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      Clinical phenotypes and outcomes associated with SARS-CoV-2 Omicron variants BA.2, BA.5 and BQ.1.1 in critically ill patients with COVID-19: a prospective, multicenter cohort study

      research-article
      1 , 2 , 3 , , 3 , 4 , 5 , 6 , 6 , 7 , 1 , 2 , 8 , 9 , 2 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 1 , 2 , 3 , 3 , 37 , 38 , 3 , 37 , 38 , 3 , 37 , 38 , the SEVARVIR investigators
      Intensive Care Medicine Experimental
      Springer International Publishing
      SARS-CoV-2, Omicron, Sublineage, COVID-19, Acute respiratory failure

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Despite current broad natural and vaccine-induced protection, a substantial number of patients infected with emerging SARS-CoV-2 variants (e.g., BF.7 and BQ.1.1) still experience severe COVID-19. Real-life studies investigating the impact of these variants on clinical outcomes of severe cases are currently not available. We performed a prospective multicenter observational cohort study. Adult patients with acute respiratory failure admitted between December 7, 2021 and December 15, 2022, in one of the 20 participating intensive care units (17 from the Greater Paris area and 3 from the North of France) were eligible for inclusion if they had SARS-CoV-2 infection confirmed by a positive reverse transcriptase-polymerase chain reaction (RT-PCR). Full-length SARS-CoV-2 genomes from all included patients were sequenced by means of next-generation sequencing. The primary endpoint of the study was day-28 mortality.

          Results

          The study included 158 patients infected with three groups of Omicron sublineages, including (i) BA.2 variants and their early sublineages referred as “BA.2” (n = 50), (ii) early BA.4 and BA.5 sublineages (including BA.5.1 and BA.5.2, n = 61) referred as “BA.4/BA.5”, and (iii) recent emerging BA.5 sublineages (including BQ.1, BQ.1.1, BF.7, BE.1 and CE.1, n = 47) referred as “BQ.1.1”. The clinical phenotype of BQ1.1-infected patients compared to earlier BA.2 and BA.4/BA.5 sublineages, showed more frequent obesity and less frequent immunosuppression. There was no significant difference between Omicron sublineage groups regarding the severity of the disease at ICU admission, need for organ failure support during ICU stay, nor day 28 mortality (21.7%, n = 10/47 in BQ.1.1 group vs 26.7%, n = 16/61 in BA.4/BA.5 vs 22.0%, n = 11/50 in BA.2, p = 0.791). No significant relationship was found between any SARS-CoV-2 substitution and/or deletion on the one hand and survival on the other hand over hospital follow-up.

          Conclusions

          Critically-ill patients with Omicron BQ.1.1 infection showed a different clinical phenotype than other patients infected with earlier Omicron sublineage but no day-28 mortality difference.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40635-023-00536-0.

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          Most cited references27

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          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            Acute respiratory distress syndrome: the Berlin Definition.

            The acute respiratory distress syndrome (ARDS) was defined in 1994 by the American-European Consensus Conference (AECC); since then, issues regarding the reliability and validity of this definition have emerged. Using a consensus process, a panel of experts convened in 2011 (an initiative of the European Society of Intensive Care Medicine endorsed by the American Thoracic Society and the Society of Critical Care Medicine) developed the Berlin Definition, focusing on feasibility, reliability, validity, and objective evaluation of its performance. A draft definition proposed 3 mutually exclusive categories of ARDS based on degree of hypoxemia: mild (200 mm Hg < PaO2/FIO2 ≤ 300 mm Hg), moderate (100 mm Hg < PaO2/FIO2 ≤ 200 mm Hg), and severe (PaO2/FIO2 ≤ 100 mm Hg) and 4 ancillary variables for severe ARDS: radiographic severity, respiratory system compliance (≤40 mL/cm H2O), positive end-expiratory pressure (≥10 cm H2O), and corrected expired volume per minute (≥10 L/min). The draft Berlin Definition was empirically evaluated using patient-level meta-analysis of 4188 patients with ARDS from 4 multicenter clinical data sets and 269 patients with ARDS from 3 single-center data sets containing physiologic information. The 4 ancillary variables did not contribute to the predictive validity of severe ARDS for mortality and were removed from the definition. Using the Berlin Definition, stages of mild, moderate, and severe ARDS were associated with increased mortality (27%; 95% CI, 24%-30%; 32%; 95% CI, 29%-34%; and 45%; 95% CI, 42%-48%, respectively; P < .001) and increased median duration of mechanical ventilation in survivors (5 days; interquartile [IQR], 2-11; 7 days; IQR, 4-14; and 9 days; IQR, 5-17, respectively; P < .001). Compared with the AECC definition, the final Berlin Definition had better predictive validity for mortality, with an area under the receiver operating curve of 0.577 (95% CI, 0.561-0.593) vs 0.536 (95% CI, 0.520-0.553; P < .001). This updated and revised Berlin Definition for ARDS addresses a number of the limitations of the AECC definition. The approach of combining consensus discussions with empirical evaluation may serve as a model to create more accurate, evidence-based, critical illness syndrome definitions and to better inform clinical care, research, and health services planning.
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              • Article: not found

              A global clinical measure of fitness and frailty in elderly people.

