Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores

Annals of Intensive Care

卷:11

for the COVID-ICU Investigators¹  Louis Puybasset²  David Hajage³  Alexandre Demoule⁴  Muriel Fartoukh⁴  Bertrand Guidet⁴  Maharajah Ponnaiah⁵  Matthieu Schmidt⁵  Alain Combes⁵ 

[1] ;

[2] CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, Sorbonne Université;

[3] Département de Santé Publique, Centre de Pharmacoépidémiologie (Cephepi), INSER, Institut Pierre-Louis d’Epidémiologie et de Santé Publique, APHP, Hôpital Pitié–Salpêtrière, Sorbonne Université;

[4] Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié–Salpêtrière;

[5] Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition;

关键词: Acute respiratory distress syndrome;    Mechanical ventilation;    COVID-19;    Outcome;    Predictive survival model;   

DOI  :  10.1186/s13613-021-00956-9

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown