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KMID : 1144420230380030343
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Acute and Critical Care
2023 Volume.38 No. 3 p.343 ~ p.352
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Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit
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Louis Boutin
Louis Morisson
Florence Riche
Romain Barthelemy
Alexandre Mebazaa
Philippe Soyer
Benoit Gallix
Anthony Dohan
Benjamin G Chousterman
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Abstract
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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.
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KEYWORD
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acute kidney injury, computed tomography, image processing, intensive care unit, sepsis
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