Hospitalization and mortality associated with SARS-CoV-2 viral clades in COVID-19

Nakamichi, Kenji; Shen, Jolie; Lee, Cecilia S.; Lee, Aaron; Roberts, Emma A.; Simonson, Paul D.; Roychoudhury, Pavitra; Andriesen, Jessica; Randhawa, April; Mathias, Patrick C.; Greninger, Alexander L.; Jerome, Keith R.; Gelder, Russell N. Van

doi:10.1038/s41598-021-82850-9

Cited by 59 publications

(53 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They showed that 17 variants had a two-fold higher risk of severe COVID-19, while 67 variants were associated with less severe COVID-19 [33]. This is in line with another study from France and the US, suggesting that different viral variants may result in different infection severity and risk of hospitalization [34,35]. Since SARS-CoV-2 is an RNA virus, its dynamic evolution is expected to in uence its biological characteristic [36], including its virulence and pathogenicity [33].…”

Section: Discussionsupporting

confidence: 68%

The Impact of SARS-CoV-2 Multiple Spike Protein Mutations on COVID-19 Outcomes

Gunadi

Hakim

Wibawa

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Recent studies focusing on the association of SARS-CoV-2 variants of concern (VOC) on COVID-19 outcomes have been reported. However, studies of the impact of multiple mutations within the spike (S) protein of SARS-CoV-2 on COVID-19 illness are limited. This study determined the association between multiple mutations within the S protein, prognosis factors, and the disease outcomes of SARS-CoV-2 infection. Methods: We included 51 COVID-19 patients from Yogyakarta and Central Java, Indonesia. Whole genome sequences of SARS-CoV-2 were determined by the Illumina MiSeq next-generation sequencer, followed by the phylogenetic analysis of 170 full-genomes of SARS-CoV-2 from different regions. We analyzed characteristics of COVID-19 patients and multiple mutations in association with different outcomes.Results: Among 51 patients, the clinical manifestations of COVID-19 were as follows: without any symptoms (13.7%), mild (47%), moderate (19.6%), severe (4%), critical (2%), and died (13.7%). The age of hospitalized patients (53.4 ± 18 years) was higher than non-hospitalized patients (34.6 ± 19) (p=0.001). A significant association between diabetes, hypertension, and anticoagulant and the hospitalization of patients was noted with p-value of 0.039 (OR=4.47 [95% CI=1.07-18.58]), 0.001 (OR=17 [95% CI=2-144]), and 0.02 (OR=27.97 [95% CI=1.54-507.13]), respectively; whereas a strong association between patients’ age, diabetes, anticoagulant, and steroid with the mortality of patients was revealed with p-value of 0.016 (OR=8.44 [95% CI=1.5-47.49]), 0.019 (OR=8.5 [95% CI=1.43-50.66]), 0.001 (46.8 [95% CI=4.63-472.77]), and 0.009 (OR=15.75 [95% CI=2-123.86]), respectively. All viruses contained the D614G variant, except one case. Accordingly, the samples were classified as the following clade: L (2%), GH (84.3%), GR (11.7%), and O (2%). Besides the D614G, the most common variants in the S protein were L5F (18.8%), V213A (18.8%), and S689R (8.3%). There was no significant association between multiple S protein variants with either hospitalization or mortality of COVID-19 (p=0.11 and 0.69, respectively). Multivariate analysis showed that hypertension and anticoagulant were the strong factors affecting the hospitalization and mortality of patients with COVID-19 with a p-value of 0.009 (OR=17.06 [95% CI=2.02-144.36]) and 0.001 (OR=46.8 (95% CI=4.63-472.77), respectively. Interestingly, the multiple S protein variants almost reached a significant level affecting the hospitalization of patients (p=0.07). Phylogenetic analysis showed that although most of the viruses from this study belonged to clade GH, none were detected as the variant of concern (VOC) and the variant of interest (VOI) of SARS-CoV-2.Conclusions: Here, we show for the first time the association between SARS-CoV-2 mutations within the S protein besides the VOC with the COVID-19 outcomes. Our findings suggest that multiple mutations in the S protein might affect the severity of COVID-19. Our study further suggests the importance of genomic surveillance to monitor SARS-CoV-2 variants, particularly those that might influence the outcomes of COVID-19 patients.

