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. 2024 Apr 18;14:8951. doi: 10.1038/s41598-024-59114-3

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© The Author(s) 2024

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/.

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AI-derived volume transient metrics. AI-derived volume transient features most relevant to MACE occurrence prediction, resulting from unsupervised learning (Vt_AI1, Vt_AI2, Vt_AI3 and Vt_AI5). The MACE (red, class 1) and No-MACE (blue, class 0) traces shown correspond to the 10^th and 90^th percentiles. This allows to visualize the pattern of change encoded by each of the unsupervised variables (RR-interval, passive vs active filling, etc.), as well as to describe how a representative MACE and No-MACE normalized volume transient would theoretically look like according to each of these four unsupervised variables. The P-value, re-substitution and leave-one-out AUCs are presented along each mode as MACE and No-MACE distributions, further stratified into infarct aetiology (STEMI and NSTEMI). This figure is available in video format, where the evolution from the MACE to the No MACE extremes is shown to better appreciate the pattern of change encoded in each of the VT_AI contraction modes (see Supplementary Video 2).