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• W3111159637 abstract "BackgroundSevere acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection causes direct lung damage, overwhelming endothelial activation, and inflammatory reaction, leading to acute respiratory failure and multi-organ dysfunction. Ongoing clinical trials are evaluating targeted therapies to hinder this exaggerated inflammatory response. Critically ill coronavirus disease 2019 (COVID-19) patients have shown heterogeneous severity trajectories, suggesting that response to therapies is likely to vary across patients.Research QuestionAre critically ill COVID-19 patients biologically and immunologically dissociable based on profiling of currently evaluated therapeutic targets?Study Design and MethodsWe did a single-center, prospective study in an ICU department in France. Ninety-six critically ill adult patients admitted with a documented SARS-CoV-2 infection were enrolled. We conducted principal components analysis and hierarchical clustering on a vast array of immunologic variables measured on the day of ICU admission.ResultsWe found that patients were distributed in three clusters bearing distinct immunologic features and associated with different ICU outcomes. Cluster 1 had a “humoral immunodeficiency” phenotype with predominant B-lymphocyte defect, relative hypogammaglobulinemia, and moderate inflammation. Cluster 2 had a “hyperinflammatory” phenotype, with high cytokine levels (IL-6, IL-1β, IL-8, tumor necrosis factor-alpha [TNF⍺]) associated with CD4+ and CD8+ T-lymphocyte defects. Cluster 3 had a “complement-dependent” phenotype with terminal complement activation markers (elevated C3 and sC5b-9).InterpretationPatients with severe COVID-19 exhibiting cytokine release marks, complement activation, or B-lymphocyte defects are distinct from each other. Such immunologic variability argues in favor of targeting different mediators in different groups of patients and could serve as a basis for patient identification and clinical trial eligibility. Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection causes direct lung damage, overwhelming endothelial activation, and inflammatory reaction, leading to acute respiratory failure and multi-organ dysfunction. Ongoing clinical trials are evaluating targeted therapies to hinder this exaggerated inflammatory response. Critically ill coronavirus disease 2019 (COVID-19) patients have shown heterogeneous severity trajectories, suggesting that response to therapies is likely to vary across patients. Are critically ill COVID-19 patients biologically and immunologically dissociable based on profiling of currently evaluated therapeutic targets? We did a single-center, prospective study in an ICU department in France. Ninety-six critically ill adult patients admitted with a documented SARS-CoV-2 infection were enrolled. We conducted principal components analysis and hierarchical clustering on a vast array of immunologic variables measured on the day of ICU admission. We found that patients were distributed in three clusters bearing distinct immunologic features and associated with different ICU outcomes. Cluster 1 had a “humoral immunodeficiency” phenotype with predominant B-lymphocyte defect, relative hypogammaglobulinemia, and moderate inflammation. Cluster 2 had a “hyperinflammatory” phenotype, with high cytokine levels (IL-6, IL-1β, IL-8, tumor necrosis factor-alpha [TNF⍺]) associated with CD4+ and CD8+ T-lymphocyte defects. Cluster 3 had a “complement-dependent” phenotype with terminal complement activation markers (elevated C3 and sC5b-9). Patients with severe COVID-19 exhibiting cytokine release marks, complement activation, or B-lymphocyte defects are distinct from each other. Such immunologic variability argues in favor of targeting different mediators in different groups of patients and could serve as a basis for patient identification and clinical trial eligibility. FOR EDITORIAL COMMENT, SEE PAGE 1706Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is an emerging pathogen, which originated in late 2019 in China and is responsible for a form of severe viral pneumonia or coronavirus disease 2019 (COVID-19).1Li Q. Guan X. Wu P. et al.Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia.N Engl J Med. 2020; 382: 1199-1207Crossref PubMed Scopus (10229) Google Scholar, 2Wang D. Hu B. Hu C. et al.Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China.JAMA. 2020; 323: 1061-1069Crossref PubMed Scopus (15450) Google Scholar, 3Guan W. Ni Z. Hu Y. et al.Clinical characteristics of coronavirus disease 2019 in China.N Engl J Med. 2020; 382: 1708-1720Crossref PubMed Scopus (19386) Google Scholar, 4Grasselli G. Zangrillo A. Zanella A. et al.Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy.JAMA. 2020; 323: 1574-1581Crossref PubMed Scopus (3648) Google Scholar In early 2020, this disease evolved in a worldwide pandemic, and it constitutes a universal challenge for health-care settings.5Griffin K.M. Karas M.G. Ivascu N.S. Lief L. Hospital preparedness for COVID-19: a practical guide from a critical care perspective.Am J Respir Crit Care Med. 2020; 201: 1337-1344Crossref PubMed Scopus (187) Google Scholar,6Phua J. Weng L. Ling L. et al.Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations.Lancet Respir Med. 2020; 8: 506-517Abstract Full Text Full Text PDF PubMed Scopus (988) Google Scholar From a clinical standpoint, distinct patients trajectories have emerged, with different disease progression courses as well as different disease stages.7Siddiqi H.K. Mehra M.R. COVID-19 illness in native and immunosuppressed states: a clinical–therapeutic staging proposal.J Heart Lung Transplant. 2020; 39: 405-407Abstract Full Text Full Text PDF PubMed Scopus (1133) Google Scholar In this regard, several clinical parameters have been described as potential risk factors for severe forms of COVID-19, namely age, clinical frailty, or preexisting comorbidities such as immunosuppression.2Wang D. Hu B. Hu C. et al.Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China.JAMA. 2020; 323: 1061-1069Crossref PubMed Scopus (15450) Google Scholar,3Guan W. Ni Z. Hu Y. et al.Clinical characteristics of coronavirus disease 2019 in China.N Engl J Med. 2020; 382: 1708-1720Crossref PubMed Scopus (19386) Google Scholar,8Ruan Q. Yang K. Wang W. Jiang L. Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China.Intensive Care Med. 2020; 46: 846-848Crossref PubMed Scopus (3218) Google Scholar SARS-CoV-2-related lung injury results from the interplay between direct viral damage to the alveolar epithelial cell and excessive endothelial activation.9Tay M.Z. Poh C.M. Rénia L. MacAry P.A. Ng L.F.P. The trinity of COVID-19: immunity, inflammation and intervention.Nat Rev Immunol. 2020; 20: 363-374Crossref PubMed Scopus (2730) Google Scholar Both insults lead to the exaggerated cytokine production that is responsible for the most severe respiratory cases. This inflammation is composed of many overlapping signaling pathways, mediated by multiple key players, chiefly interleukins and the complement system.9Tay M.Z. Poh C.M. Rénia L. MacAry P.A. Ng L.F.P. The trinity of COVID-19: immunity, inflammation and intervention.Nat Rev Immunol. 2020; 20: 363-374Crossref PubMed Scopus (2730) Google Scholar,10Risitano A.M. Mastellos D.C. Huber-Lang M. et al.Complement as a target in COVID-19?.Nat Rev Immunol. 2020; 20: 343-344Crossref PubMed Scopus (371) Google Scholar Early evidence pointed to IL-6 and IL-1β as pivotal biomarkers of disease severity.11Zhang S. Li L. Shen A. Chen Y. Qi Z. Rational use of tocilizumab in the treatment of novel coronavirus pneumonia.Clin Drug Investig. 2020; 40: 511-518Crossref PubMed Scopus (135) Google Scholar,12Huang C. Wang Y. Li X. et al.Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.The Lancet. 2020; 395: 497-506Abstract Full Text Full Text PDF PubMed Scopus (31105) Google Scholar The complement system proteins act as key mediators of the innate immune response and are present in the pulmonary alveolar epithelium,13Wang R. Xiao H. Guo R. Li Y. Shen B. The role of C5a in acute lung injury induced by highly pathogenic viral infections.Emerg Microbes Infect. 2015; 4: 1-7Google Scholar,14Guo R.-F. Ward P.A. Role of C5a in inflammatory responses.Annu Rev Immunol. 2005; 23: 821-852Crossref PubMed Scopus (777) Google Scholar with some evidence of deposits of terminal complex components C5b-9 in the lung microvasculature in COVID-19 patients.15Magro C. Mulvey J.J. Berlin D. et al.Complement associated microvascular injury and thrombosis in the pathogenesis of severe COVID-19 infection: a report of five cases.Transl Res. 2020; 220: 1-13Abstract Full Text Full Text PDF PubMed Scopus (1555) Google Scholar Additionally, the anaphylatoxin C5a has been involved in pulmonary endothelium damage in ARDS, and murine models of induced Middle East respiratory syndrome-coronavirus FOR EDITORIAL COMMENT, SEE PAGE 1706 and SARS-CoV-1.13Wang R. Xiao H. Guo R. Li Y. Shen B. The role of C5a in acute lung injury induced by highly pathogenic viral infections.Emerg Microbes Infect. 2015; 4: 1-7Google Scholar,16Garcia C.C. Weston-Davies W. Russo R.C. et al.Complement C5 activation during influenza A infection in mice contributes to neutrophil recruitment and lung injury.PLoS One. 2013; 8e64443Crossref PubMed Scopus (77) Google Scholar Therefore, early trials have not only evaluated antiviral17Cao B. Wang Y. Wen D. et al.A trial of lopinavir–ritonavir in adults hospitalized with severe Covid-19.N Engl J Med. 2020; 382: 1787-1799Crossref PubMed Scopus (3751) Google Scholar,18Grein J. Ohmagari N. Shin D. et al.Compassionate use of remdesivir for patients with severe Covid-19.N Engl J Med. 2020; 382: 2327-2336Crossref PubMed Scopus (1956) Google Scholar efficacy but rapidly sought to evaluate the impact of targeted antiinflammatory therapies,19Radbel J. Narayanan N. Bhatt P.J. Use of tocilizumab for COVID-19-induced cytokine release syndrome.Chest. 