Rare Daily Staff

The National Institutes of Health has provided a $4.7 million grant to researchers at the University of Pennsylvania’ Perelman School of Medicine and the University of Florida College to use artificial intelligence to identify patients at risk of developing different types of the rare conditions vasculitis and spondyloarthritis.

The researchers will use the four-year grant to develop a set of algorithms derived from information already available in patients’ electronic health records. They said the work could greatly increase the chance of patients being diagnosed sooner. The proposed machine learning algorithms will adaptively update their key parameters as more data are made available.

The efforts dubbed “PANDA,” an acronym for “Predictive Analytics via Networked Distributed Algorithms for multi-system diseases,” will pull data through Patient-Centered Clinical Research Networks (PCORnet), a national database including information from different health systems, adding up to more than 27 million patients. De-identified data from these patients, including lab test results, comorbid conditions, past treatments, and other commonly available information, will be used to create the algorithms. Once built, the researchers will test each algorithm’s predictive power across 10-plus health systems, and then following these tests, the methods the team develops will be shared and available to apply to other diseases.

“Despite the clear need to reduce the dangerous and costly delays in diagnosis, individual clinicians, especially in primary care, face important challenges,” Yong Chen, a professor of Biostatistics at Penn and one of the principal investigators on the project said.

As an example of the promise of the PANDA system, Chen pointed to granulomatosis with polyangiitis (GPA), one of the forms of vasculitis under study. GPA involves inflammation of many organs and can be very severe or even fatal. Mortality rates for patients with this condition remain high in the first year after diagnosis, and the correct diagnosis of this type of vasculitis, and all the other types, can be delayed by months or even years.

“An earlier diagnosis of any of the types of vasculitis and spondyloarhritis we’re working on leads to a much better prognosis and better clinical outcomes,” Peter Merkel, chief of Rheumatology and a professor of Medicine and Epidemiology at Penn and a principal investigator on the project said. “Even if we determine that a patient has just a 10 percent likelihood of developing one of these diseases, that is a much higher chance of a rare problem, and clinicians can keep that in mind and make better decisions for their patients.”

Among the challenges in diagnosis faced by clinicians and their patients are how rare diseases can camouflage themselves as other common diseases, a lack of access to data or other clinicians the patient works with, and, simply, a lack of familiarity with extremely uncommon conditions. An algorithm that automatically scans known information to identify the possibility of a disease like GPA could be lifesaving.

“The increasing availability of real-world data, such as electronic health records collected through routine care, provides a golden opportunity to generate real-world evidence to inform clinical decision-making,” Principal Investigator Jiang Bian, chief data scientist of the University of Florida Health system and a professor in the Health Outcomes & Biomedical Informatics at the University of Florida College of Medicine said. “Nevertheless, to leverage these large collections of real-world data, which are often distributed across multiple sites, novel distributed algorithms like PANDA are much needed.”

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