Does comorbidity increase the risk of patients with COVID-19: evidence from meta-analysis

Wang, Bolin; Ruo-bao, LI; Lü, Zhong; Huang, Yan

doi:10.18632/aging.103000

Cited by 1,087 publications

(1,092 citation statements)

References 13 publications

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“…A previous meta-analysis found that there was no correlation between cancer and the severity of patients with COVID-19 [7]. However, our meta-analysis indicated .…”

Section: Comparison With Other Studiescontrasting

confidence: 79%

“…CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) that cancer was significantly associated with an increased risk of severe COVID-19 disease, which was inconsistent with the previous meta-analysis [7]. The previous reviewers conducted a meta-analysis based on only four studies with a sample of 1,356 patients [7].…”

Section: Comparison With Other Studiesmentioning

confidence: 93%

“…As of April 22, 2020, a total of 2,471,136 laboratory-confirmed cases were reported worldwide, with a mortality rate of 6.8% [4]. Previous studies have shown that comorbidities in patients with COVID-19 are associated with poor prognosis, including hypertension, diabetes, chronic obstructive pulmonary disease (COPD), cardiovascular disease, and cerebrovascular disease [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Cancer is associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis

Gao

Liu

Shi

et al. 2020

Preprint

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word count: 249. AbstractBackground: Cancer patients are considered a highly vulnerable population in the COVID-19 epidemic, but the relationship between cancer and the severity and mortality of patients with COVID-19 remains unclear. This study aimed to explore the prevalence of cancer in patients with COVID-19 and to examine whether cancer patients with COVID-19 may be at an increased risk of severe illness and mortality. Methods:A comprehensive electronic search in seven databases was performed, to identified studies reporting the prevalence of cancer in COVID-19 patients, or providing data of cancer between patients with severe or non-severe illness or between non-survivors and survivors. Meta-analyses were performed to estimate the pooled prevalence and odds risk (OR) using the inverse variance method with the random-effects model. Results:Thirty-four studies with 8080 patients were included. The pooled prevalence of cancer in patients with COVID-19 was 2.0% (95% CI: 2.0% to 3.0%). The prevalence in Italy (5.0%), France (6.0%), and Korea (4.0%) were higher than that in China (2.0%). Cancer was associated with a 2.84-fold significantly increased risk of severe illness (OR = 2.84, 95%CI: 1.75 to 4.62, P < 0.001) and a 2.60-fold increased risk of death (OR = 2.60, 95%CI: 1.28 to 5.26, P = 0.008) in patients with COVID-19.Sensitivity analyses showed that the results were stable after excluding studies with a sample size of less than 100. : medRxiv preprint 3 associated with a significantly increased risk of severity and mortality of patients with COVID-19.

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“…A previous meta-analysis found that there was no correlation between cancer and the severity of patients with COVID-19 [7]. However, our meta-analysis indicated .…”

Section: Comparison With Other Studiescontrasting

confidence: 79%

Section: Comparison With Other Studiesmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Cancer is associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis

Gao

Liu

Shi

et al. 2020

Preprint

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“…Yet other studies took into account deaths due to Covid and age of people, location and other demographic details (Henderson & Keiding, 2005;Hollnagel, 1999;Soyiri & Reidpath, 2013;Zeegers et al, 2016). There are studies and discussions that point to comorbidity as a significant factor for the number of casualties (Guan et al, 2020;Wang et al, 2020). Without considering comorbidity, fatalities may be mistakenly read as covid deaths.…”

Section: Discussionmentioning

confidence: 99%

A model for survival rate projection of Covid19 patients

Madalli¹,

Gajbe²,

Gopalji³

et al. 2020

Preprint

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The novel coronavirus has been declared a worldwide pandemic. The pandemic has unleashed health as well as economic devastation across the world. In view of this, various governments, researchers and agencies are trying to work towards solutions to control the spread of the virus. There are many studies that have been initiated that consider the various parameters that contribute to the spread of the virus. We present a model to predict the survival rate of Covid19 infected patients. The study takes into consideration death rate (normal) and Covid infection death rate in India. Other factors considered are the number of infections and active infection numbers. We also incorporate a ‘learning module’ to learn from the observed error rate. We compute the moving average of error which is then deployed to minimise projection error in the model. The factor obtained from the learning is deployed into the survival rate computation to achieve best results.

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“…The virus-laden droplets deposit to the lining of the nose and throat of the nearby people who inhale them 79,80 . COVID-19 is turned out to be the most lethal disease in the world with about 68% of the mortality are linked to comorbidity 81 . Our study clearly evident that the potential role of resveratrol to control the COVID-19 epidemic.…”

Section: Resveratrol a Potential Drug To Human Coronaviruses (Covs) Wmentioning

confidence: 99%

Identification of pulmonary comorbid diseases network based repurposing effective drugs for COVID-19

Patel

Tulswani

Khurana

et al. 2020

Preprint

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The number of hospitalization of COVID-19 patients with one or more comorbid diseases is highly alarming. Despite the lack of large clinical data and incomplete understanding of virus pathology, identification of the COVID-19 associated diseases with clinical precision are highly limited. In this regard, our text mining of 6238 PubMed abstracts (as on 23 April 2020) successfully identified broad spectrum of COVID-19 comorbid diseases/disorders (54), and their prevalence on the basis of the number of occurrence of disease terms in the abstracts. The disease ontology based semantic similarity network analysis revealed the six highly comorbid diseases of COVID-19 namely Viral Pneumonia, Pulmonary Fibrosis, Pulmonary Edema, Acute Respiratory Distress Syndrome (ARDS), Chronic Obstructive Pulmonary Disease (COPD) and Asthma. The disease gene bipartite network revealed 15 genes that were strongly associated with several viral pathways including the corona viruses may involve in the manifestation (mild to critical) of COVID-19. Our tripartite network- based repurposing of the approved drugs in the world market revealed six promising drugs namely resveratrol, dexamethasone, acetyl cysteine, Tretinoin, simvastatin and aspirin to treat comorbid symptoms of COVID-19 patients. Our animal studies in rats and literatures strongly supported that resveratrol is the most promising drug to possibly reduce several comorbid symptoms associated with COVID-19 including the severe hypoxemia induced vascular leakage. Overall, the anti-viral properties of resveratrol against corona virus could be readily exploited to effectively control the viral load at early stage of COVID-19 infection through nasal administration.

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Does comorbidity increase the risk of patients with COVID-19: evidence from meta-analysis

Cited by 1,087 publications

References 13 publications

Cancer is associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis

Cancer is associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis

A model for survival rate projection of Covid19 patients

Identification of pulmonary comorbid diseases network based repurposing effective drugs for COVID-19

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