              There is no single generally accepted clinical definition of frailty. Previously developed tools to assess frailty that have been shown to be predictive of death or need for entry into an institutional facility have not gained acceptance among practising clinicians. We aimed to develop a tool that would be both predictive and easy to use. We developed the 7-point Clinical Frailty Scale and applied it and other established tools that measure frailty to 2305 elderly patients who participated in the second stage of the Canadian Study of Health and Aging (CSHA). We followed this cohort prospectively; after 5 years, we determined the ability of the Clinical Frailty Scale to predict death or need for institutional care, and correlated the results with those obtained from other established tools. The CSHA Clinical Frailty Scale was highly correlated (r = 0.80) with the Frailty Index. Each 1-category increment of our scale significantly increased the medium-term risks of death (21.2% within about 70 mo, 95% confidence interval [CI] 12.5%-30.6%) and entry into an institution (23.9%, 95% CI 8.8%-41.2%) in multivariable models that adjusted for age, sex and education. Analyses of receiver operating characteristic curves showed that our Clinical Frailty Scale performed better than measures of cognition, function or comorbidity in assessing risk for death (area under the curve 0.77 for 18-month and 0.70 for 70-month mortality). Frailty is a valid and clinically important construct that is recognizable by physicians. Clinical judgments about frailty can yield useful predictive information.
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                Author and article information

