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Blood Transfusion, All-Cause Mortality and Hospitalization Period in COVID-19 Patients: Machine Learning Analysis of National Health Insurance Claims Dataopen access

Authors
Lee, Byung-HyunLee, Kwang-SigKim, Hae-InJung, Jae-SeungShin, Hyeon-JuPark, Jong-HoonHong, Soon-CheolAhn, Ki Hoon
Issue Date
Dec-2022
Publisher
MDPI AG
Keywords
blood transfusion; mortality; hospitalization; COVID-19; machine learning
Citation
Diagnostics, v.12, no.12
Indexed
SCIE
SCOPUS
Journal Title
Diagnostics
Volume
12
Number
12
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/62105
DOI
10.3390/diagnostics12122970
ISSN
2075-4418
Abstract
This study presents the most comprehensive machine-learning analysis for the predictors of blood transfusion, all-cause mortality, and hospitalization period in COVID-19 patients. Data came from Korea National Health Insurance claims data with 7943 COVID-19 patients diagnosed during November 2019-May 2020. The dependent variables were all-cause mortality and the hospitalization period, and their 28 independent variables were considered. Random forest variable importance (GINI) was introduced for identifying the main factors of the dependent variables and evaluating their associations with these predictors, including blood transfusion. Based on the results of this study, blood transfusion had a positive association with all-cause mortality. The proportions of red blood cell, platelet, fresh frozen plasma, and cryoprecipitate transfusions were significantly higher in those with death than in those without death (p-values < 0.01). Likewise, the top ten factors of all-cause mortality based on random forest variable importance were the Charlson Comorbidity Index (53.54), age (45.68), socioeconomic status (45.65), red blood cell transfusion (27.08), dementia (19.27), antiplatelet (16.81), gender (14.60), diabetes mellitus (13.00), liver disease (11.19) and platelet transfusion (10.11). The top ten predictors of the hospitalization period were the Charlson Comorbidity Index, socioeconomic status, dementia, age, gender, hemiplegia, antiplatelet, diabetes mellitus, liver disease, and cardiovascular disease. In conclusion, comorbidity, red blood cell transfusion, and platelet transfusion were the major factors of all-cause mortality based on machine learning analysis. The effective management of these predictors is needed in COVID-19 patients.
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2. Clinical Science > Department of Obstetrics and Gynecology > 1. Journal Articles
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Anam Hospital (Department of Thoracic and Cardiovascular Surgery, Anam Hospital)
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