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.