An algorithm had moderate sensitivity for identifying older women in nursing homes at risk of fracture.

Description of prediction guide The women’s most recent minimum data set (MDS) (collected on all nursing home residents in the US) was the primary source of information for the algorithm (total 75 variables). The data pertained to physical functioning and activities of daily living, ambulatory status, vision status, mood and behaviour patterns, incontinence, dementia, other health conditions, and use of restraints. Classification and regression tree (CART) methodology and logistic regression were used to identify variables that would predict fractures. An additive scoring algorithm was derived in a random 67% of the cohort using cut points suggested by the CART methodology and validated in the remainder of the cohort.