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Machine-learning-derived sepsis bundle of care

A Correspondence to this article was published on 02 January 2023

Abstract

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.

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Data availability

This study was performed with the data from the Medical Information Mart for Intensive Care (MIMIC)-IV version 1.0 and the eICU Collaborative Research Database (eICU-CRD) version 1.2. Even though datasets are de-identified, restrictions have been imposed on data sharing since they contain sensitive information. Conventions are signed for researchers before any access to the data. For data access, interested researchers must fulfill all of the following requirements: be a credentialed user of https://physionet.org/, finish required training and sign the data use agreement for the project.

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Funding

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AK had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: RP. Acquisition, analysis, or interpretation of data: AK, IL, CC, A-SJ, RP. Drafting of the manuscript: AK, IL, RP. Critical revision of the manuscript for important intellectual content: AK, IL, CC, A-SJ, RP. Statistical analysis: AK, IL. Administrative, technical, or material support: Jannot, RP. Supervision: RP, IL.

Corresponding author

Correspondence to Romain Pirracchio.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Permission to use the data was obtained for both databases (No. 45398938). Because of the de-identified nature of the data, informed consent was waived. Consent was obtained for the original data collection.

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Kalimouttou, A., Lerner, I., Cheurfa, C. et al. Machine-learning-derived sepsis bundle of care. Intensive Care Med 49, 26–36 (2023). https://doi.org/10.1007/s00134-022-06928-2

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