TriAxia Health is partnering with three academic medical centers to build a large database aiming for a better understanding of rare pulmonary diseases, beginning with pulmonary arterial hypertension (PAH).
To be meaningful, medical research often requires very large quantities of data. Bigger datasets give a study greater statistical power; essentially, with more data on which to base conclusions, researchers can be more certain that a given conclusion is right.
This kind of research can be done at large single institutions. But doing it at a huge scale is inherently complex, and all data included is subjected to possible confounders (by virtue of all people being treated at the same place).
The new initiative hopes to overcome these challenges by leveraging data collected from three large medical institutions associated with academic centers: Brigham and Women’s Hospital (associated with Harvard Medical School), UPMC (associated with the University of Pittsburgh), and Banner Health (associated with the University of Arizona Health Sciences).
“The only way to effectively understand rare disease patients is to aggregate their clinical data from multiple hospitals, since each hospital provides care differently, and no one hospital has enough of these patients to power research,” Simon Kennedy, chief executive officer of TriAxia Health, said in a press release.
The company and centers aim to “build the largest, most comprehensive global data set ever for these diseases, combining both clinical and genomic data, to ensure that patients living with rare diseases receive the most effective treatment, and to enable new treatments by providing new insights about rare diseases,” Kennedy added.
The initiative will combine clinical data collected from these hospitals with sequencing data, at the level of both DNA and RNA, in the hope of better understanding rare pulmonary diseases like PAH, and ultimately find better ways to care for people with these conditions.
All data analyzed will be de-identified (anonymous), and patients will need to provide their consent before their data is included in the system.
“By combining clinical and molecular data at a patient level, from multiple different care settings, and leveraging the latest in machine learning and artificial intelligence, we hope to provide significant new understanding of which treatments are most effective for which patients, and why,” said Aaron Waxman, MD, PhD, a physician at Brigham and Women’s Hospital, and chair of a scientific advisory board at TriAxia Health.
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