Abstract
Background: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort. Methods: We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score. Results: We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58–0.62), AUPRC (0.62–0.74), and Brier score (0.286–0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52–0.57), AURPC (0.59–0.64), and Brier Score (0.265–0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26). Conclusion: Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools.
Original language | English |
---|---|
Pages (from-to) | 1490-1502 |
Number of pages | 13 |
Journal | Artificial Organs |
Volume | 47 |
Issue number | 9 |
DOIs | |
Publication status | Published - Sept 2023 |
Keywords
- ARDS
- COVID-19
- ECLS
- extracorporeal life support
- extracorporeal membrane oxygenation
- mortality
- prediction scores
- Sars-Cov2
- V-V ECMO
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In: Artificial Organs, Vol. 47, No. 9, 09.2023, p. 1490-1502.
Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Validation of extracorporeal membrane oxygenation mortality prediction and severity of illness scores in an international COVID-19 cohort
AU - Shah, Neel
AU - Xue, Bing
AU - Xu, Ziqi
AU - Yang, Hanqing
AU - Marwali, Eva
AU - Dalton, Heidi
AU - Payne, Philip P.R.
AU - Lu, Chenyang
AU - Said, Ahmed S.
AU - Abdukahil, Sheryl Ann
AU - Abdulkadir, Nurul Najmee
AU - Absil, Lara
AU - Acker, Andrew
AU - Adrião, Diana
AU - Hssain, Ali Ait
AU - Akwani, Chika
AU - Al Qasim, Eman
AU - Alalqam, Razi
AU - Al-dabbous, Tala
AU - Alex, Beatrice
AU - Al-Fares, Abdulrahman
AU - Alfoudri, Huda
AU - Aliudin, Jeffrey
AU - Alves, João Melo
AU - Alves, Rita
AU - Alves, João Melo
AU - Cabrita, Joana Alves
AU - Amaral, Maria
AU - Amira, Nur
AU - Andini, Roberto
AU - Anthonidass, Sivanesen
AU - Antonelli, Massimo
AU - Arabi, Yaseen
AU - Arcadipane, Antonio
AU - Arenz, Lukas
AU - Arnold-Day, Christel
AU - Arora, Lovkesh
AU - Arora, Rakesh
AU - Ashraf, Muhammad
AU - Asyraf, Amirul
AU - Atique, Anika
AU - Bach, Benjamin
AU - Baillie, John Kenneth
AU - Bak, Erica
AU - Bakar, Nazreen Abu
AU - Balakrishnan, Mohanaprasanth
AU - Barbalho, Renata
AU - Barclay, Wendy S.
AU - Barnett, Saef Umar
AU - Barnikel, Michaela
AU - Barrasa, Helena
AU - Barrigoto, Cleide
AU - Baruch, Joaquín
AU - Basri, Muhammad Fadhli Hassin
AU - Battaglini, Denise
AU - Rincon, Diego Fernando Bautista
AU - Bee, Ker Hong
AU - Begum, Husna
AU - Beljantsev, Aleksandr
AU - Benjiman, Lionel Eric
AU - Bento, Luís
AU - Sobrino, José Luis Bernal
AU - Bertolino, Lorenzo
AU - Bhatt, Amar
AU - Bianco, Claudia
AU - Bidin, Farah Nadiah
AU - Humaid, Felwa Bin
AU - Kamarudin, Mohd Nazlin Bin
AU - Blanco-Schweizer, Pablo
AU - Bloos, Frank
AU - Boccia, Filomena
AU - Bogaert, Debby
AU - Borges, Diogo
AU - Bouhmani, Dounia
AU - Bouziotis, Jason
AU - Boylan, Maria
AU - Bozza, Fernando Augusto
AU - Brazzi, Luca
AU - Brewster, David
AU - Brickell, Kathy
AU - Broadley, Tessa
AU - Brozzi, Nicolas
AU - Buchtele, Nina
AU - Burrell, Aidan
AU - Cabral, Susana
AU - Cabrita, Joana
AU - Garcês, Rui Caetano
AU - Calligy, Kate
AU - Campbell, Paul
AU - Cardoso, Sofia
AU - Cardoso, Filipe
AU - Cardoso, Filipa
AU - Carelli, Simone
AU - Carrier, François Martin
AU - Carson, Gail
AU - Cascão, Mariana
AU - Casimiro, José
AU - Castanheira, Nidyanara
AU - Castro, Ivo
AU - Catarino, Ana
AU - Cavalin, Roberta
AU - Cavalli, Giulio Giovanni
AU - Cavayas, Alexandros
AU - Chand, Meera
AU - Chen, Anjellica
AU - Chen, Yih Sharng
AU - Cheng, Matthew Pellan
AU - Chica, Julian
AU - Chidambaram, Suresh Kumar
AU - Tho, Leong Chin
AU - Cho, Sung Min
AU - Chow, Ting Soo
AU - Chua, Hiu Jian
AU - Chua, Jonathan
AU - Cidade, Jose Pedro
AU - Citarella, Barbara Wanjiru
AU - Ciullo, Anna
AU - Clarke, Jennifer
AU - Clohisey, Sara
AU - Codan, Cassidy
AU - Coles, Jennifer
AU - Colombo, Sebastiano Maria
AU - Connor, Marie
AU - Cooke, Graham S.
