A novel scoring system in mortality prediction of severe patients with COVID-19

Shao, Anwen; Zhou, Yunxiang; Tu, Sheng; Sheng, Jifang

doi:10.1016/j.eclinm.2020.100450

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…COVID-19 infection is considered a pandemic across the whole world and has been declared as a global public health emergency situation by the world health organization [1, 2]. COVID-19 was primarily reported in China [3]. The mortality rate across countries had ranged from 1% up to more than 10% [4] and it is underestimated in some countries [5].…”

Section: Introductionmentioning

confidence: 99%

“…in the other hand, epidemiological models for forecasting the excess mortality and for forecasting the pandemic itself were developed [14, 15]. Many mortality scores were developed to predict COVID-19 outcomes [3, 16, 17]. United nations, OCHA reported that the average COVID-19 daily cases in Sudan had reached 300 cases in December 2020, with Khartoum State accounting for 78% of the reported cases [18].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of COVID-19 mortality among hospitalized patients in Sudan

El‐Raheem

Yousif²,

Mohamed³

2021

Preprint

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Background: COVID-19 was primarily reported in China. The mortality rate across countries had ranged from 1% up to more than 10% and it is underestimated in some countries. Advanced age is the most frequently reported factor associated to mortality. Other factors were the presence of comorbidities such as diabetes mellitus, hypertension and obesity. Several models for mortality prediction had been developed to assist in improving the prognosis. The aim of our study was to assess the factors related to mortality among COVID-19 patients and develop a prediction model based on these factors. Methods: A retrospective cohort study assessed the factors related to the mortality among COVID-19 patients who attended Imperial Hospital isolation centre on November- December, 2020, Khartoum, Sudan. Statistical tests performed were chi-square test, odds ratio and regression to develop the prediction model. Tests were considered statistically significant when p < 0.05. Results: 105 patients were studied. 29% of the patients were deceased, while, 71% were discharged alive. A statistically significant association was found between the age and severity with regards to mortality rate (p=0.034, 0.018 respectively). The model equation for mortality prediction: Mortality = -14.724+ (1.387* Age) + (-0.323* Gender) + (1.814* Admission) + ( 0.193* Ischemic Heart Disease) + ( -0.369* Fever) + ( 1.595* Cough) + ( 1.953* Complications) + ( 0.149* Duration of hospitalization) + ( 0.999* Enoxaparin dose). Conclusions: Age, admission ward, cough and enoxaparin dose were statistically significant predictors for COVID-19 mortality ( p= 0.014, 0.011, 0.015, 0.006 respectively).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 mortality among hospitalized patients in Sudan

El‐Raheem

Yousif²,

Mohamed³

2021

Preprint

View full text Add to dashboard Cite

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Evaluation of the effect values of risk factors by clustering method in patients who died due to COVID-19 disease

METİN

Eraybar

Aygün

et al. 2022

The European Research Journal

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Objectives: The aim of this study is to determine the factors that may be associated with mortality in patients who died due to COVID-19 and to determine the effect sizes of the factors that make a statistically significant difference. Methods: The patients who died due to COVID-19 between 01.03.2020 and 01.03.2021 in Bursa province were evaluated retrospectively. In addition to demographic information such as age, gender, nationality, existing chronic diseases of the patients, COVID- PCR test results, length of hospital stay, intensive care unit follow up times, intubation application times were recorded. The effect size of the variables on mortality were evaluated. Results: Total of 3,510 deaths due to COVID-19 were evaluated. Of these, 2,107 (60%) were male and 1,403 (40%) were female. Three thousand three hundred and seventy-four (96.12%) patients are 50 years or older. In both sexes, the highest number of deaths were in the age range of 70-79. The most common comorbidities were hypertension (HT) (n = 1,182; 34.16%) and diabetes mellitus (DM) (n =776; 22.43%). HT and DM had a strong effect value between the groups (p < 0.001 and p < 0.001, phi effect values: 0.661 and 0.681, respectively). Although there was a statistically significant difference for the age variable, it had an insignificant effect value (p = 0.008, 𝛿 = 0.074). Conclusions: Risk factors frequently reported for COVID-19 deaths but there are no studies showing the true effect values. In this study, HT and DM had a strong effect separately, gender and coronary artery disease (CAD) variables were moderate, chronic obstructive pulmonary disease (COPD), lung cancer and other chronic disease variables had weak effect values, age and non-lung cancers had insignificant effect.

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A novel scoring system in mortality prediction of severe patients with COVID-19

Cited by 2 publications

References 10 publications

Prediction of COVID-19 mortality among hospitalized patients in Sudan

Prediction of COVID-19 mortality among hospitalized patients in Sudan

Evaluation of the effect values of risk factors by clustering method in patients who died due to COVID-19 disease

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