Validation of extracorporeal membrane oxygenation mortality prediction and severity of illness scores in an international COVID-19 cohort
ISARIC Clinical Characterisation Group
(2023) Artificial Organs, volume 47, issue 9, pp. 1490 - 1502
(Article)
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
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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.
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Keywords: ARDS, COVID-19, ECLS, extracorporeal life support, extracorporeal membrane oxygenation, mortality, prediction scores, Sars-Cov2, V-V ECMO, Bioengineering, Medicine (miscellaneous), Biomaterials, Biomedical Engineering
ISSN: 0160-564X
Publisher: Wiley-Blackwell
Note: 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. 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.
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