Machine-learning-derived sepsis bundle of care - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Intensive Care Medicine Year : 2022

Machine-learning-derived sepsis bundle of care


Purpose Compliance to the Surviving Sepsis Campaign (SSC) guidelines is limited. This is known to be associated with increased mortality. The aim of this retrospective cohort study was to identify among the SCC guidelines the optimal bundle of recommendations that minimize 28-day mortality. Methods We used a training cohort to identify, using a least absolute shrinkage and selection operator penalized machine learning model, this bundle. Patients with sepsis/septic shock admitted to the intensive care unit (ICU) were extracted from two US databases, the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training and internal validation cohorts) and the eICU Collaborative Research Database (eICU-CRD) (external validation cohort). In the validation cohorts, we defined a bundle group that includes patients who were treated with at least all the recommendations selected in our bundle and a no-bundle group that includes patients in whom at least one recommendation from our bundle was omitted. Results All-cause 28-day mortality was the primary outcome measure. A total of 42,735 patients were included. Six recommendations (antimicrobials, balanced crystalloid, insulin therapy, corticosteroids, vasopressin, and bicarbonate therapy) were identified from the training cohort to be included in our bundle. In the propensity score-(PS)-matched internal validation cohort, the bundle group was associated with a lower mortality (OR 0.41 [0.33–0.53]; p < 0.001) compared to the no-bundle group. This was confirmed in the PS-matched external validation cohort (OR 0.75 [0.60–0.94]; p 0.02). Conclusion Our bundle of six recommendations is associated with a dramatic reduction in mortality in sepsis and septic shock. This bundle needs to be evaluated prospectively.
No file

Dates and versions

hal-03911622 , version 1 (23-12-2022)



Alexandre Kalimouttou, Ivan Lerner, Chérifa Cheurfa, Anne-Sophie Jannot, Romain Pirracchio. Machine-learning-derived sepsis bundle of care. Intensive Care Medicine, In press, ⟨10.1007/s00134-022-06928-2⟩. ⟨hal-03911622⟩
61 View
0 Download



Gmail Mastodon Facebook X LinkedIn More