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The American Journal of Managed Care February 2012
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Identifying Patients With Osteoporosis or at Risk for Osteoporotic Fractures
Yong Chen, MD, PhD; Leslie R. Harrold, MD, MPH; Robert A. Yood, MD; Terry S. Field, DSc; and Becky A. Briesacher, PhD
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Identifying Patients With Osteoporosis or at Risk for Osteoporotic Fractures

Yong Chen, MD, PhD; Leslie R. Harrold, MD, MPH; Robert A. Yood, MD; Terry S. Field, DSc; and Becky A. Briesacher, PhD
Identifying patients with osteoporosis or low bone mineral density and high risk for osteoporotic fractures is possible in administrative data using osteoporosis diagnoses and fracture risk profile.
Table 4 shows the sensitivity, specificity, PPV, and NPV when the diagnostic criterion was a T-score less than or equal to –2.0 (scenarios 3 and 4). Compared with the statistics in scenario 1, sensitivity (ranging from 22.8% to 63.1%) decreased; specificity changed slightly (ranging from 71.5% to 95%); and PPV improved substantially, varying from 66.5% to 83.1% in scenario 3 (the total study population, n = 2520). After restricting to the population with at least 1 of the 5 FRAX risk factors we identified (scenario 4), we found that sensitivity was still lower than that in scenario 1 and specificity changed slightly. However, the PPV was the highest among all 4 scenarios, ranging from 70.6% to 85.0%.

DISCUSSION

To our knowledge, our study is the first to test the validity of identifying patients who have OP or who have low BMD (osteopenia) and additional risk factors for osteoporotic fractures, using algorithms applied to administrative data in a US population. We used the results of BMD tests as the diagnostic standard and assessed the performance of our algorithms using a strict OP criterion (T-score <–2.5) and an expanded criterion (T-score <–2.0) in a population with at least 1 of the 5 FRAX risk factors we were able to identify in administrative data. Our work is also unique in that we did not require information about use of OP medications, and thus our methods are suitable for identifying the at-risk patient population before OP intervention. However, it is possible prescription claims are the only source to identify OP for some patients. In our study, we found that 489 patients had a bisphosphonate prescription (alendronate or risedronate) before the BMD test. We conducted a sensitivity analysis incorporating 2 commonly used bisphosphonates, alendronate and risedronate, into our algorithms and found the results of case-identification remained similar (data not shown).

Our study is also unique in that we incorporated 5 CRFs or proxies of CRFs from the WHO FRAX tool (RA, previous fracture, alcohol use, smoking, and history of treatment with oral glucocorticoids) into the risk assessment. This expansion incorporates current clinical recommendations that treatment to prevent osteoporotic fractures may also be appropriate for patients with a T-score better than –2.5 if other risk factors are present.10,12 In fact, we found that FRAX information did improve predictive power when the diagnostic criterion was a T-score less than or equal to –2.0, but not for the scenarios where the diagnostic criterion was a T-score less than or equal to –2.5. This suggests that incorporating FRAX risk factors may improve more of the performance of case-identification algorithms for osteopenia rather than OP using administrative data. Furthermore, other predictors, such as age, may improve the performance of the study algorithms because older age is= associated with OP. However, when we stratified the sample by age (>65 or <65 years of age), we found the algorithms performed similarly between the stratified sample and unstratified sample. Therefore, we report the results without stratification. A possible reason for this observation is that we have an older population with median age of 70 years and 25th percentile of 63 years.

We show that our algorithms achieved reasonable sensitivity, specificity, PPV, and AUC (sensitivity ranged from 34.9% to 80.4%; specificity from 65.3% to 92.8%; PPV from 43.1% to 65.3%; and AUC from 0.636 to 0.737, as shown in Table 2). These ranges are consistent with those found in previous research using prescription drug data from Canada (sensitivity ranged from 34.1% to 93.3%; specificity from 50.8% to 91.4%; PPV from 48.1% to 64.9%; and AUC from 0.627 to 0.7547). However, we did not find a single algorithm that surpassed the others. Different algorithms offered different advantages, depending on the objectives for case identification. For instance, if we want to estimate the prevalence of OP in the population, our simplest algorithm of requiring only 1 diagnostic code from any setting (eg, algorithm 1, scenario 1) should provide a reasonable estimate due to high sensitivity. If, however, the purpose of case identification is to reliably find possible candidates for OP treatment, then algorithm 1 in scenario 4 is more appropriate due to the high sensitivity and PPV.