AU - Corley, Amanda
AU - Cornelis, Sabine
AU - Corpuz, Arianne Joy
AU - Crump, Jonathan
AU - Cruz, Claudina
AU - Bermúdez, Juan Luis Cruz
AU - Rojo, Jaime Cruz
AU - Csete, Marc
AU - Cullen, Ailbhe
AU - Cummings, Matthew
AU - Curley, Gerard
AU - Custodio, Paula
AU - da Silva Filipe, Ana
AU - Dabaliz, Al Awwab
AU - Dagens, Andrew
AU - Dahly, Darren
AU - Dalton, Heidi
AU - Dalton, Jo
AU - Damas, Juliana
AU - Dankwa, Emmanuelle A.
AU - Dantas, Jorge
AU - de Loughry, Gillian
AU - De Rosa, Rosanna
AU - de Silva, Thushan
AU - Deacon, Jillian
AU - Dean, David
AU - DeBenedictis, Bianca
AU - Dell’Amore, Andrea
AU - Denis, Emmanuelle
AU - Depuydt, Pieter
AU - Desai, Mehul
AU - Dhangar, Pathik
AU - Diaz, Rodrigo
AU - Dieperink, Wim
AU - Docherty, Annemarie B.
AU - Doherty, Helen
AU - Donnelly, Christl A.
AU - Donohue, Chloe
AU - Downer, Triona
AU - Drake, Thomas
AU - Duculan, Toni
AU - Dunning, Jake
AU - Duplaix, Mathilde
AU - Durante-Mangoni, Emanuele
AU - Durham, Lucian
AU - Ean, Sim Choon
AU - Echeverria-Villalobos, Marco
AU - Elotmani, Loubna
AU - Elshazly, Tarek
AU - Eng, Chan Chee
AU - Escher, Martina
AU - Santo, Catarina Espírito
AU - Estevão, João
AU - Everding, Anna Greti
AU - Fairfield, Cameron J.
AU - Faria, Pedro
AU - Fateena, Hanan
AU - Fayed, Mohamed
AU - Fernandes, Jorge
AU - Fernandes, Marília Andreia
AU - Fernandes, Susana
AU - Ferrão, Joana
AU - Ferraz, Mário
AU - Ferreira, Bernardo
AU - Ferreira, Benigno
AU - Figueiredo-Mello, Claudia
AU - Fiorda, Juan
AU - Fletcher, Tom
AU - Florio, Letizia Lucia
AU - Fonseca, Tatiana
AU - Forsyth, Simon
AU - Foti, Giuseppe
AU - Fowler, Robert A.
AU - Fraser, John F.
AU - Fraser, Christophe
AU - Ribeiro, Ana Freitas
AU - French, Craig
AU - Fukuda, Masahiro
AU - Argin, G.
AU - Gagliardi, Massimo
AU - Gaião, Sérgio
AU - Gamble, Carrol
AU - Gani, Yasmin
AU - Garcia, Rebekha
AU - Barrio, Noelia García
AU - Gavin, Aisling
AU - Germano, Nuno
AU - Giani, Marco
AU - Gilroy, Elaine
AU - Girvan, Michelle
AU - Gnall, Eric
AU - Goffard, Jean Christophe
AU - Goh, Jin Yi
AU - Golob, Jonathan
AU - Gonzalez, Alicia
AU - Graf, Jeronimo
AU - Grasselli, Giacomo
AU - Green, Christopher A.
