Mortality of critically ill patients with severe influenza starting four years after the 2009 pandemic

Vandroux, David; Allyn, Jérôme; Ferdynus, Cyril; Gaüzère, Bernard-Alex; Kerambrun, Hugo; Galas, Thomas; Allou, Nicolas; Persichini, Romain; Martinet, Olivier; Jabot, Julien

doi:10.1080/23744235.2019.1668957

Cited by 17 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients were stratified into three clinical groups based the WHO interim guidance [20,21]: group A, mild cases; group B, severe cases; and group C, critical cases. Group A involves patients with mild clinical symptoms in the form of fever, mild respiratory tract manifestations, and positive CT findings of pneumonia.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

Gouda

Yasin

2020

Egypt J Radiol Nucl Med

View full text Add to dashboard Cite

Background Since the beginning of 2020, coronavirus disease has spread widely all over the world and this required rapid adequate management; therefore, continuous searching for rapid and sensitive CT chest techniques was needed to give a hand for the clinician. We aimed to assess the validity of computed tomography (CT) quantitative and qualitative analysis in COVID-19 pneumonia and how it can predict the disease severity on admission. Results One hundred and twenty patients were enrolled in our study, 98 (81.7%) of them were males, and 22 (18.3%) of them were females with a mean age of 52.63 ± 12.79 years old, ranging from 28 to 83 years. Groups B and C showed significantly increased number of involved lung segments and lobes, frequencies of consolidation, crazy-paving pattern, and air bronchogram. The total lung severity score and the total score for crazy-paving and consolidation are used as severity indicators in the qualitative method and could differentiate between groups B and C and group A (90.9% sensitivity, 87.5% specificity, and 93.2% sensitivity, 87.5% specificity, respectively), while the quantitative indicators could differentiate these three groups. Using the quantitative CT indicators, the validity to differentiate different groups showed 84.1% sensitivity and 81.2% specificity for the opacity score, and 90.9% sensitivity and 81.2% specificity for the percentage of high opacity. Conclusion Advances in CT COVID-19 pneumonia assessment provide an accurate and rapid tool for severity assessment, helping for decision-making notably for the critical cases.

show abstract

Section: Methodsmentioning

confidence: 99%

COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

Gouda

Yasin

2020

Egypt J Radiol Nucl Med

View full text Add to dashboard Cite

show abstract

“…Severe cases are defined as those who had a respiratory rate ≧ 30 times per minute, or oxygen saturation ≦ 93% at rest, or arterial oxygen partial pressure(PaO2)/inspired oxygen (FiO2) ≦ 300mmHg(1mmHg=0.133kPa), or significant progress in chest CT findings of pneumonia within 24-48 hours≧ 50%. Critical cases are defined as those who are admitted to the intensive care unit for mechanical ventilation or had a FiO2 of at least 60% or more 21,22 . Finally, fifty-one Copyright © 2020 Wolters Kluwer Health, Inc.…”

Section: Study Design and Participatesmentioning

confidence: 99%

The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics

et al. 2020

View full text Add to dashboard Cite

This paper can be cited using the date of access and the unique DOI number which can be found in the footnotes. Objectives: To assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest CT indicators and identify the CT characteristics of critical cases. Materials and Methods: Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A,n=12), severe cases(group B, n=15) and critical cases (group C,n=24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher's exact test, one-way ANOVA, Kruskal-Wallis H test and receiver operating characteristic analysis. Results: Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A(69% sensitivity, 83% specificity and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these three groups with high sensitivity(B+C vs.A ,90%; C vs. B, 92%),specificity(100%, 87%) and accuracy(92%, 90%). Critical cases had higher total severity score(>10) and higher total score for crazy-paving and consolidation(>4) than ordinary cases, and had higher mean lung density(>-779HU) and full width at half maximum(>128HU) but lower relative volume of normal lung density(≦50%) than ordinary/severe cases. In our critical cases, eight patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and two of them had died.

show abstract

“…We found that overall mortality was similar to previous series. However, in ICU cases, mortality was significantly lower (9% vs. 10 to 31% range), despite the fact that the majority of the cases reported in this study were older than 70 years and had at least one coexisting illness [4][5][6] . Also, the relative amount of patients admitted to ICU was higher.…”

Section: Discussionmentioning

confidence: 51%

Influenza season 2019: analysis of 143 hospitalized cases

Bisso

Prado

Massó

et al. 2020

Preprint

View full text Add to dashboard Cite

Introduction Influenza virus infection is a latent public health problem, affecting millions of people through the planet, and it is an important cause of morbidity and mortality. In Argentina, there is a significant absence of data regarding influenza severe respiratory disease and, therefore, a lack of knowledge about the impact of this disease at health institutions. Objectives Analysis of clinical characteristics, image findings and laboratory variables in patients with influenza viruses during 2019. Methods Retrospective, single-centre study, we analyzed all confirmed cases of influenza in a high complexity hospital from Buenos Aires. Results 143 patients with influenza virus were hospitalized in this period of time. The 98.6% were infected by type A influenza, and most of them 61.5% were H1N1 subtype. Median age was 71 years (IQR 60 - 82), 77.6% were older than 70 years, and 88.1% had at least one coexisting illness. 39.1% of the patients required intensive care, 11.1% invasive mechanical ventilation and 4.1% died during hospitalization. Conclusion Mortality and severity were similar to previous series of non-pandemic influenza. Analysis of annual data would be valuable in order to document the severity of influenza hospitalizations by age‐group and comorbidities according to the circulating influenza viruses.

show abstract

Mortality of critically ill patients with severe influenza starting four years after the 2009 pandemic

Cited by 17 publications

References 25 publications

COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity

The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics

Influenza season 2019: analysis of 143 hospitalized cases

Contact Info

Product

Resources

About