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“People were still getting fractures.”
That was the problem faced by Sundeep Khosla, M.D., Mayo Clinic endocrinologist and osteoporosis expert. He and his team were troubled. Patients without a diagnosis of osteoporosis were arriving at the clinic with unexplained hip and spine fractures.
Osteoporosis is a chronic disease that causes gradual bone weakening. Low bone density is one factor used to identify osteoporosis, and the gold standard measurement for bone density is the dual X-ray absorptiometry (DXA) scan.
“A low DXA score, below a certain level, is defined as osteoporosis,” Dr. Khosla explains. “But for patients not quite in that zone but still at risk, DXA doesn’t pick them up. We needed a better identification method for fracture risk.”
The day a patient is diagnosed with osteoporosis is not the day that patient became ill. The disease process occurs over time, and presents a complex picture. Researchers have to determine how age, gender, and other diseases may affect bone remodeling. But to gather that information, they need comprehensive data that spans years.
Dr. Khosla had access to long-term patient data, but what he needed was a new and innovative way to assess fracture risk. Read the rest of the article on Discovery's Edge.
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