Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: A complex relationship

Dananché, Cédric; Elias, Christelle; Hénaff, Laetitia; Amour, Sélilah; Kuczewski, Elisabetta; Gustin, Marie-Paule; Escuret, Vanessa; Saadatian‐Elahi, Mitra; Vanhems, Philippe

doi:10.1371/journal.pone.0261428

Cited by 9 publications

(7 citation statements)

References 34 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This difference is due to the fact that January 1 was the peak month of the epidemic in Hanoi, although fewer patients were hospitalized, but deaths rate was higher due to late addmision . Our study differed from the results of Danaché et al ( 2022) with the majority age in over 75 group (46,4%) [2].…”

Section: Discussioncontrasting

confidence: 99%

Epidemiological Characteristics of Death Cases by Covid-19 at Duc Giang General Hospital From January to February, 2022

Thường

Xuyen

Nga³

et al. 2023

VMJ

View full text Add to dashboard Cite

The study was conducted to describe the epidemiological characteristics of the patients who died of COVID-19 at Duc Giang General Hospital from January to February, 2022. This is a cross-sectional study using quantitative methods. Research result shows that: Mortality accounted for 27% of hospitalizations; Rate of deaths in the first 24 hours and 1-7 days were 6% and 49,1%, respectively; The majority of death cases were over 70 years old (90,7%); 74,7% mortal patients had comorbidities: hypertension (47,6%), cardiovascular disease (22,3%), cerebral stroke (17,5%); 31,6% mortal patients had vaccination of Covid 19, of which, 17,8% got full vaccination. Clinical classification upon admission: 46,4% critical, 40,8% severe, 9% medium 3,8% mild; Common causes of death included: respiratory failure (84,4%), Sepsis (37,2%), heart failure (32,3%).

show abstract

Section: Discussioncontrasting

confidence: 99%

Epidemiological Characteristics of Death Cases by Covid-19 at Duc Giang General Hospital From January to February, 2022

Thường

Xuyen

Nga³

et al. 2023

VMJ

View full text Add to dashboard Cite

show abstract

“…The need for admission of COVID-19 patients in the general population depends mainly on their age and preexisting comorbidities, such as chronic respiratory diseases and DM [ 21 , 22 ]. The likelihood of hospitalization increases with age up to a maximum of 18.4% in patients ≥80 years old [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Outcomes of COVID-19 in Inflammatory Rheumatic Diseases: A Retrospective Cohort Study

Alhowaish¹,

Alhamadh²,

Alhabeeb³

et al. 2022

Cureus

View full text Add to dashboard Cite

BackgroundSimilar to coronavirus disease 2019 , the pathogenesis of inflammatory rheumatic diseases includes cytokines dysregulation and increased expression of pro-inflammatory cytokines. Although current data from international studies suggest that rheumatic diseases are associated with a higher risk of COVID-19 infection and worse outcomes, there is limited literature in Saudi Arabia. This study aims to evaluate the outcomes and length of hospital stay of COVID-19 patients with inflammatory rheumatic diseases in Saudi Arabia. MethodThis was a single-center retrospective cohort study that included 122 patients with inflammatory rheumatic diseases and documented coronavirus disease 2019 (COVID-19) infection from 2019 to 2021. Patients with suspected COVID-19 infection, non-inflammatory diseases, such as osteoarthritis, or inflammatory diseases but without or with weak systemic involvement, such as gout, were excluded. ResultsThe vast majority (81.1%) of the patients were females. Rheumatoid arthritis was the most common primary rheumatological diagnosis. The admission rate was 34.5% with an overall mortality rate of 11.5%. Number of episodes of COVID-19 infection, mechanical ventilation, cytokine storm syndrome, secondary bacterial infection, number of comorbidities, rituximab, diabetes mellitus, hypertension, chronic kidney disease, and heart failure were significantly associated with a longer hospital stay. Additionally, hypertension, heart failure, rituximab, mechanical ventilation, cytokine storm syndrome, and secondary bacterial infection were significantly associated with higher mortality. Predictors of longer hospitalization were obesity, number of episodes of COVID-19 infection, mechanical ventilation, number of comorbidities, and chronic kidney disease, whereas, hypertension was the only predictor of mortality. ConclusionObesity, number of episodes of COVID-19 infection, mechanical ventilation, number of comorbidities, and chronic kidney disease were significantly associated with higher odds of longer hospitalization, whereas, hypertension was significantly associated with higher odds of mortality. We recommend that these patients should be prioritized for the COVID-19 vaccine booster doses, and rituximab should be avoided unless its benefit clearly outweighs its risk.

show abstract

“…In periods of high pressure on the healthcare system, the delay between the onset of symptoms and diagnosis or hospital admission generally increases [103]. This delay has been shown to be positively associated with the severity of COVID-19 [104][105][106]. Additionally, hospital and ICU loads increase in epidemiological wave periods, with increased loads associated with increased disease severity and higher in-hospital mortality [107][108][109][110] of COVID-19 patients.…”

Section: Pressure On the Healthcare Systemmentioning

confidence: 99%

Evaluating methodological approaches to assess the severity of infection with SARS-CoV-2 variants: scoping review and applications on Belgian COVID-19 data

et al. 2022

View full text Add to dashboard Cite

Background Differences in the genetic material of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants may result in altered virulence characteristics. Assessing the disease severity caused by newly emerging variants is essential to estimate their impact on public health. However, causally inferring the intrinsic severity of infection with variants using observational data is a challenging process on which guidance is still limited. We describe potential limitations and biases that researchers are confronted with and evaluate different methodological approaches to study the severity of infection with SARS-CoV-2 variants. Methods We reviewed the literature to identify limitations and potential biases in methods used to study the severity of infection with a particular variant. The impact of different methodological choices is illustrated by using real-world data of Belgian hospitalized COVID-19 patients. Results We observed different ways of defining coronavirus disease 2019 (COVID-19) disease severity (e.g., admission to the hospital or intensive care unit versus the occurrence of severe complications or death) and exposure to a variant (e.g., linkage of the sequencing or genotyping result with the patient data through a unique identifier versus categorization of patients based on time periods). Different potential selection biases (e.g., overcontrol bias, endogenous selection bias, sample truncation bias) and factors fluctuating over time (e.g., medical expertise and therapeutic strategies, vaccination coverage and natural immunity, pressure on the healthcare system, affected population groups) according to the successive waves of COVID-19, dominated by different variants, were identified. Using data of Belgian hospitalized COVID-19 patients, we were able to document (i) the robustness of the analyses when using different variant exposure ascertainment methods, (ii) indications of the presence of selection bias and (iii) how important confounding variables are fluctuating over time. Conclusions When estimating the unbiased marginal effect of SARS-CoV-2 variants on the severity of infection, different strategies can be used and different assumptions can be made, potentially leading to different conclusions. We propose four best practices to identify and reduce potential bias introduced by the study design, the data analysis approach, and the features of the underlying surveillance strategies and data infrastructure.

show abstract

Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: A complex relationship

Cited by 9 publications

References 34 publications

Epidemiological Characteristics of Death Cases by Covid-19 at Duc Giang General Hospital From January to February, 2022

Epidemiological Characteristics of Death Cases by Covid-19 at Duc Giang General Hospital From January to February, 2022

Outcomes of COVID-19 in Inflammatory Rheumatic Diseases: A Retrospective Cohort Study

Evaluating methodological approaches to assess the severity of infection with SARS-CoV-2 variants: scoping review and applications on Belgian COVID-19 data

Contact Info

Product

Resources

About