OBJECTIVES: To test the validity of using administrative data to identify patients with osteoporosis or low bone mineral density (BMD) and high risk for osteoporotic fractures. STUDY DESIGN: We conducted a retrospective cohort study. METHODS: We analyzed data from a managed care plan in Massachusetts. We developed 6 case-identification algorithms based on number of osteoporosis (OP) diagnoses, clinical setting of the OP diagnosis, timing of the OP diagnosis relative to BMD test, and clinical fracture risk factors adapted from the World Health Organization Fracture Risk Assessment Tool. We validated the algorithms against BMD results and calculated sensitivity, specificity, and positive predictive value (PPV) against 2 diagnostic criteria (T-score ≤--2.5 and T-score ≤--2.0). RESULTS: When compared against the first criterion (T-score ≤--2.5), the sensitivity of algorithm (35% to 80%), specificity (65% to 93%), PPV (44% to 63%), and adding fracture risk factors did not improve case identification. When compared against the expanded criterion (T-score ≤--2.0), we found the sensitivity of the algorithms ranged from 23% to 63%, specificity from 72% to 95%, and PPV from 67% to 83%. Including fracture risk in the expanded OP criterion improved case identification, and the algorithms achieved the highest PPV: 70% to 85%. CONCLUSIONS: Identifying patients with OP or low BMD and high risk for osteoporotic fractures is possible in administrative data if using information about both OP diagnoses and fracture risk profile.
OBJECTIVES: To test the validity of using administrative data to identify patients with osteoporosis or low bone mineral density (BMD) and high risk for osteoporotic fractures. STUDY DESIGN: We conducted a retrospective cohort study. METHODS: We analyzed data from a managed care plan in Massachusetts. We developed 6 case-identification algorithms based on number of osteoporosis (OP) diagnoses, clinical setting of the OP diagnosis, timing of the OP diagnosis relative to BMD test, and clinical fracture risk factors adapted from the World Health Organization Fracture Risk Assessment Tool. We validated the algorithms against BMD results and calculated sensitivity, specificity, and positive predictive value (PPV) against 2 diagnostic criteria (T-score ≤--2.5 and T-score ≤--2.0). RESULTS: When compared against the first criterion (T-score ≤--2.5), the sensitivity of algorithm (35% to 80%), specificity (65% to 93%), PPV (44% to 63%), and adding fracture risk factors did not improve case identification. When compared against the expanded criterion (T-score ≤--2.0), we found the sensitivity of the algorithms ranged from 23% to 63%, specificity from 72% to 95%, and PPV from 67% to 83%. Including fracture risk in the expanded OP criterion improved case identification, and the algorithms achieved the highest PPV: 70% to 85%. CONCLUSIONS: Identifying patients with OP or low BMD and high risk for osteoporotic fractures is possible in administrative data if using information about both OP diagnoses and fracture risk profile.
Authors: John A Kanis; Anders Oden; Helena Johansson; Fredrik Borgström; Oskar Ström; Eugene McCloskey Journal: Bone Date: 2009-02-03 Impact factor: 4.398
Authors: Lisa M Lix; Marina S Yogendran; William D Leslie; Souradet Y Shaw; Richard Baumgartner; Christopher Bowman; Colleen Metge; Abba Gumel; Janet Hux; Robert C James Journal: J Clin Epidemiol Date: 2008-07-10 Impact factor: 6.437
Authors: E S Siris; P D Miller; E Barrett-Connor; K G Faulkner; L E Wehren; T A Abbott; M L Berger; A C Santora; L M Sherwood Journal: JAMA Date: 2001-12-12 Impact factor: 56.272
Authors: S Patel; J T Kwan; E McCloskey; G McGee; G Thomas; D Johnson; R Wills; L Ogunremi; J Barron Journal: J Bone Miner Res Date: 2001-10 Impact factor: 6.741