2020; 158: e15-e19Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar,20Sciascia S. Aprà F. Baffa A. et al.Pilot prospective open, single-arm multicentre study on off-label use of tocilizumab in patients with severe COVID-19.Clin Exp Rheumatol. 2020; 38: 529-532PubMed Google Scholar namely, C5a,21Diurno F. Numis F.G. Porta G. et al.Eculizumab treatment in patients with COVID-19: preliminary results from real life ASL Napoli 2 Nord experience.Eur Rev Med Pharmacol Sci. 2020; 24: 4040-4047PubMed Google Scholar IL-6,22Xu X. Han M. Li T. et al.Effective treatment of severe COVID-19 patients with tocilizumab.Proc Natl Acad Sci U S A. 2020; 117: 10970-10975Crossref PubMed Scopus (1736) Google Scholar and IL-1 inhibitors, or other immunomodulatory therapies such as steroids, hydroxychloroquine, IV immunoglobulins, or convalescent plasma,23Zeng Q.-L. Yu Z.-J. Gou J.-J. et al.Effect of convalescent plasma therapy on viral shedding and survival in COVID-19 patients.J Infect Dis. 2020; 222: 38-43Crossref PubMed Scopus (228) Google Scholar to alleviate the inflammatory reaction, thereby avoiding the need for mechanical ventilation and case fatality. However, one could suppose that eligibility for these specific therapies may not be identical for all patients. Therefore, we hypothesized that critically ill COVID-19 patients with established clinical severity could be biologically and immunologically dissociable, and we sought to characterize this heterogeneity at baseline (ICU admission), with a particular focus on therapeutic targets currently being evaluated in ongoing clinical trials. This study was approved by the Ethics Committee of the French Intensive Care Society (FICS; CE SRLF n°20-32). Between March 1 and April 30, 2020, all consecutive adult patients referred for severe SARS-CoV-2 infection (defined as the need for oxygen > 9 L/min to achieve oxygen saturation levels of Spo2 > 94%, or the need for high-flow nasal oxygen or mechanical ventilation on the first day of ICU stay) were prospectively included on admission to the medical ICU of the Saint-Louis Hospital, Paris, France. Laboratory confirmation for SARS-Cov-2 was defined as a positive result of real-time reverse transcriptase–polymerase chain reaction assay of nasopharyngeal or rectal swabs. Data were collected by local investigators, using electronic case report forms, then centralized and anonymized. We collected epidemiologic, demographic, medical history, biologic, and immunologic data on the day of ICU admission. We collected routine blood examinations at ICU admission, including blood count, coagulation profile, and serum biochemical tests. Blood samples at admission were also collected for each patient for subsequent biomarkers measurements. Serum IL-6, IL-1β, tumor necrosis factor alpha (TNF⍺), and IL-8 were analyzed according to the manufacturer’s instructions (Ella, ProteinSimple). Concomitant with clinical and biological data, circulating levels of C3 and soluble C5b-9 were determined according to the instructions of the manufacturer (Siemens and Quidel). Continuous variables are described as median and interquartile range (IQR) and compared using the Kruskal-Wallis test; categorical variables are summarized by counts (percentages) and compared using Fisher exact test, as appropriate. We performed a hierarchical clustering in a principal component approach (namely, hierarchical clustering on principal components) to identify different phenotypes. First, we performed a principal component analysis (PCA), including a set of biological and immunological data, including D-dimers, a panel of cytokines (serum IL-6, IL-1β, TNF⍺), two complement biomarkers (C3 and soluble C5b-9), gamma-globulin level and lymphocyte counts (natural killer [NK] cells, CD8+, CD4+ T cells, and B cells). These covariables have been selected a priori because of their importance in severe SARS-CoV-2 infection and related potential therapeutic implications. Variables were standardized as they were measured in different units. Then, a hierarchical cluster analysis based on the first four dimensions of the PCA was used to determine subgroups of patients according to these characteristics. The clustering of patients was performed using Euclidean distance and the Ward agglomerative method. Missing data were imputed using iterative PCA. Briefly, we estimated the number of dimensions to use in the reconstruction formula, and then missing values were predicted using an iterative PCA algorithm.24Josse J. Pagès J. Husson F. Multiple imputation in principal component analysis.Adv Data Anal Classif. 