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In JBMR plus ; h5-index 0.0

Although the WHO fracture risk algorithm (FRAX) is used to predict fracture, the utility of some simple machine-learning methods, such as classification and regression trees (CARTs) should be evaluated to determine their efficacy in fracture prediction. Follow-up time for the hip fracture analyses of 5977 community-dwelling American men aged ≥65 years old was truncated to 10 years. There were 172 (2.9%) men who had an incident nontraumatic hip fracture. The CARTs were developed using hip BMD and common clinical risk factors as follows: model 1 = using classification with continuous variables of age, total hip BMD, and femoral neck BMD, or together with common clinical risk factors; and model 2 = using classification with continuous variables of age, total hip BMD, femoral neck BMD, FRAX score, osteoporosis by T-score at the hip, and common clinical risk factors. The predictive performance of risk models derived from CARTs was compared with the basic classification of FRAX at 3% (basic model). From model 1, discriminators selected by CART were total hip BMD, age, and femoral neck BMD; no other clinical risk factors were selected. From model 2, discriminators selected by CART were FRAX score, femoral neck BMD, and age. Compared with the basic model using only a high-risk group by FRAX ≥3%, no significantly improved predictive performance was demonstrated by model 1 or model 2 as identified by CART with the area under the receiver-operating characteristic curve for each model of 0.714 (95% CI, 0.676 to 0.751) or 0.726 (95% CI, 0.690 to 0.762) versus 0.703 (95% CI, 0.667 to 0.740), respectively. The improved overall net reclassification improvement index was 0.02 (95% CI, -0.04 to 0.08) and 0.05 (95% CI, -0.01 to 0.10), respectively. Although a FRAX category is a good clinical indicator for hip fracture risk, a simple classification by age and BMD may provide an alternative way to estimate a clinical risk level of 3.0%. © 2019 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.

Su Yi, Kwok Timothy C Y, Cummings Steven R, Yip Benjamin H K, Cawthon Peggy M