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. 2023 Dec 29;41(1-2):59–72. doi: 10.1089/neu.2023.0046

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© Aaron M. Gusdon et al., 2024; Published by Mary Ann Liebert, Inc.

This Open Access article is distributed under the terms of the Creative Commons License (CC-BY) (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

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FIG. 4. — Receiver operating curve (ROC) analysis. Machine learning models were developed in order to determine the prognostic value of plasma lipid metabolites to predict outcomes after mild traumatic brain injury (mTBI). Baseline models included age and sex. Models were developed adding each of the three metabolites found to be upregulated in subjects with good discharge outcomes and to have significant associations with outcomes in multivariable models. Areas under the curve (AUCs) relative to the baseline model are shown for predicting discharge outcomes (A). A model was also created including all three metabolites added to the baseline model to predict discharge outcomes (B). Only one metabolite (1-linoleoyl-GPC [18:2]) when added to the baseline model individually significantly increased the AUC to predict 6-month outcomes (C). Principal components (3) generated from 25 detected lysophosphlipid (LPL) metabolites were added to the baseline model to predict 6-month outcomes (D).