2011; 5: 231-246Crossref Scopus (81) Google Scholar Details about the method used are available in the online data supplement (e-Figs 1-4, e-Table 1). All tests were two-sided, and P < 5% was considered to indicate significant associations. Analyses were performed using R statistical platform, version 3.0.2 (https://cran.r-project.org/), using packages FactomineR and missMDA. During the study period, 96 patients were admitted to the ICU, and all were included in the current study. Median age was 58 years (IQR [53-67]), and most patients were male (n = 69, 72%). Main comorbidities included hypertension (n = 52; 55%), diabetes mellitus (n = 28; 30%), and cardiovascular disease (n = 11; 12%). Most common past medications included antiplatelet therapy (n = 21; 22%), statins (n = 23; 24%), or antihypertensive drugs, mainly angiotensin-converting-enzyme inhibitors (n = 11; 12%) or angiotensin II receptor blockers medication (n = 16; 17%) (Table 1). Twenty-six patients had a history of cancer or solid organ transplantation. Among them, 16 patients were still receiving immunosuppressive therapy on admission (eg, chemotherapy, immunotherapy, glucocorticoids, or other immunosuppressive treatment; Table 2).Table 1Overall Population and Cluster CharacteristicsOverall (N = 96)Cluster 1 (n = 34)Cluster 2 (n = 20)Cluster 3 (n = 42)PDemographicsAge, y58 [53-67]57 [50-67]61 [55-67]60 [53-66].638BMI28 [23-31]25 [23-31]27 [26-31]28 [25-31].268Male69 (72)25 (74)18 (90)26 (62).068Underlying conditionsHypertension52 (55)16 (49)12 (60)24 (57).693Diabetes mellitus28 (30)10 (30)5 (25)13 (31).916Cardiovascular disease11 (12)5 (15)3 (15)3 (7).499Solid organ transplant10 (10.4)6 (17.6)3 (15.0)1 (2.4).048Past history of malignancy16 (16.8)12 (35.3)2 (10.0)2 (4.9).002Active malignancyaChemotherapy during the last 6 months.6 (6.2)6 (17.6)0 (0)0 (0)<.005Clinical characteristicsSAPS-II score28 [21-39]29 [22-37]36 [25-49]25 [18-34].04SOFA score4 [2-7]5 [2.25-8]5.5 [2.75-8.25]2 [2-6].019Respiratory2 [2-3]2 [2-3]2 [2-4]2 [1.25-3].221Hemodynamic0 [0-3]0 [0-3]3 [0-3]0 [0-3].131Renal0 [0-1]0 [0-1]0.5 [0-1.25]0 [0-0].042Liver0 [0-0]0 [0-0]0 [0-0]0 [0-0]NSNeurologic0 [0-0]0 [0-0]0 [0-0]0 [0-0]NSCoagulation0 [0-0]0 [0-1]0 [0, 0]0 [0, 0].009Time from symptom onset, days8 [6-12]6 [3-12]7 [4-8]9 [5-12].12Oxygen flow on arrival, L/min9 [6-12]9 [6-10]12 [6-15]9 [6-12].292RR, breaths/min28 [23-34]29 [20-30]30 [25-35]28 [24-34].483Oxygenation strategies on day 1Standard oxygen alone51 (53.1)18 (52.9)8 (40.0)25 (59.5).36HFNC30 (31.2)12 (35.3)6 (30.0)12 (28.6).846Mechanical ventilation15 (15.6)4 (11.8)6 (30.0)5 (11.9).173Biological markersLDH, U/L809 [634-908]696 [558-842]870 [799-935]850 [710-903].036D-dimers, μg/L1,360 [780-2,840]1,230 [730-1,960]1,780 [955-3,155]1,360 [820-2,740].646CRP, mg/L181 [84-261]132 [77-226]263 [133-322]179 [94-238].075Ferritin, μg/L1,272 [636-2,234]1,238 [523-2,272]1,658 [1,183-2,099]1,045 [641-1,644].128Cytokine releaseTNF⍺, pg/mL22.7 [18.7-28.0]18.2 [14.4-23.8]29.7 [23.4-36.7]22.2 [19.2-26.5]<.001IL-1β, pg/mL0.44 [0.32-0.86]0.44 [0.32-0.59]1.01 [0.75-1.27]0.36 [0.32-0.52]<.001IL-6, pg/mL74 [41-137]89 [54-147]135 [79-220]46.7 [29.7-76.5]<.001IL-8, pg/mL50 [31-78]44 [28-59]57 [47-90]42 [31-74].048Lymphocytes typingTotal lymphocytes, cells/mm3790 [580-1,170]710 [550-960]750 [340-1,240]960 [720-1,380].023T lymphocytes, cells/mm3539 [343-764]567 [370-752]344 [244-525]637 [392-799].087CD8+ T lymphocytes, cells/mm3182 [114-269]235 [170-356]101 [67-201]177 [134-238].027CD4+ T lymphocytes, cells/mm3332 [184-464]322 [162-395]186 [168-375]413 [239-539].016NK cells, cells/mm3106 [76-153]93 [57-117]139 [94-299]103 [73-156].011B lymphocytes, cells/mm3104 [54-184]49 [14-82]100 [65-130]183 [143-283]<.001Gamma globulins, g/L9.1 [7.4-11.5]7.7 [6.9-8.9]8.8 [7.1-12.3]10.9 [9.1-12.0]<.001HLA-DR/monocyte, count8,631 [6,828-13,962]7,712 [5,939-11,668]7,852 [6,701-10,265]1,1073 [8,533-16,559].144Complement pathwayC3, mg/L1,305 [1,173-1,550]1,260 [1,150-1,540]1,230 [1,160-1,340]1,445 [1,243-1,630].072sC5b-9, ng/mL373 [270-471]292 [217-449]368 [330-442]392 [357-492].034SC5b-9 > 360, ng/mL43 (56)10 (35)9 (56)24 (75).006ICU outcomeTime of follow-up, days15 [7-20.25]17 [12-21]13 [7-19]15 [7-19].609Mechanical ventilation53 (55)18 (53)15 (75)20 (48).123Noninvasive ventilation (NIV)4 (4.2)0 (0.0)1 (5.0)3 (7.1).338ECMO4 (4.3)1 (2.9)1 (5.3)2 (4.9)NSAKI in ICU42 (44)12 (35)15 (75)15 (36).007Renal replacement therapy13 (13.5)3 (8.8)5 (25.0)5 (11.9).269Venous thromboembolic events13 (13.5)3 (8.8)2 (10.0)8 (19.5).4In-ICU mortality29 (31)11 (32.4)11 (55)7 (17.5).015Values are given in No. (%) or median [interquartile range (IQR)]. Univariate