Clinical Prediction and Spatial Statistical Analysis of Ascending Thoracic Aortic Aneurysm Structure

This study presents an analysis of data from patients with ascending thoracic aortic aneurysms (ATAAs). Two databases of 87 patients were available: one containing clinical variables and the other consisting of measurements of the maximum diameter taken along the ascending aorta. For the clinical database, both a supervised and an unsupervised learning method were applied to explore patterns within the data. On the other hand, for the ascending aorta dataset, experimental variograms were calculated, from which key parameters of interest were extracted. These parameters were then analyzed over time to assess temporal patterns. This analysis aimed to assess the emergence of similar patterns or behaviour in patients with aneurysms of comparable sizes. Based on the analyses conducted, the clinical variables with the greatest importance in surgical decision-making were identified, while the spatial statistical analysis revealed patterns that may be related to elasticity, stiffness, or deformations of the aorta.

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Publication Details

Journal
Mathematical and Computational Applications
Published
2026-01-09
DOI
https://doi.org/10.3390/mca31010010
Primary Topic
Aortic aneurysm repair treatments
Type
article
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article

Clinical Prediction and Spatial Statistical Analysis of Ascending Thoracic Aortic Aneurysm Structure

Katalina Oviedo Rodríguez, Rodrigo Valente, António Cruz Tomás, José Xavier et al.
Mathematical and Computational Applications
Aortic aneurysm repair treatments
article

Clinical Prediction and Spatial Statistical Analysis of Ascending Thoracic Aortic Aneurysm Structure

Katalina Oviedo Rodríguez, Rodrigo Valente, António Cruz Tomás, José Xavier, Alda Carvalho
article en

Abstract

This study presents an analysis of data from patients with ascending thoracic aortic aneurysms (ATAAs). Two databases of 87 patients were available: one containing clinical variables and the other consisting of measurements of the maximum diameter taken along the ascending aorta. For the clinical database, both a supervised and an unsupervised learning method were applied to explore patterns within the data. On the other hand, for the ascending aorta dataset, experimental variograms were calculated, from which key parameters of interest were extracted. These parameters were then analyzed over time to assess temporal patterns. This analysis aimed to assess the emergence of similar patterns or behaviour in patients with aneurysms of comparable sizes. Based on the analyses conducted, the clinical variables with the greatest importance in surgical decision-making were identified, while the spatial statistical analysis revealed patterns that may be related to elasticity, stiffness, or deformations of the aorta.

Mathematical and Computational ApplicationsVol. 31(1)
University of Lisbon (PT), Universidad Nacional (CR), Universidade Aberta (PT), Universidade Politécnica de Lisboa (PT), Intelligent Systems Research (United States) (US), CUF Porto Hospital (PT), Hospital de Santa Marta (PT), Universidade Nova de Lisboa (PT)
Peace, Justice and strong institutions
Openalex Percentile: Top 94%
Aortic aneurysm repair treatments
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Clinical Prediction and Spatial Statistical Analysis of Ascending Thoracic Aortic Aneurysm Structure — Katalina Oviedo Rodríguez, Rodrigo Valente, et al. · Mathematical and Computational Applications (2026) | TGRS Research Map | TGRS