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. 2023 Jul 26;22:186. doi: 10.1186/s12933-023-01901-9

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — Comparison of discriminant ability of predictive models. To determine incremental discriminatory capacities of Global ΔCT-FFR in addition to clinical variables and atherosclerotic burden as predictor of MACE in diabetic patients with CT-FFR < 0.75, 3 analytic models were constructed as follows: model 1: Framingham risk score; model 2: model 1 + Leiden score; model 3: model 2 + HRP;model4: model3 + Global ΔCT-FFR. ROC receiver-operating characteristic, MACE major adverse cardiovascular events, NRI net reclassification improvement, HRP high risk plaque, GΔCT-FFR Global ΔCT-FFR (the sum of the trans-lesional CT-FFR gradient in all epicardial vessels)