analysis according to cluster status was done using Fisher exact test for categorical variables, and Kruskal-Wallis test for continuous nonnormal variables. AKI was defined using the Kidney Disease Improving Global Outcome (KDIGO) classification. Mechanical ventilation status was defined as any requirement for mechanical ventilation during ICU stay. AKI = acute kidney injury; CRP = C-reactive protein; ECMO = extracorporeal membrane oxygenation; HFNC = high-flow nasal canula; LDH = lactate dehydrogenase; NIV = noninvasive ventilation; NK = natural killer; RR = respiratory rate; RRT = renal replacement therapy; SAPS-II = simplified acute physiology score (SAPS II); sC5b-9 = soluble membrane attack complex; SOFA = Sequential Organ Failure Assessment; TNF-⍺ = tumor necrosis factor-alpha.a Chemotherapy during the last 6 months. Open table in a new tab Table 2Demographic, Clinical, and Biological Characteristics of Patients According to Immunologic Status Before ICU AdmissionOverall (N = 96)Immunocompetent (n = 80)Immunocompromised (n = 16)PSolid organ transplant10 (10.4)…10 (10.4)… Kidney9 (9.4)…9 (9.4)… Heart1 (1.0)…1 (1.0)… None86 (89.6)…86 (89.6)…Active malignancy6 (6.3)…6 (6.3)…Lymphoproliferative3 (3.1)…3 (3.1)…Myeloma3 (3.1)…3 (3.1)…Immunomodulatory treatments…Corticosteroids12 (12.5)…12 (12.5)…Daratumumab1 (1.1)…1 (1.1)…Rituximab/obinituzumab3 (3.1)…3(3.1)…Ixazomib1 (1.1)…1 (1.1)…Belatacept2 (2.1)…2 (2.1)…Cyclosporin6 (6.3)…6 (6.3)…Tacrolimus1 (1.1)…1 (1.1)…Biological markersD-dimers, μg/L1,360 [780-2,840]1,310 [770-2,790]1,500 [953-2,948].608Ferritin, μg/L1,272 [636-2,234]1,182 [626-1,971]1,645 [1,230-2,272].213CRP, mg/L181 [84-261]181 [84-251]201 [103-272].489Cytokine releaseTNF⍺, pg/mL22.7 [18.7-28]22.7 [18-29]22 [19.5-26].682IL-1β, pg/mL0.44 [0.32-0.86]0.44 [0.33-0.87]0.54 [0.32-0.84].746IL-6, pg/mL74 [41-137]76 [41-143]51 [33-81].225IL-8, pg/mL50 [31-78]49 [30-76]52 [41-57].866Lymphocytes typingTotal lymphocytes, cells/mm3790 [580-1,170]890 [690-1,340]560 [320-690]<.001T lymphocytes, cells/mm3539 [343-764]593 [357-816]410 [271-468].012CD8+ T lymphocytes, cells/mm3182 [114-269]180 [113-262]203 [118-280].804CD4+ T lymphocytes, cells/mm3332 [184-464]375 [196-491]168 [69-257].001NK cells, cells/mm3106 [76-153]111 [88-170]67 [37-116].024B lymphocytes, cells/mm3104 [54-184]125 [71-197]14 [11-33]<.001Gamma globulins, g/L9.1 [7.4-11.5]10 [8.4-11.7]7.2 [4.5-7.5]<.001HLA-DR/monocyte, count8,631 [6,828-13,962]8,631 [7,224-13,116]8,327 [4,558-13,608].662Complement pathwayC3, mg/L1,305 [1,173-1,550]1,340 [1,180-1,565]1,240 [1,148-1,370].151sC5b-9, ng/mL373 [270-471]381 [286-491]318 [213-443].175Patients with immunocompromised status included patients with solid organ transplant and active malignancy, with immunomodulatory treatments. AKI = acute kidney injury; ECMO = extracorporeal membrane oxygenation; HFNC = high-flow nasal canula; IQR = interquartile range; NK = natural killer; RR = respiratory rate; RRT = renal replacement therapy; SAPS-II: simplified acute physiology score (SAPS II); sC5b-9 = soluble membrane attack complex;SOFA = sequential organ failure assessment; TNF-⍺ = tumor necrosis factor-alpha Open table in a new tab Values are given in No. (%) or median [interquartile range (IQR)]. Univariate analysis according to cluster status was done using Fisher exact test for categorical variables, and Kruskal-Wallis test for continuous nonnormal variables. AKI was defined using the Kidney Disease Improving Global Outcome (KDIGO) classification. Mechanical ventilation status was defined as any requirement for mechanical ventilation during ICU stay. AKI = acute kidney injury; CRP = C-reactive protein; ECMO = extracorporeal membrane oxygenation; HFNC = high-flow nasal canula; LDH = lactate dehydrogenase; NIV = noninvasive ventilation; NK = natural killer; RR = respiratory rate; RRT = renal replacement therapy; SAPS-II = simplified acute physiology score (SAPS II); sC5b-9 = soluble membrane attack complex; SOFA = Sequential Organ Failure Assessment; TNF-⍺ = tumor necrosis factor-alpha. Patients with immunocompromised status included patients with solid organ transplant and active malignancy, with immunomodulatory treatments. AKI = acute kidney injury; ECMO = extracorporeal membrane oxygenation; HFNC = high-flow nasal canula; IQR = interquartile range; NK = natural killer; RR = respiratory rate; RRT = renal replacement therapy; SAPS-II: simplified acute physiology score (SAPS II); sC5b-9 = soluble membrane attack complex; SOFA = sequential organ failure assessment; TNF-⍺ = tumor necrosis factor-alpha Time from symptom onset to ICU admission was 8 (6-12) days. On admission, mean respiratory rate was 28 (23-34) breaths/min, and median oxygen flow requirement to achieve Sao2 > 94% was 9 (6-12) liters per minute. Sequential Organ Failure