TY - JOUR
T1 - Determinants of the effect of extracorporeal carbon dioxide removal in the SUPERNOVA trial
T2 - implications for trial design
AU - Goligher, Ewan C.
AU - Combes, Alain
AU - Brodie, Daniel
AU - Ferguson, Niall D.
AU - Pesenti, Antonio M.
AU - Ranieri, V. Marco
AU - Slutsky, Arthur S.
AU - Beale, Richard
AU - Brochard, Laurent
AU - Chiche, Jean Daniel
AU - Fan, Eddy
AU - de Backer, Daniel
AU - Francois, Guy
AU - Laffey, John
AU - Mercat, Alain
AU - McAuley, Daniel F.
AU - Müller, Thomas
AU - Quintel, Michael
AU - Vincent, Jean Louis
AU - Taccone, Fabio Silvio
AU - Peperstraete, Harlinde
AU - Morimont, Philippe
AU - Schmidt, Matthieu
AU - Levy, Bruno
AU - Diehl, Jean Luc
AU - Guervilly, Christophe
AU - Capelier, Gilles
AU - Vieillard-Baron, Antoine
AU - Messika, Jonathan
AU - Karagiannidis, Christian
AU - Moerer, Onnen
AU - Urbino, Rosario
AU - Antonelli, Massimo
AU - Mojoli, Francesco
AU - Alessandri, Francesco
AU - Grasselli, Giacomo
AU - Donker, Dirk
AU - Ferrer, Ricard
AU - Mancebo, Jordi
AU - Fanelli, Vito
AU - Pham, Tai
N1 - Funding Information:
Richard BEALE 8, Laurent BROCHARD 1,7, Jean-Daniel CHICHE 9, Eddy FAN 1,2,3,5, Daniel DE BACKER 10, Guy FRANCOIS 11, John LAFFEY 12, Alain MERCAT 13, Daniel F. McAULEY 14, Thomas MÜLLER 15, Michael QUINTEL 16, Jean-Louis VINCENT 17, Fabio Silvio TACCONE 17, Harlinde PEPERSTRAETE 18, Philippe MORIMONT 19, Matthieu SCHMIDT 4, Bruno LEVY 20, Jean-Luc DIEHL 21, Christophe GUERVILLY 22, Gilles CAPELIER 23, Antoine VIEILLARD-BARON 24, Jonathan MESSIKA 25, Christian KARAGIANNIDIS 26, Onnen MOERER 16, Rosario Urbino 27, Massimo ANTONELLI 28, Francesco MOJOLI 29, Francesco ALESSANDRI 30, Giacomo GRASSELLI 6, Dirk DONKER 31, Ricard FERRER 32, Jordi MANCEBO 33, Vito FANELLI 27, Tai PHAM 7 1Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada; 2Department of Medicine, Division of Respirology, University Health Network, Toronto, Canada; 3Toronto General Hospital Research Institute, Toronto, Canada; 4Sorbonne Université, INSERM, UMRS_1166-ICAN, Institute of Cardio-metabolism and Nutrition, and Service de médecine intensive-réanimation, Institut de Cardiologie, APHP Hôpital Pitié–Salpêtrière, PARIS, France; 5Institute for Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada; 6Dipartimento di Anestesia, Rianimazione ed Emergenza Urgenza, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy; 7Keenan Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada; 8Guy’s and St. Thomas’ NHS Foundation Trust, London, UK; 9Hôpital Cochin, Université Paris Descartes, France; 10Hôpital de Braine l’Alleud-Waterloo, Université Libre de Bruxelles, Belgium; 11European Society of Intensive Care Medicine, Brussels, Belgium; 12Galway University Hospitals, Galway, Ireland; 13Centre Hospitalier Universitaire, University of Angers, France; 14Centre for Experimental Medicine, Queen’s University Belfast and Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, UK; 15University Hospital Regensburg, Regensburg, Germany; 16Universitätsmedizin Göttingen, Germany; 17Erasme Hospital, Brussels Free University, Belgium; 18Ghent University Hospital, Belgium; 19University Hospital Liège, Belgium; 20Service de Réanimation Médicale Brabois, CHRU Nancy, Pôle Cardio-Médico-Chirurgical, INSERM U1116, Faculté de Médecine, 54511 Vandoeuvre-les-Nancy, France ; Université de Lorraine, Nancy, France; 21Hôpital Européen Georges Pompidou, Paris, France; 22AP-HM Hôpital Nord, Marseille, France; 23Centre Hospitalier Universitaire de Besançon, France; 24Hôpital Ambroise Paré, Paris, France; 25Hôpital Louis Mourier, Paris, France; 26Kliniken der Stadt, Köln, Germany; 27University of Turin, Città della Salute e della Scienza di Torino, Department of Anesthesia and Intensive Care Medicine, Turin, Italy; 28Università Cattolica-Policlinico Universitario A.Gemelli, Roma, Italy; 29Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; 30Policlinico Umberto I, Roma, Italy; 31University Medical Center, Utrecht University Medical Center, Utrecht, Netherlands; 32Hospital Universitari Vall d’Hebron, Barcelona, Spain; 33Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
Funding Information:
The SUPERNOVA trial (Strategy of Ultra-Protective Lung Ventilation with extracorporeal CO removal for new-onset Moderate to severe ARDS) was supported by the European Society of Intensive Care Medicine and endorsed by the International ECMO Network (ECMONet). 2
Funding Information:
Dr. Goligher is supported by an Early Career Investigator Award (AR7-162822) from the Canadian Institutes of Health Research and an IDCCM scholarship from the University of Toronto. Dr. Slutsky is supported in part by grants 137772 and 143285 from the Canadian Institutes of Health Research. Acknowledgements
Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Purpose: To describe the variability and determinants of the effect of extracorporeal CO2 removal (ECCO2R) on tidal volume (Vt), driving pressure (ΔP), and mechanical power (PowerRS) and to determine whether highly responsive patients can be identified for the purpose of predictive enrichment in ECCO2R trial design. Methods: Using data from the SUPERNOVA trial (95 patients with early moderate acute respiratory distress syndrome), the independent effects of alveolar dead space fraction (ADF), respiratory system compliance (Crs), hypoxemia (PaO2/FiO2), and device performance (higher vs lower CO2 extraction) on the magnitude of reduction in Vt, ΔP, and PowerRS permitted by ECCO2R were assessed by linear regression. Predicted and observed changes in ΔP were compared by Bland–Altman analysis. Hypothetical trials of ECCO2R, incorporating predictive enrichment and different target CO2 removal rates, were simulated in the SUPERNOVA study population. Results: Changes in Vt permitted by ECCO2R were independently associated with ADF and device performance but not PaO2/FiO2. Changes in ΔP and PowerRS were independently associated with ADF, Crs, and device performance but not PaO2/FiO2. The change in ΔP predicted from ADF and Crs was moderately correlated with observed change in ΔP (R2 0.32, p < 0.001); limits of agreement between observed and predicted changes in ΔP were ± 3.9 cmH2O. In simulated trials, restricting enrollment to patients with a larger predicted decrease in ΔP enhanced the average reduction in ΔP, increased predicted mortality benefit, and reduced sample size and screening size requirements. The increase in statistical power obtained by restricting enrollment based on predicted ΔP response varied according to device performance as specified by the target CO2 removal rate. Conclusions: The lung-protective benefits of ECCO2R increase with higher alveolar dead space fraction, lower respiratory system compliance, and higher device performance. ADF and Crs, rather than severity of hypoxemia, should be the primary factors determining whether to enroll patients in clinical trials of ECCO2R.
AB - Purpose: To describe the variability and determinants of the effect of extracorporeal CO2 removal (ECCO2R) on tidal volume (Vt), driving pressure (ΔP), and mechanical power (PowerRS) and to determine whether highly responsive patients can be identified for the purpose of predictive enrichment in ECCO2R trial design. Methods: Using data from the SUPERNOVA trial (95 patients with early moderate acute respiratory distress syndrome), the independent effects of alveolar dead space fraction (ADF), respiratory system compliance (Crs), hypoxemia (PaO2/FiO2), and device performance (higher vs lower CO2 extraction) on the magnitude of reduction in Vt, ΔP, and PowerRS permitted by ECCO2R were assessed by linear regression. Predicted and observed changes in ΔP were compared by Bland–Altman analysis. Hypothetical trials of ECCO2R, incorporating predictive enrichment and different target CO2 removal rates, were simulated in the SUPERNOVA study population. Results: Changes in Vt permitted by ECCO2R were independently associated with ADF and device performance but not PaO2/FiO2. Changes in ΔP and PowerRS were independently associated with ADF, Crs, and device performance but not PaO2/FiO2. The change in ΔP predicted from ADF and Crs was moderately correlated with observed change in ΔP (R2 0.32, p < 0.001); limits of agreement between observed and predicted changes in ΔP were ± 3.9 cmH2O. In simulated trials, restricting enrollment to patients with a larger predicted decrease in ΔP enhanced the average reduction in ΔP, increased predicted mortality benefit, and reduced sample size and screening size requirements. The increase in statistical power obtained by restricting enrollment based on predicted ΔP response varied according to device performance as specified by the target CO2 removal rate. Conclusions: The lung-protective benefits of ECCO2R increase with higher alveolar dead space fraction, lower respiratory system compliance, and higher device performance. ADF and Crs, rather than severity of hypoxemia, should be the primary factors determining whether to enroll patients in clinical trials of ECCO2R.
KW - Acute respiratory distress syndrome
KW - Artificial ventilation
KW - Extracorporeal carbon dioxide removal
KW - Predictive enrichment
KW - Ventilator-induced lung injury
UR - http://www.scopus.com/inward/record.url?scp=85070821269&partnerID=8YFLogxK
U2 - 10.1007/s00134-019-05708-9
DO - 10.1007/s00134-019-05708-9
M3 - Article
C2 - 31432216
AN - SCOPUS:85070821269
SN - 0342-4642
VL - 45
SP - 1219
EP - 1230
JO - Intensive Care Medicine
JF - Intensive Care Medicine
IS - 9
ER -