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. 2022 Jun 15;12:9961. doi: 10.1038/s41598-022-14000-8

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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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Simultaneous quantification of mechanical dyssynchrony and the dyssynchrony index. Standard deviation of the time to peak strain (TPS-SD) plotted against the dyssynchrony index. The marker styles represent different patients, 1 and 2 represent the ventricular stimulations denoted in Table 1. Green shaded area and blue vertical dashed line indicate, respectively, the range (0.519–0.657) and average (0.588) of the optimal cut-off values determined by using a logistic regression analysis (see the Methods section, subsection Statistical Methods and Fig. S5). Inset: Box plot showing TPS-SD for 10 patients (20 pacing configurations) grouped according to the average cut-off value for the dyssynchrony index (p = 0.00031). The figure was created using Python’s Matplotlib library (v.3.4.2.)³⁷.