Assessment score at admission was 4 (2-7). Most common symptoms were shortness of breath (n = 86; 90%), fever (n = 80; 83%), cough (n = 75; 78%), and fatigue (n = 68; 71%), and myalgias (n = 40; 42%), headaches (n = 13; 14%), and diarrhea (n = 16; 17%) were less frequent. Chest radiographs showed bilateral interstitial pneumonia (median number of quadrants involved on chest radiograph: 4 [3-4]). Laboratory findings on the day of ICU admission are summarized in Table 1. At baseline, the most common abnormalities were elevated inflammation markers, characterized by increased levels of C-reactive protein (179 [83-256] mg/L), and fibrinogen (6.79 [5.76-7.75] g/L). Eighty-two (92%) patients had elevated D-dimers (>500 μg/L). Lymphopenia (<1,500 cells/mm3) was found in 78 (85%) patients, with a median value of 790 (580-1170) cells/mm3Guan W. Ni Z. Hu Y. et al.Clinical characteristics of coronavirus disease 2019 in China.N Engl J Med. 2020; 382: 1708-1720Crossref PubMed Scopus (19386) Google Scholar, affecting CD4+ T cells (332 [184-464] cells/mm3), CD8+ T cells (182 [114-269] cells/mm3), and B lymphocytes (104 [54-184] cells/mm3). We found that the level of C3 was elevated (>1,250 mg/L) in 45 (56%) patients with concomitant elevated levels of the soluble membrane attack complex sC5b-9 in 43 (53%) consistent with an activation of the terminal complement pathway. Increased IL-6 was found in 82 patients (>95%) (74 pg/mL [41, 137]), together with IL-1β in 59 (75%) (0.44 pg/mL [0.32, 0.86]), IL-8 in 76 (>95%) (50 pg/mL [31, 78]), and TNF⍺ in 74 (94%) (22.7 pg/mL [18.7, 28.0]). Analysis in clusters characterized three distinct immunophenotypes (Table 1, Fig 1, e-Fig 5). Thirty-four patients (35%) could be considered as a “humoral response deficiency” phenotype (cluster 1). These patients exhibited profound lymphopenia (710 [550, 960] cells/mm3), mainly on B cells (49 [14, 82] cells/mm3), and NK cells (93 [57, 117] cells/mm3) associated with hypogammaglobulinemia (7.7 g/L [6.9, 8.9]), which contrasted with relatively preserved T-cell count (567 [370, 752]) and moderate cytokine release (IL-1β [0.44 pg/mL (0.32; 0.59)], IL-6 [89 pg/mL (54-147)]) (Table 1). Most immunocompromised patients belonged to this cluster. Twenty patients (21%) had a “hyperinflammatory” phenotype (cluster 2). These patients had very important hallmarks of cytokine release syndrome, with the highest pro-inflammatory cytokine values compared with other clusters (P < .001): IL-1β (1.01 [0.75-1.27] pg/mL), IL-6 (135 [79-220] pg/mL), and TNF⍺ (29.7 [23.4; 36.7] pg/mL) (Table 1). Another main feature seemed to be a T cell lymphocytopenia of both CD4+ (186 [168-374] cells/mm3), and CD8+ (101 [67-201] cells/mm3) lymphocytes. To note, sC5b-9 was discretely elevated in cluster 2 (368 [330; 442] ng/mL). Finally, 42 patients (44%) exhibited a pattern of dependency on the terminal complement pathway with elevated median C3 concentrations (1,445 [1,243-1,630] mg/L), and a significant elevation of the soluble membrane attack complex sC5b-9 (392 ng/mL [357-492]) (P = .034) (Table 1). This cluster 3 could be named the “complement-dependent” phenotype. Figure 2 reports the respective importance of each of them in the partition process. Clinical characteristics and outcomes in the overall population and in each specific cluster are summarized in Table 1. As shown, clusters had similar clinical characteristics, and mortality rates varied from 55% (n = 11) in cluster 2 to 17.5% (n = 7) in cluster 3 (P = .015; Table 1). These results persisted after exclusion of immunocompromised patients (Table 2, e-Fig 6). Using a clustering approach on a vast array of immunologic and biologic variables on the day of ICU admission, our study provides new insights into the immunologic basis of heterogeneity in critically ill COVID-19 patients. This method without any a priori criteria allowed us to characterize, in a cohort of 96 patients, three distinct immunophenotypes, namely the “humoral response deficiency” phenotype (cluster 1), the “hyper-inflammatory” phenotype (cluster 2), and the “complement-dependent” phenotype (cluster 3). Cluster 1 showed a high dependency on B-cell defects, associated hypogammaglobulinemia, and inflammation. Cluster 2 was characterized by the highest cytokine release (increased IL-6, IL-1β, TNF⍺, IL-8) and CD4 and CD8 T-cell defects, and carried the most severe mortality outcome. Finally, cluster 3 showed more discrete inflammation characteristics while having a high dependency on terminal complement activation (suggested by an increase in sC5b-9). Few studies have focused on the immunologic subtypes critically ill COVID-19 patients might display. A recent study25Chen G. Wu D. Guo W. et al.Clinical and immunological features of severe and moderate coronavirus disease 2019.J Clin Invest. 