Liu, Weifang / Zhou, Feng / Chen, Ming-Ming / Zhao, Yan-Ci / Zhang, Xiao-Jing / She, Zhi-Gang / Xu, Qingbo / Ma, Xinliang /
    Zhang, Peng / Ji, Yan-Xiao / Zhang, Xin / Yang, Juan / Xie, Jing / Ye, Ping / Azzolini, Elena / Aghemo, Alessio / Ciccarelli, Michele / Condorelli, Gianluigi / Stefanini, Giulio G / Xia, Jiahong / Zhang, Bing-Hong / Yuan, Yufeng / Wei, Xiang / Wang, Yibin / Cai, Jingjing / Li, Hongliang

    Med (New York, N.Y.)

    2021  Volume 2, Issue 4, Page(s) 435–447.e4

    Abstract: ... and accurate risk assessment tool that can predict the mortality for COVID-19 patients during ... Background: To develop a sensitive risk score predicting the risk of mortality in patients ... with coronavirus disease 2019 (COVID-19) using complete blood count (CBC).: Methods: We performed ...

    Abstract Background: To develop a sensitive risk score predicting the risk of mortality in patients with coronavirus disease 2019 (COVID-19) using complete blood count (CBC).
    Methods: We performed a retrospective cohort study from a total of 13,138 inpatients with COVID-19 in Hubei, China, and Milan, Italy. Among them, 9,810 patients with
    Findings: Five risk factors were derived to construct a composite score (PAWNN score) using the Cox regression model, including platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio. The PAWNN score showed good accuracy for predicting mortality in 10-fold cross-validation (AUROCs 0.92-0.93) and subsets with different quartile intervals of follow-up and preexisting diseases. The performance of the score was further validated in 2,949 patients with only 1 CBC record from the Hubei cohort (AUROC 0.97) and 227 patients from the Italian cohort (AUROC 0.80). The latent Markov model (LMM) demonstrated that the PAWNN score has good prediction power for transition probabilities between different latent conditions.
    Conclusions: The PAWNN score is a simple and accurate risk assessment tool that can predict the mortality for COVID-19 patients during their entire hospitalization. This tool can assist clinicians in prioritizing medical treatment of COVID-19 patients.
    Funding: This work was supported by National Key R&D Program of China (2016YFF0101504, 2016YFF0101505, 2020YFC2004702, 2020YFC0845500), the Key R&D Program of Guangdong Province (2020B1111330003), and the medical flight plan of Wuhan University (TFJH2018006).
    MeSH term(s) Blood Cell Count ; COVID-19 ; Hospital Mortality ; Humans ; Prognosis ; Retrospective Studies ; Risk Factors ; SARS-CoV-2
    Language English
    Publishing date 2021-01-08
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2666-6340
    ISSN (online) 2666-6340
    DOI 10.1016/j.medj.2020.12.013
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters.

    Rahman, Tawsifur / Khandakar, Amith / Hoque, Md Enamul / Ibtehaz, Nabil / Kashem, Saad Bin / Masud, Reehum / Shampa, Lutfunnahar / Hasan, Mohammad Mehedi / Islam, Mohammad Tariqul / Al-Maadeed, Somaya / Zughaier, Susu M / Badran, Saif / Doi, Suhail A R / Chowdhury, Muhammad E H

    IEEE access : practical innovations, open solutions

    2021  Volume 9, Page(s) 120422–120441

    Abstract: ... the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count ... the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries. ... for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 ...

    Abstract 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.
    Language English
    Publishing date 2021-08-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2687964-5
    ISSN 2169-3536
    ISSN 2169-3536
    DOI 10.1109/ACCESS.2021.3105321
    Database MEDical Literature Analysis and Retrieval System OnLINE

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