Acute respiratory distress syndrome; COVID-19; Mechanical ventilation; Outcome; Predictive survival model; Critical Care and Intensive Care Medicine
Abstract :
[en] [en] BACKGROUND: Predicting outcomes of critically ill intensive care unit (ICU) patients with coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU stays.
METHODS: The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID-ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14.
RESULTS: Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICU-entry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models. For the test dataset, SOSIC-7's area under the ROC curve was slightly higher (0.80 [0.74-0.86]) than those for SOSIC-1 (0.76 [0.71-0.81]) and SOSIC-14 (0.76 [0.68-0.83]). Similarly, SOSIC-1 and SOSIC-7 had excellent calibration curves, with similar Brier scores for the three models.
CONCLUSION: The SOSIC scores showed that entering 15 to 27 baseline and dynamic clinical parameters into an automatable XGBoost algorithm can potentially accurately predict the likely 90-day mortality post-ICU admission (sosic.shinyapps.io/shiny). Although external SOSIC-score validation is still needed, it is an additional tool to strengthen decisions about life-sustaining treatments and informing family members of likely prognosis.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Schmidt, Matthieu ; Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition, Paris, France. matthieu.schmidt@aphp.fr ; Service de Médecine Intensive-Réanimation, Institut de Cardiologie, iCAN, Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (APHP), 47, bd de l'Hôpital, 75651, Paris Cedex 13, France. matthieu.schmidt@aphp.fr ; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France. matthieu.schmidt@aphp.fr
Guidet, Bertrand; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France ; Institut Pierre-Louis d'Epidémiologie et de Santé Publique, APHP, Hôpital Saint-Antoine, INSERM, Service de Réanimation, Sorbonne Université, Paris, France
Demoule, Alexandre; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France ; Service de Pneumologie, Médecine Intensive-Réanimation (Département R3S), Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Paris, France ; Sorbonne Université, INSERM UMRS_1158, Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, France
Ponnaiah, Maharajah; Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition, Paris, France
Fartoukh, Muriel; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France ; Service de Médecine Intensive-Réanimation, Hôpital Tenon, Département Médico-Universitaire APPROCHES, APHP, Paris, France ; Groupe de Recherche Clinique CARMAS, Université Paris-Est Créteil, Créteil, France
Puybasset, Louis; CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Paris, France ; Department of Anesthesiology & Critical Care, APHP, Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France
Combes, Alain; Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition, Paris, France ; Service de Médecine Intensive-Réanimation, Institut de Cardiologie, iCAN, Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (APHP), 47, bd de l'Hôpital, 75651, Paris Cedex 13, France ; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France
Hajage, David; Département de Santé Publique, Centre de Pharmacoépidémiologie (Cephepi), INSER, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, APHP, Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France
COVID-ICU Investigators
Other collaborator :
Lambermont, Bernard ; Centre Hospitalier Universitaire de Liège - CHU > > Service des soins intensifs
Language :
English
Title :
Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores.
Publication date :
11 December 2021
Journal title :
Annals of Intensive Care
eISSN :
2110-5820
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
AP-HP - Fondation de l’AP-HP French Ministry of Social Affairs and Health
Funding text :
This study was funded by the Fondation APHP and its donators though the program “Alliance Tous Unis Contre le Virus”, the Direction de la Recherche Clinique et du Développement, and the French Ministry of Health. The Reseau European de recherche en Ventilation Artificielle (REVA) network received a 75,000 € research grant from Air Liquide Healthcare. The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The sponsor was Assistance Publique Hôpitaux de Paris (APHP).
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