Abstract

The more than 300,000 hip fractures that occur each year in the United States carry high morbidity and mortality rates and are costly, but the ability to correctly identify patients at high risk would help to prevent these injuries. The investigators attempted to develop an algorithm with which to predict the 5-year risk of hip fracture in postmenopausal women. The basic study population included 93,676 postmenopausal women participating in the observational component of the Women’s Health Initiative, a longitudinal multiethnic trial. They were used to develop a predictive algorithm based on readily available clinical features. Factors that ostensibly predicted hip fracture then were validated in 68,132 women taking part in the clinical trial. Finally, the model was tested in 10,750 women whose bone mass density had been measured by dual-energy x-ray absorptiometry (DXA). During an average follow-up interval of 7.6 years, hip fractures occurred at an annualized rate of 0.16% in women included in the observational study. Those taking part in the clinical trial had hip fractures at a rate of 0.14% per year during an average follow-up of 8.0 years. The factors that predicted the occurrence of hip fracture within 5 years were age, self-reported health, body weight, height, race/ethnicity, self-reported physical activity, a history of fracture after age 54 years, hip fracture in a parent, current smoking, current steroid use, and treated diabetes. Receiver operating characteristic (ROC) curves showed that the algorithm had an area under the curve of 80% at a 95% confidence interval (CI) of 0.77% to 0.82% when tested in women participating in the clinical trial. A simplified point score was developed to express the likelihood of hip fracture. ROC curves comparing DXA scan predictions based on a 10% subset of the cohort and the algorithm in those participating in the clinical trial were similar. The respective areas under the ROC curve were 79% (95% CI, 73%–85%) and 71% (95% CI, 66%–76%).

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.