show abstract

Section: Discussionsupporting

confidence: 68%

The Impact of SARS-CoV-2 Multiple Spike Protein Mutations on COVID-19 Outcomes

Gunadi

Hakim

Wibawa

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A study conducted on isolates from patients in Washington, US, allowed the identification of two major clades distinguished by twelve polymorphisms in five genes. No significant difference regarding mortality was observed among patients infected with these two clades [43]. In a study conducted on 88 patients in the USA, most of the sequences (93%) were clustered in three main clades (clades 1, 2 and 3), defining mutations at the US level.…”

Section: Relation Between Viral Mutations and Severity Of Covid-19 Infectionmentioning

confidence: 94%

“…Of the 63 publications included, five were non-peer reviewed preprints [16,17,52,54,66]. Eleven articles reported in vitro, in silico or animal model studies [15,18,19,[22][23][24][25][26][27][28][29]; nineteen articles reported clinical studies [16,[37][38][39][41][42][43][44][45][46][47][48][49]51,[70][71][72][73]. The remaining 33 articles analyzed SARS-CoV-2 genomes downloaded from the GISAID or other available databases with patient status [8][9][10][11]20,21,[30][31][32][33][34][35][36]40,50,…”

Section: Study Selection and Types Of Studiesmentioning

confidence: 99%

“…A total of 45 articles addressed the effect of viral mutations on severity of COVID-19 in patients, of which fifteen studies (five bigdata analyses [21,30,33,34,40] and ten clinical studies [39,[41][42][43][44][45][46][47][48][49]) showed no association between SARS-CoV-2 variants and severity of disease. Three studies (one bigdata analysis [50] and two clinical studies [17,51]) reported that the SARS-CoV-2 variants were significantly associated with decrease in severity of disease.…”

Section: Relation Between Viral Mutations and Severity Of Covid-19 Infectionmentioning

confidence: 99%

See 1 more Smart Citation

SARS-CoV-2 Infectivity and Severity of COVID-19 According to SARS-CoV-2 Variants: Current Evidence

Dao

Hoang

Colson

et al. 2021

JCM

View full text Add to dashboard Cite

Background: We conducted this review to summarize the relation between viral mutation and infectivity of SARS-CoV-2 and also the severity of COVID-19 in vivo and in vitro. Method: Articles were identified through a literature search until 31 May 2021, in PubMed, Web of Science and Google Scholar. Results: Sixty-three studies were included. To date, most studies showed that the viral mutations, especially the D614G variant, correlate with a higher infectivity than the wild-type virus. However, the evidence of the association between viral mutation and severity of the disease is scant. A SARS-CoV-2 variant with a 382-nucleotide deletion was associated with less severe infection in patients. The 11,083G > U mutation was significantly associated with asymptomatic patients. By contrast, ORF1ab 4715L and S protein 614G variants were significantly more frequent in patients from countries where high fatality rates were also reported. The current evidence showed that variants of concern have led to increased infectivity and deteriorating epidemiological situations. However, the relation between this variant and severity of COVID-19 infection was contradictory. Conclusion: The COVID-19 pandemic continues to spread worldwide. It is necessary to anticipate large clinical cohorts to evaluate the virulence and transmissibility of SARS-CoV-2 mutants.

show abstract

“…In the early detection and prognosis studies that integrated heterogenous data ( Table 3 ), 8 studies imputed missing data. Most studies performed simple imputation based on mean, mode, or median values, while 2 studies performed multivariate imputation by chained equations, 100 , 104 and 1 study imputed missing values using bagging trees. 96 …”

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Guo

Zhang

Lyu

et al. 2021

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. Results In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Discussion Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. Conclusion There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

show abstract

Hospitalization and mortality associated with SARS-CoV-2 viral clades in COVID-19

Cited by 59 publications

References 35 publications

The Impact of SARS-CoV-2 Multiple Spike Protein Mutations on COVID-19 Outcomes

The Impact of SARS-CoV-2 Multiple Spike Protein Mutations on COVID-19 Outcomes

SARS-CoV-2 Infectivity and Severity of COVID-19 According to SARS-CoV-2 Variants: Current Evidence

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Contact Info

Product

Resources

About