An algorithm had moderate sensitivity for identifying older women in nursing homes at risk of fracture
QUESTION: Does an algorithm composed of routinely collected baseline data identify older women in nursing homes at increased risk of fracture?
18 month follow up of a cohort of nursing home residents divided into derivation and validation samples.
A stratified random sample of 47 long term nursing facilities in Maryland, USA.
1427 white women living in nursing homes who were ≥65 years of age (mean age 85 y), had no terminal cancer or bone metastases, were not comatose, had ≥1 wrist or forearm free of prosthetic implants and open skin lesions, were not admitted for rehabilitation only, and were able to have bone mineral density (BMD) measurements.
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 …