title: Prediction of fracture in nursing home residents.
abstract: OBJECTIVE
  To investigate cross-validated methods of identifying patients at increased risk of fracture in nursing homes using readily available data.
  
  
  METHODS
  Prospective cohort study with 18 months of follow-up.
  
  
  METHODS
  Forty-seven randomly selected nursing homes in Maryland.
  
  
  METHODS
  One thousand four hundred twenty-seven white female nursing home residents aged 65 and older were followed for fracture for 18 months after baseline assessment.
  
  
  METHODS
  Fracture ascertained by physician note or x-ray from chart abstraction; demographic and baseline data extracted from the Minimum Data Set (MDS).
  
  
  RESULTS
  Exploratory analyses on a random subset (67%) of the data (development sample) identified variables that might be important in predicting subsequent fracture and included variables for how the resident moved between locations in her room or adjacent corridor (mobility), age, weight, height, independence in eating and dressing, urinary incontinence, resistance to care, falls in the previous 6 months, a dementia score, and other activities of daily living. A simple scoring algorithm derived from a subset of these MDS variables showed good sensitivity (.70) but low specificity (.39) in the random validation sample.
  
  
  CONCLUSIONS
  A scoring algorithm developed in more than 1,400 white females from 47 nursing homes in the state of Maryland shows high sensitivity for identifying women at increased risk for fracture and may be useful in targeting fracture prevention programs.
authors.affiliation: Department of Epidemiology, Merck Research Laboratories, Blue Bell, Pennsylvania 19422, USA. girman@merck.com
authors.forename: Cynthia J
authors.initials: CJ
authors.lastname: Girman
authors.forename: Julie M
authors.initials: JM
authors.lastname: Chandler
authors.forename: Sheryl I
authors.initials: SI
authors.lastname: Zimmerman
authors.forename: Allison R
authors.initials: AR
authors.lastname: Martin
authors.forename: William
authors.initials: W
authors.lastname: Hawkes
authors.forename: J Richard
authors.initials: JR
authors.lastname: Hebel
authors.forename: Philip D
authors.initials: PD
authors.lastname: Sloane
authors.forename: Jay
authors.initials: J
authors.lastname: Magaziner
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015519:Bone Density; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D005544:Forecasting; D050723:Fractures, Bone / Q000453:epidemiology / Q000517:prevention & control*; D006707:Homes for the Aged / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D009735:Nursing Homes / Q000706:statistics & numerical data*; D010024:Osteoporosis; D011446:Prospective Studies; D011897:Random Allocation; D012306:Risk; D012307:Risk Factors; D013997:Time Factors