| Autor: |
Rahman T; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar., Al-Ishaq FA; Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar., Al-Mohannadi FS; Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar., Mubarak RS; Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar., Al-Hitmi MH; Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar., Islam KR; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar., Khandakar A; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar., Hssain AA; Medical ICU, Hamad General Hospital, Doha 3050, Qatar., Al-Madeed S; Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar., Zughaier SM; Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar., Chowdhury MEH; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar. |
| Abstrakt: |
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. |
