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. 2024 Oct 22;14:24810. doi: 10.1038/s41598-024-75556-1

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

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Fig. 4 — Comparison of results from primary and comorbidity-adjusted analyses. A comparison of results of primary analysis (before adjusting for comorbidities, black) and secondary analysis (after adjusting for comorbidities, dark gray). Light gray highlights the overlap of significant associations/predictors between the two approaches. a Total number of associations. b Number of predictors across evaluated conditions. c Number of condition-specific (Ndiagnoses = 1) and shared predictors (Ndiagnoses > 1). AFF—atrial fibrillation and flutter, HHD with HF—hypertensive heart disease with heart failure, CIHD—chronic ischemic heart disease, T2D—type 2 diabetes. Cox models for primary analysis were adjusted for age, body mass index, sex, smoking status. Cox models for sensitivity analysis were further adjusted by the first two principal components calculated from Hamming distances between comorbidity profiles.