1. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals
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Lombardi, Yannis, Azoyan, Loris, Szychowiak, Piotr, Bellamine, Ali, Lemaitre, Guillaume, Bernaux, Mélodie, Daniel, Christel, Leblanc, Judith, Riller, Quentin, Steichen, Olivier, Ancel, Pierre-Yves, Bauchet, Alain, Beeker, Nathanael, Benoit, Vincent, Bey, Romain, Bourmaud, Aurélie, Bréant, Stéphane, Burgun, Anita, Carrat, Fabrice, Caucheteux, Charlotte, Champ, Julien, Cormont, Sylvie, Dubiel, Julien, Duclos, Catherine, Esteve, Loic, Frank, Marie, Garcelon, Nicolas, Gramfort, Alexandre, Griffon, Nicolas, Grisel, Olivier, Guilbaud, Martin, Hassen-Khodja, Claire, Hemery, François, Hilka, Martin, Jannot, Anne Sophie, Lambert, Jerome, Layese, Richard, Lebouter, Léo, Leprovost, Damien, Lerner, Ivan, Sallah, Kankoe Levi, Maire, Aurélien, Mamzer, Marie-France, Martel, Patricia, Mensch, Arthur, Moreau, Thomas, Neuraz, Antoine, Orlova, Nina, Paris, Nicolas, Rance, Bastien, Ravera, Hélène, Rozes, Antoine, Rufat, Pierre, Salamanca, Elisa, Sandrin, Arnaud, Serre, Patricia, Tannier, Xavier, Treluyer, Jean-Marc, van Gysel, Damien, Varoquaux, Gael, Vie, Jill-Jênn, Wack, Maxime, Wajsburt, Perceval, Wassermann, Demian, Zapletal, Eric, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Trousseau [Tours], Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données (SODA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Centre d'Immunologie et de Maladies Infectieuses (CIMI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Direction générale déléguée à l'innovation (DGD-I), Institut National de Recherche en Informatique et en Automatique (Inria), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de Référence Maladies auto-immunes Systémiques Rares [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Tenon [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU), Inria Siège, Service de Département de médecine interne et immunologie clinique [CHU Pitié-Salpêtrière] (DMIIC), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), and Gestionnaire, HAL Sorbonne Université 5
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Adult ,Paris ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Original ,[SDV]Life Sciences [q-bio] ,030204 cardiovascular system & hematology ,Critical Care and Intensive Care Medicine ,law.invention ,Cohort Studies ,Hospitals, University ,03 medical and health sciences ,0302 clinical medicine ,law ,Anesthesiology ,medicine ,Humans ,030212 general & internal medicine ,Mortality ,Retrospective Studies ,[SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology ,Receiver operating characteristic ,SARS-CoV-2 ,business.industry ,External validation ,COVID-19 ,Prognosis ,University hospital ,Intensive care unit ,3. Good health ,Intensive Care Units ,Editorial ,SARS-CoV2 ,[SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology ,Emergency medicine ,Cohort ,business ,Cohort study - Abstract
Purpose The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19. Methods We used “COVID-19 Evidence Alerts” (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. Results We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome. Conclusion Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06524-w.
- Published
- 2021