2020; 130: 2620-2629Crossref PubMed Scopus (3169) Google Scholar has found a CD4 and CD8 lymphopenia in most patients, with a small subset of patients showing decreased NK cell levels, and normal or higher B-lymphocyte count. These defects appeared to be markedly more profound in critically ill patients as compared with patients with less severe disease. The complement pathway is also believed to play a pivotal role in the pulmonary lesion resulting from endothelial activation, and to date, data on complement activation in COVID-19 are scarce. A recent report10Risitano A.M. Mastellos D.C. Huber-Lang M. et al.Complement as a target in COVID-19?.Nat Rev Immunol. 2020; 20: 343-344Crossref PubMed Scopus (371) Google Scholar suggested that, given the interplay between complement and inflammation mediators in the endothelial lesion, and the high dependency of the IL-6 cytokine release on C3 in SARS-CoV-1, both interventions targeting the IL-6 receptor and complement might act synergically on SARS-CoV-2. The COVID-19 pandemic has affected millions of individuals, and no current specific treatment has been approved. The two previous outbreaks (SARS-CoV-1, and H1N1) did not provide conclusive data on the use of specific antiviral agents or antiinflammatory agents. Acute respiratory failure results in part from overwhelming inflammation causing extensive pulmonary and multiorgan endothelial lesions, largely described as a hallmark of severe forms. Therefore, the current pandemic has led to large-scale evaluations of many therapeutic antiviral and antiinflammatory agents in ongoing randomized control trials. Recent data provided evaluation of antiviral,17Cao B. Wang Y. Wen D. et al.A trial of lopinavir–ritonavir in adults hospitalized with severe Covid-19.N Engl J Med. 2020; 382: 1787-1799Crossref PubMed Scopus (3751) Google Scholar,18Grein J. Ohmagari N. Shin D. et al.Compassionate use of remdesivir for patients with severe Covid-19.N Engl J Med. 2020; 382: 2327-2336Crossref PubMed Scopus (1956) Google Scholar targeted antiinflammatory,19Radbel J. Narayanan N. Bhatt P.J. Use of tocilizumab for COVID-19-induced cytokine release syndrome.Chest. 2020; 158: e15-e19Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar,20Sciascia S. Aprà F. Baffa A. et al.Pilot prospective open, single-arm multicentre study on off-label use of tocilizumab in patients with severe COVID-19.Clin Exp Rheumatol. 2020; 38: 529-532PubMed Google Scholar,22Xu X. Han M. Li T. et al.Effective treatment of severe COVID-19 patients with tocilizumab.Proc Natl Acad Sci U S A. 2020; 117: 10970-10975Crossref PubMed Scopus (1736) Google Scholar or immunomodulatory therapies.26Chen J. A pilot study of hydroxychloroquine in treatment of patients with moderate COVID-19.J Zhejiang Univ Med Sci. 2020; 49: 215-219Google Scholar However, the use of such drugs might lead to different expected outcomes in critically ill patients, as compared with patients with less severe disease. Because severity and associated organ dysfunctions are the main drivers of mortality, any treatment that should be evaluated would need to be given early in the course of the disease to be beneficial. Moreover, this disease has shown a remarkable underlying heterogeneity in terms of patients’ profiles and severity, suggesting that a response to specific targeted therapies is likely to vary across patients. The three clusters identified in this study argue for targeting cytokines, complement system, or humoral response in different groups of patients with seemingly identical clinical severity. Taken together, our findings highlight that not all patients with severe COVID-19 who bear similar clinical characteristics have the same immunologic profile; they may not benefit from targeted therapies in the same way and therefore would not be eligible for the same targeted interventions. Our study suffers from limitations. In the current study, we focused only on data available on the day of ICU admission, regardless of the temporal evolution during the subsequent hospital stay. Also, we cannot exclude that a patient's profile may change over time. However, we chose to define immunophenotypes at baseline, because it is a timely window for deciding on specific treatment eligibility. We also included time from symptoms onset to ICU admission in the current analysis, to take into account a possible difference in the course of the disease. Our results generate hypotheses that need to be validated on larger cohorts. Whether the immunologic phenotypes described in this study can be expanded to other patients’ registries or can explain inconsistent results from clinical trials needs to be determined. Although this could be the biological translation of heterogeneity, this might be of use when selecting a targeted therapy. How these