Mortality prediction utilizing blood biomarkers to predict the severity of COVID-19 using machine learning technique
Author | Rahman T. |
Author | Al-Ishaq F.A. |
Author | Al-Mohannadi F.S. |
Author | Mubarak R.S. |
Author | Al-Hitmi M.H. |
Author | Islam K.R. |
Author | Khandakar A. |
Author | Hssain A.A. |
Author | Al-Maadeed, Somaya |
Author | Zughaier S.M. |
Author | Chowdhury M.E.H. |
Available date | 2022-05-19T10:23:06Z |
Publication Date | 2021 |
Publication Name | Diagnostics |
Resource | Scopus |
Identifier | http://dx.doi.org/10.3390/diagnostics11091582 |
Abstract | 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 |
Sponsor | Funding: 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. |
Language | en |
Publisher | MDPI |
Subject | biological 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 |
Type | Article |
Issue Number | 9 |
Volume Number | 11 |
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Computer Science & Engineering [2288 items ]
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COVID-19 Research [811 items ]