Thomas R Radomski1, Xinhua Zhao2, Joseph T Hanlon3, Joshua M Thorpe4, Carolyn T Thorpe4, Jennifer G Naples5, Florentina E Sileanu2, John P Cashy2, Jennifer A Hale2, Maria K Mor2, Leslie R M Hausmann6, Julie M Donohue7, Katie J Suda8, Kevin T Stroupe9, Chester B Good10, Michael J Fine6, Walid F Gellad11. 1. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 230 McKee Place Suite 600, Pittsburgh, PA, 15213, USA. Electronic address: radomskitr@upmc.edu. 2. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA. 3. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA; Division of Geriatrics, Department of Medicine, University of Pittsburgh School of Medicine, 3471 5th Ave, Kaufmann Building Suite 500, Pittsburgh, PA, 15213, USA. 4. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA; Division of Pharmaceutical Outcomes and Policy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC, USA. 5. Division of Geriatrics, Department of Medicine, University of Pittsburgh School of Medicine, 3471 5th Ave, Kaufmann Building Suite 500, Pittsburgh, PA, 15213, USA. 6. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 230 McKee Place Suite 600, Pittsburgh, PA, 15213, USA. 7. Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, 130 De Soto Street, Pittsburgh, PA, 15261, USA. 8. Center of Innovation for Complex Chronic Healthcare, Edward Hines Jr. VA Hospital, PO Box 1033, 5000 S. 5th Ave, Hines, IL, USA; Department of Pharmacy Systems, Outcomes, and Policy, University of Illinois at Chicago College of Pharmacy, 833 S. Wood Street, Chicago, IL, 60612, USA. 9. Department of Pharmacy Systems, Outcomes, and Policy, University of Illinois at Chicago College of Pharmacy, 833 S. Wood Street, Chicago, IL, 60612, USA. 10. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 230 McKee Place Suite 600, Pittsburgh, PA, 15213, USA; Division of Pharmaceutical Outcomes and Policy, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, NC, USA. 11. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA, 15240, USA; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, 230 McKee Place Suite 600, Pittsburgh, PA, 15213, USA; Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, 130 De Soto Street, Pittsburgh, PA, 15261, USA.
Abstract
BACKGROUND: There is systemic undercoding of medical comorbidities within administrative claims in the Department of Veterans Affairs (VA). This leads to bias when applying claims-based risk adjustment indices to compare outcomes between VA and non-VA settings. Our objective was to compare the accuracy of a medication-based risk adjustment index (RxRisk-VM) to diagnostic claims-based indices for predicting mortality. METHODS: We modified the RxRisk-V index (RxRisk-VM) by incorporating VA and Medicare pharmacy and durable medical equipment claims in Veterans dually-enrolled in VA and Medicare in 2012. Using the concordance (C) statistic, we compared its accuracy in predicting 1 and 3-year all-cause mortality to the following models: demographics only, demographics plus prescription count, or demographics plus a diagnostic claims-based risk index (e.g., Charlson, Elixhauser, or Gagne). We also compared models containing demographics, RxRisk-VM, and a claims-based index. RESULTS: In our cohort of 271,184 dually-enrolled Veterans (mean age = 70.5 years, 96.1% male, 81.7% non-Hispanic white), RxRisk-VM (C = 0.773) exhibited greater accuracy in predicting 1-year mortality than demographics only (C = 0.716) or prescription counts (C = 0.744), but was less accurate than the Charlson (C = 0.794), Elixhauser (C = 0.80), or Gagne (C = 0.810) indices (all P < 0.001). Combining RxRisk-VM with claims-based indices enhanced its accuracy over each index alone (all models C ≥ 0.81). Relative model performance was similar for 3-year mortality. CONCLUSIONS: The RxRisk-VM index exhibited a high level of, but slightly less, accuracy in predicting mortality in comparison to claims-based risk indices. IMPLICATIONS: Its application may enhance the accuracy of studies examining VA and non-VA care and enable risk adjustment when diagnostic claims are not available or biased. LEVEL OF EVIDENCE: Level 3.
BACKGROUND: There is systemic undercoding of medical comorbidities within administrative claims in the Department of Veterans Affairs (VA). This leads to bias when applying claims-based risk adjustment indices to compare outcomes between VA and non-VA settings. Our objective was to compare the accuracy of a medication-based risk adjustment index (RxRisk-VM) to diagnostic claims-based indices for predicting mortality. METHODS: We modified the RxRisk-V index (RxRisk-VM) by incorporating VA and Medicare pharmacy and durable medical equipment claims in Veterans dually-enrolled in VA and Medicare in 2012. Using the concordance (C) statistic, we compared its accuracy in predicting 1 and 3-year all-cause mortality to the following models: demographics only, demographics plus prescription count, or demographics plus a diagnostic claims-based risk index (e.g., Charlson, Elixhauser, or Gagne). We also compared models containing demographics, RxRisk-VM, and a claims-based index. RESULTS: In our cohort of 271,184 dually-enrolled Veterans (mean age = 70.5 years, 96.1% male, 81.7% non-Hispanic white), RxRisk-VM (C = 0.773) exhibited greater accuracy in predicting 1-year mortality than demographics only (C = 0.716) or prescription counts (C = 0.744), but was less accurate than the Charlson (C = 0.794), Elixhauser (C = 0.80), or Gagne (C = 0.810) indices (all P < 0.001). Combining RxRisk-VM with claims-based indices enhanced its accuracy over each index alone (all models C ≥ 0.81). Relative model performance was similar for 3-year mortality. CONCLUSIONS: The RxRisk-VM index exhibited a high level of, but slightly less, accuracy in predicting mortality in comparison to claims-based risk indices. IMPLICATIONS: Its application may enhance the accuracy of studies examining VA and non-VA care and enable risk adjustment when diagnostic claims are not available or biased. LEVEL OF EVIDENCE: Level 3.
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