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. 2021 Jul 1;4:104. doi: 10.1038/s41746-021-00477-6

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© The Author(s) 2021

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Logistic regression where the predictors are baseline patient characteristics plus mortality risk trajectories (Predictable vs. Unpredictable), and the outcome is each of the end-of-life (EOL) metric. An odds ratio >1 indicates that patients assigned to the “Predictable” trajectory are more likely to have the corresponding EOL care compared to those assigned to the “Unpredictable” trajectory.