AU - Greenhalf, William
AU - Grieco, Domenico Luca
AU - Griffee, Matthew
AU - Griffiths, Fiona
AU - Lordemann, Anja Grosse
AU - Gruner, Heidi
AU - Gu, Yusing
AU - Guerreiro, Daniela
AU - de Castro, Maisa Guimarães
AU - Hakak, Sheeba
AU - Hall, Matthew
AU - Halpin, Sophie
AU - Hamer, Ansley
AU - Hammond, Terese
AU - Hammond, Naomi
AU - Han, Lim Yuen
AU - Hao, Kok Wei
AU - Hardwick, Hayley
AU - Harrison, Samuel Bernard Ekow
AU - Harrison, Ewen M.
AU - Harrison, Janet
AU - McArthur, Colin
AU - Nichol, Alistair D.
AU - Parke, Rachael
AU - Peake, Sandra L.
AU - Trapani, Tony
AU - Udy, Andrew
AU - Webb, Steve
AU - ISARIC Clinical Characterisation Group
N1 - Funding Information: This work was made possible by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the philanthropic support of the donors to the University of Oxford’s COVID‐19 Research Response Fund (0009109); grants from the National Institute for Health Research (NIHR; award CO‐CIN‐01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award ISBRC‐1215‐20013), and NIHR Clinical Research Network providing infrastructure support; CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and the coordination in Canada by Sunnybrook Research Institute; funding by the Health Research Board of Ireland [CTN‐2014‐12]; the Rapid European COVID‐19 Emergency Response research (RECOVER) [H2020 project 101003589] and European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; Cambridge NIHR Biomedical Research Centre (award NIHR203312); the Comprehensive Local Research Networks (CLRNs) of which PJMO is an NIHR Senior Investigator (NIHR201385); Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; funding from Medical University of Vienna, Department of Anaesthesia, Intensive Care Medicine and Pain Medicine; Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009), the Prince Charles Hospital Foundation, Australia; Australian Department of Health grant (3273191); grants from Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018‐7; the Firland Foundation, Shoreline, Washington, USA; a grant from foundation Bevordering Onderzoek Franciscus; Institute for Clinical Research (ICR), National Institutes of Health (NIH) supported by the Ministry of Health Malaysia. Funding Information: The investigators acknowledge the support of the COVID clinical management team, AIIMS, Rishikesh, India; the COVID‐19 Clinical Management team, Manipal Hospital Whitefield, Bengaluru, India; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team and supported by the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; the Liverpool School of Tropical Medicine and the University of Oxford; Imperial NIHR Biomedical Research Centre; endorsement of the Irish Critical Care‐ Clinical Trials Group, co‐ordination in Ireland by the Irish Critical Care‐ Clinical Trials Network at University College Dublin; and preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection. This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances; and the generosity of the patients and their families for their individual contributions in these difficult times. The COVID‐19 Clinical Information Network (CO‐CIN) data was collated by ISARIC4C Investigators. We also acknowledge the support of Jeremy J Farrar and Nahoko Shindo. Publisher Copyright: © 2023 International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.
PY - 2023/9
Y1 - 2023/9
N2 - Background: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort. Methods: We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score. Results: We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58–0.62), AUPRC (0.62–0.74), and Brier score (0.286–0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52–0.57), AURPC (0.59–0.64), and Brier Score (0.265–0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26). Conclusion: Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools.
AB - Background: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort. Methods: We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score. Results: We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58–0.62), AUPRC (0.62–0.74), and Brier score (0.286–0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52–0.57), AURPC (0.59–0.64), and Brier Score (0.265–0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26). Conclusion: Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools.
KW - ARDS
KW - COVID-19
KW - ECLS
KW - extracorporeal life support
KW - extracorporeal membrane oxygenation
KW - mortality
KW - prediction scores
KW - Sars-Cov2
KW - V-V ECMO
UR - http://www.scopus.com/inward/record.url?scp=85157988558&partnerID=8YFLogxK
U2 - 10.1111/aor.14542
DO - 10.1111/aor.14542
M3 - Article
C2 - 37032544
AN - SCOPUS:85157988558
SN - 0160-564X
VL - 47
SP - 1490
EP - 1502
JO - Artificial Organs
JF - Artificial Organs
IS - 9
ER -