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AuthorRahman T.
AuthorAl-Ishaq F.A.
AuthorAl-Mohannadi F.S.
AuthorMubarak R.S.
AuthorAl-Hitmi M.H.
AuthorIslam K.R.
AuthorKhandakar A.
AuthorHssain A.A.
AuthorAl-Maadeed, Somaya
AuthorZughaier S.M.
AuthorChowdhury M.E.H.
Available date2022-05-19T10:23:06Z
Publication Date2021
Publication NameDiagnostics
ResourceScopus
Identifierhttp://dx.doi.org/10.3390/diagnostics11091582
URIhttp://hdl.handle.net/10576/31085
AbstractHealthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management
SponsorFunding: This work was supported in part by the Qatar National Research under Grant UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
Subjectbiological marker
C reactive protein
creatinine
D dimer
lactate dehydrogenase
adult
aged
area under the curve
Article
blood cell count
China
cohort analysis
comparative study
controlled study
coronavirus disease 2019
coughing
disease severity
dyspnea
emergency ward
feature selection
female
hospital admission
human
lymphocyte count
machine learning
major clinical study
male
middle aged
mortality risk
nomogram
oxygen consumption
oxygen saturation
pandemic
physician
positive pressure ventilation
prediction
prognosis
retrospective study
thorax pain
TitleMortality prediction utilizing blood biomarkers to predict the severity of COVID-19 using machine learning technique
TypeArticle
Issue Number9
Volume Number11
dc.accessType Abstract Only


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