New Algorithms Help Predict Osteoporotic Fracture Risk
QFractureScores may identify patients at high risk of fracture within general practice setting
FRIDAY, Nov. 20 (HealthDay News) -- Two new algorithms, QFractureScores, may accurately predict fracture risk without laboratory measurements, and may be suitable for use in both clinical settings and for self assessment, according to a U.K. study published online Nov. 19 in BMJ.
Julia Hippisley-Cox, M.D., of the University of Nottingham in the United Kingdom, and a colleague collected data from 357 general practices to develop the scores, and from 178 practices to validate the scores.
The researchers found that the QFractureScores showed good discrimination, as these algorithms were able to differentiate between subjects who did and did not subsequently develop a fracture. They were also able to explain more than 60 percent of the variation for hip fracture. In addition, QFractureScores improved discrimination compared with the fracture risk assessment (FRAX) algorithm for hip fracture. In their analysis, the D statistic, a measure of discrimination where higher values indicate better discrimination, had values that were 0.11 higher in women and 0.17 higher in men.
"The validation statistics for the hip fracture algorithm suggest that the models are likely to be at least as effective at identifying patients at high risk of hip fracture within primary care as the FRAX algorithm," the authors conclude.
The study was funded by the medical director of EMIS. One of the authors reported a financial relationship with EMIS and a software company. Another author serves as a consultant statistician for the same software company.