Do focus on falls when assessing fracture risk
The Crystal Bone (Amgen) novel algorithm predicted 2-year risk of osteoporotic fractures in a large dataset with an accuracy that was consistent with FRAX 10-year risk predictions, researchers report.
The algorithm was built using machine learning and artificial intelligence to predict fracture risk based on International Classification of Diseases (ICD) codes, as described in an article published in the Journal of Medical Internet Research.
The current validation study was presented September 9 as a poster at the annual meeting of the American Society for Bone and Mineral Research.
The scientists validated the algorithm in more than 100,000 patients aged 50 and older (that is, at risk of fracture) who were part of the Reliant Medical Group dataset (a subset of Optum Care).
Importantly, the algorithm predicted increased fracture in many patients who did not have a diagnosis of osteoporosis.
The next steps are validation in other datasets to support the generalizability of Crystal Bone across U.S. health care systems, Elinor Mody, MD, Reliant Medical Group, and colleagues report.
“Implementation research, in which patients identified by Crystal Bone undergo a bone health assessment and receive ongoing management, will help inform the clinical utility of this novel algorithm,” they conclude.
At the poster session, Tina Kelley, Optum Life Sciences, explained: “It’s a screening tool that says: ‘These are your patients that maybe you should spend a little extra time with, ask a few extra questions.’ ”
However, further study is needed before it should be used in clinical practice, she emphasized to this news organization.
‘A very useful advance’ but needs further validation
Invited to comment, Peter R. Ebeling, MD, outgoing president of the ASBMR, noted that “many clinicians now use FRAX to calculate absolute fracture risk and select patients who should initiate anti-osteoporosis drugs.”
With FRAX, clinicians input a patient’s age, sex, weight, height, previous fracture, [history of] parent with fractured hip, current smoking status, glucocorticoids, rheumatoid arthritis, secondary osteoporosis, alcohol (3 units/day or more), and bone mineral density (by DXA at the femoral neck) into the tool, to obtain a 10-year probability of fracture.
“Crystal Bone takes a different approach,” Dr. Ebeling, from Monash University, Melbourne, who was not involved with the research but who disclosed receiving funding from Amgen, told this news organization in an email.
The algorithm uses electronic health records (EHRs) to identify patients who are likely to have a fracture within the next 2 years, he explained, based on diagnoses and medications associated with osteoporosis and fractures. These include ICD-10 codes for fractures at various sites and secondary causes of osteoporosis (such as rheumatoid and other inflammatory arthritis, chronic obstructive pulmonary disease, asthma, celiac disease, and inflammatory bowel disease).
“This is a very useful advance,” Dr. Ebeling summarized, “in that it would alert the clinician to patients in their practice who have a high fracture risk and need to be investigated for osteoporosis and initiated on treatment. Otherwise, the patients would be missed, as currently often occurs.”
“It would need to be adaptable to other [EMR] systems and to be validated in a large separate population to be ready to enter clinical practice,” he said, “but these data look very promising with a good [positive predictive value (PPV)].”
Similarly, Juliet Compston, MD, said: “It provides a novel, fully automated approach to population-based screening for osteoporosis using EHRs to identify people at high imminent risk of fracture.”
Dr. Compston, emeritus professor of bone medicine, University of Cambridge, England, who was not involved with the research but who also disclosed being a consultant for Amgen, selected the study as one of the top clinical science highlights abstracts at the meeting.
“The algorithm looks at ICD codes for previous history of fracture, medications that have adverse effects on bone – for example glucocorticoids, aromatase inhibitors, and anti-androgens – as well as chronic diseases that increase the risk of fracture,” she explained.
“FRAX is the most commonly used tool to estimate fracture probability in clinical practice and to guide treatment decisions,” she noted. However, “currently it requires human input of data into the FRAX website and is generally only performed on individuals who are selected on the basis of clinical risk factors.”
“The Crystal Bone algorithm offers the potential for fully automated population-based screening in older adults to identify those at high risk of fracture, for whom effective therapies are available to reduce fracture risk,” she summarized.
“It needs further validation,” she noted, “and implementation into clinical practice requires the availability of high-quality EHRs.”