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“Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute
This issue of the Yale Journal of Biology and Medicine (YJBM) focuses on Big Data and precision analytics in medical research. At the Aortic Institute at Yale New Haven Hospital, the vast majority of our investigations have emanated from our large, prospective clinical database of patients with thor...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
YJBM
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524815/ https://www.ncbi.nlm.nih.gov/pubmed/37780996 http://dx.doi.org/10.59249/LNDZ2964 |
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author | Zafar, Mohammad A. Ziganshin, Bulat A. Li, Yupeng Ostberg, Nicolai P. Rizzo, John A. Tranquilli, Maryann Mukherjee, Sandip K. Elefteriades, John A. |
author_facet | Zafar, Mohammad A. Ziganshin, Bulat A. Li, Yupeng Ostberg, Nicolai P. Rizzo, John A. Tranquilli, Maryann Mukherjee, Sandip K. Elefteriades, John A. |
author_sort | Zafar, Mohammad A. |
collection | PubMed |
description | This issue of the Yale Journal of Biology and Medicine (YJBM) focuses on Big Data and precision analytics in medical research. At the Aortic Institute at Yale New Haven Hospital, the vast majority of our investigations have emanated from our large, prospective clinical database of patients with thoracic aortic aneurysm (TAA), supplemented by ultra-large genetic sequencing files. Among the fundamental clinical and scientific discoveries enabled by application of advanced statistical and artificial intelligence techniques on these clinical and genetic databases are the following: From analysis of Traditional “Big Data” (Large data sets). 1. Ascending aortic aneurysms should be resected at 5 cm to prevent dissection and rupture. 2. Indexing aortic size to height improves aortic risk prognostication. 3. Aortic root dilatation is more malignant than mid-ascending aortic dilatation. 4. Ascending aortic aneurysm patients with bicuspid aortic valves do not carry the poorer prognosis previously postulated. 5. The descending and thoracoabdominal aorta are capable of rupture without dissection. 6. Female patients with TAA do more poorly than male patients. 7. Ascending aortic length is even better than aortic diameter at predicting dissection. 8. A “silver lining” of TAA disease is the profound, lifelong protection from atherosclerosis. From Modern “Big Data” Machine Learning/Artificial Intelligence analysis: 1. Machine learning models for TAA: outperforming traditional anatomic criteria. 2. Genetic testing for TAA and dissection and discovery of novel causative genes. 3. Phenotypic genetic characterization by Artificial Intelligence. 4. Panel of RNAs “detects” TAA. Such findings, based on (a) long-standing application of advanced conventional statistical analysis to large clinical data sets, and (b) recent application of advanced machine learning/artificial intelligence to large genetic data sets at the Yale Aortic Institute have advanced the diagnosis and medical and surgical treatment of TAA. |
format | Online Article Text |
id | pubmed-10524815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | YJBM |
record_format | MEDLINE/PubMed |
spelling | pubmed-105248152023-09-29 “Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute Zafar, Mohammad A. Ziganshin, Bulat A. Li, Yupeng Ostberg, Nicolai P. Rizzo, John A. Tranquilli, Maryann Mukherjee, Sandip K. Elefteriades, John A. Yale J Biol Med Perspectives This issue of the Yale Journal of Biology and Medicine (YJBM) focuses on Big Data and precision analytics in medical research. At the Aortic Institute at Yale New Haven Hospital, the vast majority of our investigations have emanated from our large, prospective clinical database of patients with thoracic aortic aneurysm (TAA), supplemented by ultra-large genetic sequencing files. Among the fundamental clinical and scientific discoveries enabled by application of advanced statistical and artificial intelligence techniques on these clinical and genetic databases are the following: From analysis of Traditional “Big Data” (Large data sets). 1. Ascending aortic aneurysms should be resected at 5 cm to prevent dissection and rupture. 2. Indexing aortic size to height improves aortic risk prognostication. 3. Aortic root dilatation is more malignant than mid-ascending aortic dilatation. 4. Ascending aortic aneurysm patients with bicuspid aortic valves do not carry the poorer prognosis previously postulated. 5. The descending and thoracoabdominal aorta are capable of rupture without dissection. 6. Female patients with TAA do more poorly than male patients. 7. Ascending aortic length is even better than aortic diameter at predicting dissection. 8. A “silver lining” of TAA disease is the profound, lifelong protection from atherosclerosis. From Modern “Big Data” Machine Learning/Artificial Intelligence analysis: 1. Machine learning models for TAA: outperforming traditional anatomic criteria. 2. Genetic testing for TAA and dissection and discovery of novel causative genes. 3. Phenotypic genetic characterization by Artificial Intelligence. 4. Panel of RNAs “detects” TAA. Such findings, based on (a) long-standing application of advanced conventional statistical analysis to large clinical data sets, and (b) recent application of advanced machine learning/artificial intelligence to large genetic data sets at the Yale Aortic Institute have advanced the diagnosis and medical and surgical treatment of TAA. YJBM 2023-09-29 /pmc/articles/PMC10524815/ /pubmed/37780996 http://dx.doi.org/10.59249/LNDZ2964 Text en Copyright ©2023, Yale Journal of Biology and Medicine https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons CC BY-NC license, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited. You may not use the material for commercial purposes. |
spellingShingle | Perspectives Zafar, Mohammad A. Ziganshin, Bulat A. Li, Yupeng Ostberg, Nicolai P. Rizzo, John A. Tranquilli, Maryann Mukherjee, Sandip K. Elefteriades, John A. “Big Data” Analyses Underlie Clinical Discoveries at the Aortic Institute |
title | “Big Data” Analyses Underlie Clinical Discoveries at the Aortic
Institute |
title_full | “Big Data” Analyses Underlie Clinical Discoveries at the Aortic
Institute |
title_fullStr | “Big Data” Analyses Underlie Clinical Discoveries at the Aortic
Institute |
title_full_unstemmed | “Big Data” Analyses Underlie Clinical Discoveries at the Aortic
Institute |
title_short | “Big Data” Analyses Underlie Clinical Discoveries at the Aortic
Institute |
title_sort | “big data” analyses underlie clinical discoveries at the aortic
institute |
topic | Perspectives |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524815/ https://www.ncbi.nlm.nih.gov/pubmed/37780996 http://dx.doi.org/10.59249/LNDZ2964 |
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