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. 2023 Aug;38(3):343-352.
doi: 10.4266/acc.2023.00136. Epub 2023 Aug 21.

Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit

Louis Boutin  1   2 , Louis Morisson  1 , Florence Riché  1 , Romain Barthélémy  1 , Alexandre Mebazaa  1   2 , Philippe Soyer  2   3 , Benoit Gallix  4   5   6 , Anthony Dohan  2   3 , Benjamin G Chousterman  1   2
Affiliations

Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit

Louis Boutin et al. Acute Crit Care. 2023 Aug.

Abstract

Background: Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis.

Methods: This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times.

Results: Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43-0.54) and 0.51 (95% CI, 0.46-0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66-0.77) for elastic net and 0.69 (95% CI, 0.64-0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91-0.96) and 0.75 (95% CI, 0.70-0.80) for elastic net and random forest, respectively.

Conclusions: This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model.

Keywords: acute kidney injury; computed tomography; image processing; intensive care unit; sepsis.

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Conflict of interest statement

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

Figures

Figure 1.
Figure 1.
Study flow chart. CT: computed tomography.
Figure 2.
Figure 2.
Receiver operating characteristic (ROC) curve for in-intensive care unit mortality prediction using radiomic analysis alone (A), using radiomic analysis and Simplified Acute Physiology Score (SAPS) II (B), using SAPS II alone (C). AUC: area under the curve; CI: confidence interval.
Figure 3.
Figure 3.
Receiver operating characteristic (ROC) curve for acute kidney injury prediction using radiomic analysis alone (A), using radiomic analysis and Simplified Acute Physiology Score (SAPS) II (B), using SAPS II alone (C). AUC: area under the curve; CI: confidence interval.
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