We identified 259 persons with a suspected VCF based on claims data. The mean ± standard deviation age was 62 ± 12 years, and 56% were women. Based upon patterns of health services utilization in the 12 months prior to the index date, they averaged 9.5 ± 9.9 physician visits per year. The mean comorbidity count was 1.9 ± 2.0. For 10 (4%) of the persons with VCF claims, there was no spinal imaging performed within ± 1 month of the VCF claim. Because there were no radiology records to review and confirm cases, we excluded these cases from analyses of prevalent VCFs. They were included in the incident VCF analysis and classified as “not confirmed”. For the remaining 249 individuals, we were able to obtain the desired medical and radiology records for 186 (75%) of persons. After medical record review, we confirmed 40 (22%) cases as prevalent VCFs and 63 (34%) as incident VCFs. For 25 (13%) of cases, we did not confirm any VCF. For the remaining 58 (31%) cases, we confirmed that there was a VCF, but the radiology and physician records were not able to evaluate whether the VCF was incident or prevalent; the timing of these VCFs was therefore classified as UTD. For the 161 confirmed VCFs, the type of imaging test used for VCF classification was MRI (33%), computerized tomogram scan (16%), bone scan (7%), and radiograph (45%).
shows the positive predictive value (PPV) of various claims algorithms that might be used to identify a VCF. As shown, a VCF claim of any type was able to identify a confirmed prevalent VCF with high PPV, but fewer than half of these claims identified an incident VCF. The PPVs of inpatient VCF claims were high but identified few VCFs. The PPV of an algorithm that required a radiologic imaging procedure followed by a physician claim for a VCF within 10 days was 59%. Shortening or lengthening this interval from 5 to 30 days produced similar results (data not shown). Additional variations that incorporated whether the patient had a prior claim for a VCF or a spine imaging test more than 30 days before the index date did not appreciably change our results and are not shown. Our preferred algorithm for an incident VCF combined a radiologic test followed within 10 days by a physician visit with a diagnosis of VCF, or a hospitalization with a VCF diagnosis in the primary position; the PPV for this algorithm was 61%.
Positive Predictive Value of Various Administrative Claims Data Patterns to Correctly Classify Confirmed Prevalent or Incident Vertebral Compression Fractures* (n = 259 suspected)
further describes the diagnostic properties of our preferred claims-based algorithm, varying our assumptions regarding the 58 fractures for which we could not classify acuity. Depending on how these fractures were handled, the PPVs ranged from 74% to 42%.
Table 2 Sensitivity Analysis showing the Sensitivity, Specificity, and Positive Predictive Value of a Claims-Based Algorithm to Correctly Classify Incident Vertebral Compression Fractures, with Variations Based on Re-Classifying the Fractures with Uncertain Acuity (more ...)
shows the effect of misclassifying VCFs on the RRRs that might be observed with use of an osteoporosis medication. Due to the misclassification of VCFs, all observed RRR were closer to the null compared to the true RRRs. As misclassification increased (i.e. the PPV decreased), the magnitude of the difference between the observed RRR and the true RRR also increased.
Table 3 Effect of Misclassifying Vertebral Compression Fractures on the Observed Relative Risk Reduction of Fracture associated with Exposure to a Hypothetical Osteoporosis Medication (from )