External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals
Yannis Lombardi
(1)
,
Loris Azoyan
(1)
,
Piotr Szychowiak
(2)
,
Ali Bellamine
(1)
,
Guillaume Lemaitre
(3)
,
Mélodie Bernaux
(1)
,
Christel Daniel
(1, 4)
,
Judith Leblanc
(5)
,
Quentin Riller
(1)
,
Olivier Steichen
(5)
,
Pierre-Yves Ancel
,
Alain Bauchet
,
Nathanael Beeker
,
Vincent Benoit
,
Romain Bey
,
Aurélie Bourmaud
,
Stéphane Bréant
,
Anita Burgun
,
Fabrice Carrat
(5)
,
Charlotte Caucheteux
,
Julien Champ
,
Sylvie Cormont
,
Julien Dubiel
,
Catherine Duclos
(4)
,
Loic Esteve
,
Marie Frank
,
Nicolas Garcelon
(6, 7, 8)
,
Alexandre Gramfort
,
Nicolas Griffon
,
Olivier Grisel
,
Martin Guilbaud
,
Claire Hassen-Khodja
,
François Hemery
,
Martin Hilka
,
Anne Sophie Jannot
,
Jerome Lambert
,
Richard Layese
,
Léo Lebouter
,
Damien Leprovost
,
Ivan Lerner
,
Kankoe Levi Sallah
,
Aurélien Maire
,
Marie-France Mamzer
,
Patricia Martel
,
Arthur Mensch
,
Thomas Moreau
,
Antoine Neuraz
,
Nina Orlova
,
Nicolas Paris
,
Bastien Rance
,
Hélène Ravera
,
Antoine Rozes
,
Pierre Rufat
,
Elisa Salamanca
,
Arnaud Sandrin
,
Patricia Serre
,
Xavier Tannier
(4)
,
Jean-Marc Treluyer
,
Damien van Gysel
,
Gael Varoquaux
,
Jill-Jênn Vie
,
Maxime Wack
,
Perceval Wajsburt
,
Demian Wassermann
,
Eric Zapletal
1
AP-HP -
Assistance publique - Hôpitaux de Paris (AP-HP)
2 CHU Trousseau [Tours]
3 SODA - Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données
4 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
5 iPLESP - Institut Pierre Louis d'Epidémiologie et de Santé Publique
6 CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers
7 HeKA - Health data- and model- driven Knowledge Acquisition
8 Imagine - U1163 - Imagine - Institut des maladies génétiques (IHU)
2 CHU Trousseau [Tours]
3 SODA - Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données
4 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
5 iPLESP - Institut Pierre Louis d'Epidémiologie et de Santé Publique
6 CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers
7 HeKA - Health data- and model- driven Knowledge Acquisition
8 Imagine - U1163 - Imagine - Institut des maladies génétiques (IHU)
Olivier Steichen
- Function : Author
- PersonId : 4845
- IdHAL : olivier-steichen
- ORCID : 0000-0002-1185-0372
- IdRef : 102126151
Pierre-Yves Ancel
- Function : Contributor
Alain Bauchet
- Function : Contributor
Nathanael Beeker
- Function : Contributor
Vincent Benoit
- Function : Contributor
Romain Bey
- Function : Contributor
Aurélie Bourmaud
- Function : Contributor
Stéphane Bréant
- Function : Contributor
Anita Burgun
- Function : Contributor
Charlotte Caucheteux
- Function : Contributor
Julien Champ
- Function : Contributor
Sylvie Cormont
- Function : Contributor
Julien Dubiel
- Function : Contributor
Catherine Duclos
- Function : Contributor
Loic Esteve
- Function : Contributor
Marie Frank
- Function : Contributor
Nicolas Garcelon
- Function : Contributor
- PersonId : 1089371
Alexandre Gramfort
- Function : Contributor
Nicolas Griffon
- Function : Contributor
Olivier Grisel
- Function : Contributor
Martin Guilbaud
- Function : Contributor
Claire Hassen-Khodja
- Function : Contributor
François Hemery
- Function : Contributor
Martin Hilka
- Function : Contributor
Anne Sophie Jannot
- Function : Contributor
Jerome Lambert
- Function : Contributor
Richard Layese
- Function : Contributor
Léo Lebouter
- Function : Contributor
Damien Leprovost
- Function : Contributor
Ivan Lerner
- Function : Contributor
Kankoe Levi Sallah
- Function : Contributor
Aurélien Maire
- Function : Contributor
Marie-France Mamzer
- Function : Contributor
Patricia Martel
- Function : Contributor
Arthur Mensch
- Function : Contributor
Thomas Moreau
- Function : Contributor
Antoine Neuraz
- Function : Contributor
Nina Orlova
- Function : Contributor
Nicolas Paris
- Function : Contributor
Bastien Rance
- Function : Contributor
Hélène Ravera
- Function : Contributor
Antoine Rozes
- Function : Contributor
Pierre Rufat
- Function : Contributor
Elisa Salamanca
- Function : Contributor
Arnaud Sandrin
- Function : Contributor
Patricia Serre
- Function : Contributor
Xavier Tannier
- Function : Contributor
Jean-Marc Treluyer
- Function : Contributor
Damien van Gysel
- Function : Contributor
Gael Varoquaux
- Function : Contributor
Jill-Jênn Vie
- Function : Contributor
Maxime Wack
- Function : Contributor
Perceval Wajsburt
- Function : Contributor
Demian Wassermann
- Function : Contributor
Eric Zapletal
- Function : Contributor
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.
Domains
Life Sciences [q-bio]
Origin : Publication funded by an institution