                Contributors
                nicolas.de-prost@aphp.fr
                Journal
                Intensive Care Med Exp
                Intensive Care Med Exp
                Intensive Care Medicine Experimental
                Springer International Publishing (Cham )
                2197-425X
                7 August 2023
                7 August 2023
                December 2023
                : 11
                : 48
                Affiliations
                [1 ]GRID grid.412116.1, ISNI 0000 0004 1799 3934, Service de Médecine Intensive Réanimation, , Hôpitaux Universitaires Henri Mondor, Assistance Publique, Hôpitaux de Paris (AP-HP), ; Créteil, France
                [2 ]GRID grid.410511.0, ISNI 0000 0001 2149 7878, Groupe de Recherche Clinique CARMAS, , Université Paris-Est-Créteil (UPEC), ; Créteil, France
                [3 ]GRID grid.410511.0, ISNI 0000 0001 2149 7878, Université Paris-Est-Créteil (UPEC), ; Créteil, France
                [4 ]GRID grid.412116.1, ISNI 0000 0004 1799 3934, Department of Public Health, , Hôpitaux Universitaires Henri Mondor, Assistance Publique, Hôpitaux de Paris (AP-HP), ; Créteil, France
                [5 ]GRID grid.462410.5, ISNI 0000 0004 0386 3258, IMRB INSERM U955, Team CEpiA, ; Créteil, France
                [6 ]GRID grid.410463.4, ISNI 0000 0004 0471 8845, U1167, RID-AGE Facteurs de Risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, University Lille, Inserm, CHU Lille, Institut Pasteur de Lille, ; 59000 Lille, France
                [7 ]GRID grid.410463.4, ISNI 0000 0004 0471 8845, Service de Virologie, , CHU de Lille, ; 59000 Lille, France
                [8 ]GRID grid.414291.b, Médecine Intensive Réanimation, , Hôpital Raymond Poincaré, Assistance Publique, Hôpitaux de Paris (AP-HP), ; Garches, France
                [9 ]GRID grid.413756.2, ISNI 0000 0000 9982 5352, Laboratoire de Virologie, , Hôpital Ambroise Paré, Assistance Publique, Hôpitaux de Paris (AP-HP), ; Boulogne, France
                [10 ]GRID grid.413784.d, ISNI 0000 0001 2181 7253, Service de Médecine Intensive-Réanimation, , Assistance Publique, Hôpitaux de Paris, Hôpital de Bicêtre, DMU 4 CORREVE Maladies du Cœur et Des Vaisseaux, FHU Sepsis, ; Le Kremlin-Bicêtre, France
                [11 ]GRID grid.463845.8, ISNI 0000 0004 0638 6872, Inserm U1018, Equipe d’Epidémiologie Respiratoire Intégrative, CESP, ; 94807 Villejuif, France
                [12 ]GRID grid.413133.7, ISNI 0000 0001 0206 8146, Laboratoire de Virologie, , Hôpital Paul Brousse, Assistance Publique, Hôpitaux de Paris, ; Villejuif, France
                [13 ]Sorbonne Université, Centre de Recherche Saint-Antoine INSERM, Médecine Intensive Réanimation, Hôpital Tenon, Assistance Publique, Hôpitaux de Paris, Paris, France
                [14 ]GRID grid.7429.8, ISNI 0000000121866389, Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, ; Paris, France
                [15 ]GRID grid.412370.3, ISNI 0000 0004 1937 1100, Laboratoire de Virologie, , Hôpital Saint-Antoine, Assistance Publique, Hôpitaux de Paris, ; 75012 Paris, France
                [16 ]Service de Réanimation Polyvalente, Hôpital Marc Jacquet, Melun, France
                [17 ]Laboratoire de Microbiologie, Hôpital Marc Jacquet, Melun, France
                [18 ]GRID grid.413756.2, ISNI 0000 0000 9982 5352, Service de Réanimation Médico-Chirurgicale, , Assistance Publique, Hôpitaux de Paris, Hôpital Ambroise Paré, ; Boulogne, France
                [19 ]GRID grid.414474.6, ISNI 0000 0004 0639 3263, Service de Réanimation, , Hôpital Victor Dupouy, ; Argenteuil, France
                [20 ]GRID grid.414474.6, ISNI 0000 0004 0639 3263, Service de Virologie, , Hôpital Victor Dupouy, ; Argenteuil, France
                [21 ]GRID grid.413328.f, ISNI 0000 0001 2300 6614, Médecine Intensive Réanimation, , Hôpital Saint-Louis, Assistance Publique, Hôpitaux de Paris, ; Paris, France
                [22 ]Université de Paris, Inserm HIPI, 75010 Paris, France
                [23 ]GRID grid.413328.f, ISNI 0000 0001 2300 6614, Laboratoire de Virologie, , Hôpital Saint-Louis, Assistance Publique, Hôpitaux de Paris, ; 75010 Paris, France
                [24 ]GRID grid.414205.6, ISNI 0000 0001 0273 556X, n, , Université de Paris, APHP, Hôpital Louis Mourier, DMU ESPRIT, Service de Médecine Intensive Réanimatio, ; Colombes, France
                [25 ]GRID grid.7429.8, ISNI 0000000121866389, INSERM U1151, CNRS UMR 8253, Institut Necker-Enfants Malades (INEM), Department of Immunology, Infectiology and Hematology, ; Paris, France
                [26 ]Université de Paris, IAME INSERM UMR 1137, Service de Virologie, Hôpital Bichat-Claude Bernard, Assistance Publique, Hôpitaux de Paris, Paris, France
                [27 ]GRID grid.511882.7, ISNI 0000 0000 9390 6979, Service de Réanimation, , Hôpital Saint-Camille, ; Bry-Sur-Marne, France
                [28 ]GRID grid.511882.7, ISNI 0000 0000 9390 6979, Laboratoire de Biologie, , Hôpital Saint-Camille, ; Bry-Sur-Marne, France
                [29 ]GRID grid.462844.8, ISNI 0000 0001 2308 1657, Sorbonne Université, Assistance Publique, Hôpitaux de Paris, Hôpital Pitié–Salpêtrière, Médecine Intensive Réanimation, ; Paris, France
                [30 ]GRID grid.477396.8, ISNI 0000 0004 3982 4357, INSERM UMRS_1166-iCAN, Institute of Cardiometabolism and Nutrition, ; Paris, France
                [31 ]GRID grid.411439.a, ISNI 0000 0001 2150 9058, Département de Virologie, , Hôpital Pitié–Salpêtrière, Assistance Publique-Hôpitaux de Paris (AP-HP), ; Paris, France
                [32 ]GRID grid.411784.f, ISNI 0000 0001 0274 3893, Médecine Intensive Réanimation, , Hôpital Cochin, Assistance Publique, Hôpitaux de Paris, ; Paris, France
                [33 ]GRID grid.411784.f, ISNI 0000 0001 0274 3893, Laboratoire de Virologie, , Hôpital Cochin, Assistance Publique, Hôpitaux de Paris, ; Paris, France
                [34 ]GRID grid.413780.9, ISNI 0000 0000 8715 2621, Service de Réanimation, , Hôpital Avicenne, Assistance Publique, Hôpitaux de Paris, ; Bobigny, France
                [35 ]GRID grid.413780.9, ISNI 0000 0000 8715 2621, Laboratoire de Virologie, , Hôpital Avicenne, Assistance Publique, Hôpitaux de Paris, ; Bobigny, France
                [36 ]GRID grid.412116.1, ISNI 0000 0004 1799 3934, Département d’Anesthésie Réanimations Chirurgicales, , Hôpitaux Universitaires Henri Mondor, Assistance Publique, Hôpitaux de Paris (AP-HP), ; Créteil, France
                [37 ]GRID grid.412116.1, ISNI 0000 0004 1799 3934, Department of Virology, , Hôpitaux Universitaires Henri Mondor, Assistance Publique, Hôpitaux de Paris, ; Créteil, France
                [38 ]GRID grid.462410.5, ISNI 0000 0004 0386 3258, INSERM U955, Team “Viruses, Hepatology, Cancer”, ; Créteil, France
                Author information
                http://orcid.org/0000-0002-4833-4320
                Article
                536
                10.1186/s40635-023-00536-0
                10404579
                37544942
                3fe8939d-2f69-489f-ae28-b3fc11b5ca63
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 May 2023
                : 10 July 2023
                Funding
                Funded by: EMERGEN consortium - ANRS Maladies Infectieuses Emergentes
                Award ID: ANRS0153
                Award ID: ANRS0153
                Award Recipient :
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                © European Society of Intensive Care Medicine and Springer Nature Switzerland AG 2023

                sars-cov-2,omicron,sublineage,covid-19,acute respiratory failure

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