immunological clusters might be associated with morbidity and mortality is uncertain. Although the precise cause of death could not be identified, there are differences between the clusters of mortality and extra-respiratory damage (acute renal failure, thromboembolic complications) that should be clarified. Furthermore, sixteen patients of our cohort were still receiving immunosuppressive therapy for cancer or solid organ transplantation (Table 2). Such conditions could be associated with immunological parameters variation. However, we performed a sensitivity analysis after removing these patients (e-Table 2, e-Fig 6), which led to the same results. Then, our study was observational, and we cannot rule out that some heterogeneity was introduced in studied populations or procedures. However, general management and data collection were protocolized without great disparities. Finally, we focused on critically ill patients at an advanced stage of disease progression, and our results would need to be validated in less severe cases. Similarly, this study was conducted in a single center and needs to be confirmed in larger cohorts. This study raises the hypothesis that, besides clinical overlap, critically ill patients with COVID-19 have heterogeneous immunological profiles. Our findings highlight that clinical trials might be analyzed based on this biological heterogeneity before concluding on clinical futility. For instance, trials that are being conducted for IL-6, IL-1β, or complement blockade might benefit from post hoc analysis stratifying primary or secondary endpoints by these immunophenotypes.Take-home PointsStudy Question: Are critically-ill COVID-19 patients biologically and immunologically dissociable based on profiling of currently evaluated therapeutic targets?Results: We found that patients were distributed in three clusters bearing distinct immunologic features and associated with different ICU outcomes.Interpretation: Severe COVID-19 patients exhibiting cytokine release marks, complement activation, or B-lymphocyte defects are distinct from each other. Study Question: Are critically-ill COVID-19 patients biologically and immunologically dissociable based on profiling of currently evaluated therapeutic targets? Results: We found that patients were distributed in three clusters bearing distinct immunologic features and associated with different ICU outcomes. Interpretation: Severe COVID-19 patients exhibiting cytokine release marks, complement activation, or B-lymphocyte defects are distinct from each other. Author contributions: G. D. is the guarantor of the content of the manuscript, including the data and analysis. T. D., G. D., and E. A. designed and performed research; T. D. and G. D. analyzed the data; T. D., G. D., and E. A. wrote the manuscript; T. D., S. C. Z., V. F. B., F. M., E. L., M. D., R. P. L., L. Z., E. A., and G. D. collected the data; T. D., S. C. Z., V. F. B., F. M., E. L., M. D., R. P. L., L. Z., E. A., and G. D. approved the final manuscript. Financial/nonfinancial disclosures: The authors have reported to CHEST the following: V. F. B.: consultancy or lecture fees or travel support from Alexion Pharmaceuticals, Inc., Apellis, and Roche. None declared (T. D., S. C.-Z., F. M., E. L., M. D., R. P. d. L., L. Z., E. A., G. D.). Other contributions: This study was approved by the Ethics Committee of the French Intensive Care Society (FICS; CE SRLF n°20-32). The datasets used or analyzed during the current study are available from the corresponding author on reasonable request. The authors declare that they have no competing interests for the present work. Additional information: The e-Figures and e-Tables can be found in the Supplemental Materials section of the online article. Download .pdf (.54 MB) Help with pdf files e-Online Data/cms/asset/aad9a773-7f07-4d80-8b82-00990b0fab3b/mmc2.mp3Loading ... Download .mp3 (22.32 MB) Help with .mp3 files Audio Immunological Subpopulations Within Critically Ill COVID-19 PatientsCHESTVol. 159Issue 5PreviewSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections cause coronavirus disease 2019 (COVID-19).1 Most SARS-CoV-2 infections are self-limiting and pauci-symptomatic. However, a minority of SARS-CoV-2 infections develop pulmonary and extrapulmonary organ dysfunction (such as hypoxemic respiratory failure, acute kidney injury, and thrombotic complications2) that require organ support (COVID-19 critical illness or severe COVID-19, equivalent to World Health Organization Clinical Progression Scale of ≥6 points). Full-Text PDF" @default.
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• W3111159637 title "Identification of Distinct Immunophenotypes in Critically Ill Coronavirus Disease 2019 Patients" @default.
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