JAMA: Amyloid PET changed clinical management in 60% of patients with dementia

Amyloid PET imaging greatly influenced the clinical management of patients with mild cognitive impairment (MCI) and dementia, according to the first phase of a multicenter trial published April 2 in JAMA. The findings may have important implications for diagnosing and managing Alzheimer’s disease.

The first phase of the Imaging Dementia—Evidence for Amyloid Scanning (IDEAS) study recruited more than 11,000 Medicare beneficiaries (mean age 75 years) with MCI or dementia. Patients underwent amyloid PET at 343 imaging centers across the U.S., nearly 950 dementia specialists were involved. To determine the effectiveness of scanning, researchers compared the pre-PET treatment plan with a plan developed 90 days after imaging.

Overall, the pre-scan plan changed 90 days after amyloid PET in 60 percent of those with MCI and 63.5 percent of patients with dementia. That mark is more than double the number the researchers predicted, according to the study.

“We are impressed by the magnitude of these results, which make it clear that amyloid PET imaging can have a major impact on how we diagnose and care for patients with Alzheimer’s disease and other forms of cognitive decline,” said study lead author and principal investigator Gil Rabinovici, MD, distinguished professor of Neurology at the UCSF Memory and Aging Center, in a prepared statement.

Launched in 2016, the IDEAS study enrolled a total of 16,000 Medicare beneficiaries. For this initial phase, the primary endpoint was how physicians altered participant’s medication prescriptions and counseling about safety and future planning. Rabinovici et al. found in MCI patients with significant amyloid deposits, physicians were twice as likely to prescribe Alzheimer’s drugs after PET imaging. In those with dementia, prescriptions rose from 63 percent pre-PET to 91 percent following amyloid imaging.

For the secondary endpoint the scientists determined if PET imaging results caused physicians to change the patient’s diagnosis. The authors found that diagnoses changed from Alzheimer’s disease to non-Alzheimer’s in 25 percent of patients, and from non-Alzheimer’s to Alzheimer’s in 11 percent of participants.

Additionally, and “critical to these trial’s success,” is the fact that, based on imaging results, physicians were able to ensure that 93 percent of patients referred to Alzheimer’s trials were amyloid-positive.

“These results present highly credible, large-scale evidence that amyloid PET imaging can be a powerful tool to improve the accuracy of Alzheimer’s diagnosis and lead to better medical management, especially in difficult-to-diagnose cases,” said Maria C. Carrillo, PhD, Alzheimer’s Association chief science officer and a co-author of the study, in the same statement. “It is important that amyloid PET imaging be more broadly accessible to those who need it.”

Researchers are currently involved in phase two of the IDEAS study, which is examining how amyloid PET scans affect health outcomes after a scan is completed. These findings are expected to be published in 2020.

The study was managed by the American College of Radiology and led by researchers at the Alzheimer’s Association UC San Francisco, Brown University School of Public Health, Virginia Commonwealth University School of Public Health, Washington University School of Medicine in St. Louis, UC Davis School of Medicine, and the Kaiser Permanente Division of Research.

""

Matt joined Chicago’s TriMed team in 2018 covering all areas of health imaging after two years reporting on the hospital field. He holds a bachelor’s in English from UIC, and enjoys a good cup of coffee and an interesting documentary.

Around the web

The new technology shows early potential to make a significant impact on imaging workflows and patient care. 

Richard Heller III, MD, RSNA board member and senior VP of policy at Radiology Partners, offers an overview of policies in Congress that are directly impacting imaging.
 

The two companies aim to improve patient access to high-quality MRI scans by combining their artificial intelligence capabilities.