Our algorithms also have other implications. First, the algorithms can be used to identify untreated patients in claims data with OP or low BMD who may need OP intervention. Second, when we required a diagnosis code of OP after a DXA test for case identification, the specificity of the algorithms increased (scenario 1, algorithms 4-6 vs 1-3 [Table 3]). However, we found this resulted in decreased sensitivity. A potential reason for this may be that physicians failed to code for the OP diagnosis because it may be considered as having lower priority than other health conditions. Further studies are warranted to understand the under-documentation of OP.

We found a large number of patients with a diagnosis of OP in their administrative records who did not undergo a BMD test during the study observation period (n = 1987, in the Figure). When we compared the characteristics of patients with and without a BMD, we found that patients without a BMD test were generally older and had more FRAX risk factors than those who did not undergo a BMD test. Possible reasons for these differences may be: (1) patients without a BMD test were tested and diagnosed before the study period, or (2) testing for OP may not be a priority in patients who are older and already have clinical risk factors of osteoporotic fractures.

Our study has some limitations. First, although our estimates of sensitivity, specificity, and PPV are similar to a previous study in Canada, our samples are from 1 managed care plan in the United States and may not be generalizable to other populations.7 Second, we only included patients who had evidence of a DXA bone density test to confirm the presence of OP. Therefore, the study results cannot be generalized to patients who have OP but are never tested for it. Third, some of the FRAX risk factors were more amenable to being identified in administrative data than others. Thus, we underestimated some individual clinical risk factors in our population (eg, alcoholism as a proxy of alcohol use) and could not identify others (eg, height and weight). There also may be misclassification in the identification of RA patients since we only required 1 diagnostic code. We also had incomplete information on prior fractures because we only included data 1.5 years prior to the bone density test. Fourth, the baseline period prior to the bone density test varied from 1 year to 2 years, which might result in bias because patients with a longer baseline period would be more likely to have a risk factor identified. In order to address this issue, we conducted a sensitivity analysis using an equal 1-year baseline period for all patients. We found the number of patients with at least 1 fracture risk factor reduced from 631 to 541. However, classification statistics (sensitivity, specificity, and PPV) did not significantly change (data not shown). Fifth, we considered a woman with at least 1 risk factor as being at high risk for fracture. However, the FRAX includes 10 risk factors and a patient may need more than 1 to be considered as being at higher risk by FRAX calculation. Sixth, our algorithms could not overcome important problems noted in the course of conducting the study—that is, inconsistent use of an OP diagnosis in relation to BMD testing. For example, approximately 20% of the study population who underwent BMD testing and had a T-score less than or equal to –2.5 did not have any OP diagnosis in their administrative records, and 15% of the study population who had a T-score >–2.5 had an OP diagnosis after the BMD. Lastly, there was no diagnostic code in our data set for identifying low BMD (osteopenia).

CONCLUSIONS

In conclusion, using administrative data only, we were able to validly identify patients with OP or those with low BMD at increased risk for osteoporotic fracture using algorithms based on diagnoses and clinical risk factors only. Our algorithms may therefore be useful in estimating the prevalence of OP and identifying untreated patients who could benefit from OP treatment.

Author Affiliations: From University of Massachusetts Medical School (YC), Division of Geriatric Medicine (LRH, RY, TSF, BB), Fallon Clinic (RY), Worcester, MA.


Funding Source: Dr Briesacher was supported by Award Number K01AG031836 from the National Institute on Aging. Dr Harrold was supported by Grant Number K23AR053856 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, or the National Institutes of Health.


Author Disclosures: Dr Briesacher reports receiving grants and consultant fees from Novartis Pharmaceuticals. The other authors (YC, LRH, RY, TSF) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.


Authorship Information: Concept and design (YC, LRH, RY, TSF, BB); acquisition of data (YC, BB); analysis and interpretation of data (YC, LRH, RY, BB); drafting of the manuscript (YC, LRH, RY, BB); critical revision of the manuscript for important intellectual content (YC, LRH, RY, TSF, BB); statistical analysis (YC, BB); and supervision (TSF, BB).


Address correspondence to: Yong Chen, MD, PhD, University of Massachusetts Medical School, Division of Geriatric Medicine, Biotech 4, Ste 315, 377 Plantation St, Worcester, MA 01605. E-mail: yong.chen@umassmed.edu.
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