Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Autor:	Susu M. Zughaier, Muhammad E. H. Chowdhury, Reem S Mubarak, Somaya Al-Madeed, Fajer A. Al-Ishaq, Khandaker Reajul Islam, Fatima S. Al-Mohannadi, Tawsifur Rahman, Amith Khandakar, Ali Ait Hssain, Maryam Al-Hitmi
Rok vydání:	2021
Předmět:	Medicine (General) medicine.medical_specialty education.field_of_study business.industry Clinical Biochemistry Population biomarkers COVID-19 Emergency department Disease Nomogram Logistic regression Article coagulopathy R5-920 machine learning D-dimer Cohort Emergency medicine Health care Risk of mortality Medicine business education
Zdroj:	Diagnostics Volume 11 Issue 9 Diagnostics, Vol 11, Iss 1582, p 1582 (2021)
ISSN:	2075-4418
Popis:	Healthcare 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6186a5bc34f1f9504da8c83d391482c3 https://pubmed.ncbi.nlm.nih.gov/34573923 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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