HSR&D Citation Abstracts
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Radomski TR, Zhao X, Hanlon JT, Thorpe JM, Thorpe CT, Naples JG, Sileanu FE, Cashy JP, Hale JA, Mor MK, Hausmann LRM, Donohue JM, Suda KJ, Stroupe KT, Good CB, Fine MJ, Gellad WF. Use of a medication-based risk adjustment index to predict mortality among veterans dually-enrolled in VA and medicare. Healthcare (Amsterdam, Netherlands). 2019 Apr 25.
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.
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.
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.
The RxRisk-VM index exhibited a high level of, but slightly less, accuracy in predicting mortality in comparison to claims-based risk indices.
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.
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