Prevalence and Associated Risk Factors of Mortality Among COVID-19 Patients: A Meta-Analysis

Noor, Farjana Akther; Islam, Md. Momin

doi:10.1007/s10900-020-00920-x

Cited by 250 publications

(269 citation statements)

References 90 publications

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“…: [15,23]) probability of entering the ICU if he had none of these comorbidities, yet this risk jumped to 31% (95% C.I. : [20,46]) if he had also CKD, and to 27% in the presence of asthma/COPD (95% C.I. : [19,36]) or of weakened immune system (95% C.I.…”

Section: Results Derived From the Modelsmentioning

confidence: 99%

“…Most studies in the Middle East region focused on risk factors of mortality [16,17,18,19] among COVID-19 patients and, to our knowledge, very little (including a few papers from subsequent parts of the Jaber Hospital cohort) has been published on factors associated with the risk of ICU admission. More globally, an international meta-analysis showed that higher mortality was strongly associated with increasing age, male gender and the presence of obesity, HT, DM, CVD, and cancer, and with some excess mortality associated with several other comorbidities [20]. Other studies in the Middle East examined the risk of disease severity or of severe complications.…”

Section: Results Derived From the Modelsmentioning

confidence: 99%

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Probabilities of ICU Admission and Hospital Discharge According to Patient Characteristics in the Designated COVID-19 Hospital of Kuwait

Kipourou

Leyrat

Alsheridah

et al. 2020

Preprint

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Background Subsequent epidemic waves have already emerged in some countries and in the absence of highly effective preventive and curative options, there is a need for detailed epidemiological examination of the role of patient characteristics on the development of outcomes. The objective of this study is to describe the probabilities of admission to the Intensive Care Unit (ICU) and the probabilities of hospital discharge among positive COVID-19 patients according to demographics and comorbidities recorded at hospital admission. Methods A prospective cohort study of all patients with COVID-19 found in the Electronic Medical Records of Jaber Al-Ahmad Al-Sabah Hospital in Kuwait was conducted. The study included 3995 individuals (symptomatic and asymptomatic) of all ages who tested positive from February 24th to May 27th, 2020, out of which 315 were treated in the ICU and 3619 were discharged including those who were transferred to a different healthcare unit without previously entering the ICU. A competing risk analysis considering two events, namely, ICU admission and hospital discharge using flexible hazard models was performed to describe the association between event-specific probabilities and patient characteristics.Results Results showed that being male, increasing age and comorbidities such as chronic kidney disease (CKD), asthma or chronic obstructive pulmonary disease and weakened immune system increased the risk of ICU admission within 10 days of entering the hospital. CKD and weakened immune system decreased the probabilities of discharge in both females and males however, the age-related pattern differed by gender. Diabetes, which was the most prevalent comorbid condition, had only a moderate impact on both probabilities whilst CKD which had the largest effect, was only present in 2% of the study cohort.Conclusion This study provides useful insight in describing the probability of ICU admission and hospital discharge according to age, gender, and comorbidities among confirmed COVID-19 cases in Kuwait. A web-tool is also provided for free to allow the user to estimate these probabilities for any combination of these covariates. These probabilities enable the deeper understanding of the hospital demand according to patient characteristics which is essential to hospital management and useful for developing a vaccination strategy.

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Section: Results Derived From the Modelsmentioning

confidence: 99%

Section: Results Derived From the Modelsmentioning

confidence: 99%

Probabilities of ICU Admission and Hospital Discharge According to Patient Characteristics in the Designated COVID-19 Hospital of Kuwait

Kipourou

Leyrat

Alsheridah

et al. 2020

Preprint

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“…При сравнении ФР, влиявших на риск летального исхода заболевания, в регистре АКТИВ с аналогичными по влиянию ФР, определенными в метаанализе Noor FM, et al [20], включившем данные 58 исследований с участием 122191 пациентов и в метаанализе Parohan M, et al [21], включившем данные 14 исследований с участием 14909 пациентов (рис. 1), установлено, что существует ряд различий.…”

Section: Discussionunclassified

“…1). Наиболее значительное негативное влияние на прогноз в евроазиатской популяции пациентов с COVID-19 оказывала полиморбидность: наличие 2 и более хронических заболеваний повышало риск смерти в 9,5 раз, тогда как в международном регистре Noor FM, et al [20] только в 2,6 раза.…”

Section: Discussionunclassified

“…Ряд ФР -мужской пол, ХОБЛ, наличие онкологического заболевания, бронхиальная астма, цереброваскулярные заболевания, хронические заболевания печени, влияющие на риск развития летального исхода, в других исследованиях [20,21] при четко выраженной тенденции не достигли статистической достоверности в регистре АКТИВ. Нам представляется, что возможным объяснением могут служить различия в объёмах проанализированной популяции.…”

Section: Discussionunclassified

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International register “Dynamics analysis of comorbidities in SARS-CoV-2 survivors” (AKTIV SARS-CoV-2): analysis of 1,000 patients

et al. 2020

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COVID-19 is a severe infection with high mortality. The concept of the disease has been shaped to a greater extent on the basis of large registers from the USA, Spain, Italy, and China. However, there is no information on the disease characteristics in Caucasian patients.Therefore, we created an international register with the estimated capacity of 5,000 patients — Dynamics Analysis of Comorbidities in SARS-CoV-2 Survivors (AKTIV SARS-CoV-2), which brought together professionals from the Russian Federation, Republic of Armenia, Republic of Kazakhstan, and Kyrgyz Republic. The article presents the first analysis of the register involving 1,003 patients. It was shown that the most significant difference of the Caucasian population was the higher effect of multimorbidity on the mortality risk vs other registers. More pronounced effect on mortality of such diseases as diabetes, obesity, hypertension, chronic kidney disease, and age over 60 years was also revealed.

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Predicting the mortality of patients with Covid‐19: A machine learning approach

Emami

Rabiei

Sohrabei

et al. 2023

Health Science Reports

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Background and Aims Infection with Covid‐19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid‐19 using machine learning techniques can be effective in reducing mortality rate in Covid‐19. The aim of this study is to compare four machine‐learning algorithm for predicting mortality in Covid‐19 disease. Methods The data of this study were collected from hospitalized patients with COVID‐19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid‐19. Each record contained 38 variables. Four machine‐learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion Considering the combination of multiple influential factors affecting death Covid‐19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

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Prevalence and Associated Risk Factors of Mortality Among COVID-19 Patients: A Meta-Analysis

Cited by 250 publications

References 90 publications

Probabilities of ICU Admission and Hospital Discharge According to Patient Characteristics in the Designated COVID-19 Hospital of Kuwait

Probabilities of ICU Admission and Hospital Discharge According to Patient Characteristics in the Designated COVID-19 Hospital of Kuwait

International register “Dynamics analysis of comorbidities in SARS-CoV-2 survivors” (AKTIV SARS-CoV-2): analysis of 1,000 patients

Predicting the mortality of patients with Covid‐19: A machine learning approach

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