title: When should the doctor order a spine X-ray? Identifying vertebral fractures for osteoporosis care: results from the European Prospective Osteoporosis Study (EPOS).
abstract: Vertebral fractures are common but usually remain unrecognized in primary care. Data from 2908 women and 2653 men in the EPOS study were used to derive algorithms to indicate the need for a spine X-ray to identify a fracture using easily elicited determinants. At a sensitivity of 50% for identifying cases, the specificity was increased from 50% to 78% in women and from 50% to 72% in men compared with a random allocation of X-rays. Use of X-rays can be optimized by selecting patients at high risk using a short screening procedure.
  
  
  BACKGROUND
  Previous osteoporotic fracture is an independent risk factor for further fractures and an indication for treatment. Vertebral fractures are the most common osteoporotic fractures before age 75, accounting for 48% of all fractures in men and 39% in women over 50. They usually remain unrecognized, so many patients requiring treatment are denied it, doubling their risk of a further fracture. Our objective was to develop an efficient algorithm indicating the need for an X-ray.
  
  
  METHODS
  Data from 2908 women and 2653 men >or=50 years of age in the European Prospective Osteoporosis Study (EPOS) were analyzed. Lateral thoracic and lumbar spine radiographs were taken at baseline and at an average of 3.8 years later. Prevalent fractures were qualitatively diagnosed by an experienced radiologist. Fracture risk was modeled as a function of age, statural height loss since age 25, gender, and fracture history including limb fractures in the last 3 years using negative binomial regression. Receiver operating characteristic (ROC) curves were used to summarize a model's predictive ability, and a prediction algorithm was devised to identify those most likely to have a fracture.
  
  
  RESULTS
  In a multivariate model for women, the risk of prevalent vertebral fracture significantly increased with age (RR, 1.67 [95% CI, 1.46, 1.93] per decade), statural height loss (1.06, [1.03, 1.10] per centimeter decrease), self-reported history of spine fracture (7.52 [5.52, 10.23]), and history of other major fracture (1.83 [1.46, 2.28]). Higher body weight reduced risk (0.86 [0.79, 0.95] per 10-kg increase). In men, the respective RR estimates were as follows: age (1.32 [1.18, 1.49]); height loss (1.06 [1.04, 1.09]); self-reported spine fracture (5.05 [3.69, 6.90]); other major fracture (1.42 [1.12, 1.81]); and weight (0.86 [0.79, 0.94]). Using algorithms based on these easily elicited determinants, specificity was increased from 50% to 78% in women and from 50% to 72% in men at a sensitivity of 50% compared with a random allocation of X-rays. At a sensitivity of 75%, the specificity was 50% in women and 40% in men. Inclusion of hip BMD (femoral neck or trochanter), measured in 1360 women and 1046 men, significantly improved the area under the ROC curves by 4% in women (p < 0.002) but not in men (p > 0.350). Spine BMD, measured in 982 women and 847 men, produced a significant 5% AUC improvement in women (p = 0.007) but not in men (p = 0.554).
  
  
  CONCLUSIONS
  A woman 65 years of age with one vertebral fracture has a one in four chance of another fracture over 5 years, which can be reduced to one in eight by treatment. Positive treatment decisions are often contingent on identifying a vertebral fracture. Selective use of lateral vertebral X-rays can be optimized using a 2-minute screening procedure administered by a nurse.
authors.affiliation: Department of Medicine, Institute of Public Health, University of Cambridge, Cambridge, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Kaptoge
authors.forename: Gabi
authors.initials: G
authors.lastname: Armbrecht
authors.forename: Dieter
authors.initials: D
authors.lastname: Felsenberg
authors.forename: Mark
authors.initials: M
authors.lastname: Lunt
authors.forename: Terence W
authors.initials: TW
authors.lastname: O'Neill
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Silman
authors.forename: Jonathan
authors.initials: J
authors.lastname: Reeve
mesh_terms: D000367:Age Factors; D000368:Aged; D000465:Algorithms; D015519:Bone Density; D005060:Europe; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008159:Lumbar Vertebrae / Q000473:pathology; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D015999:Multivariate Analysis; D010024:Osteoporosis / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D012372:ROC Curve; D011859:Radiography / Q000379:methods* / Q000706:statistics & numerical data*; D012306:Risk; D012680:Sensitivity and Specificity; D012737:Sex Factors; D016103:Spinal Fractures / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D011795:Surveys and Questionnaires; D013997:Time Factors; D014965:X-Rays