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Validation of a 5-Year Mortality Prediction Model among U.S. Medicare Beneficiaries
Rachael K Ross1, Tzy-Mey Kuo2, Michael Webster-Clark1
1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Background/Objectives:
A claims-based model predicting 5-year mortality (Lund-Lewis) was developed in a 2008 cohort of North Carolina (NC) Medicare beneficiaries and included indicators of comorbid conditions, frailty, disability, and functional impairment. The objective of this study was to validate the Lund-Lewis model externally within a nationwide sample of Medicare beneficiaries.
Design:
Retrospective validation study.
Setting:
U.S. Medicare population.
Participants:
From a random sample of Medicare beneficiaries, we created four annual cohorts from 2008 to 2011 of individuals aged 66 and older with an office visit in that year. The annual cohorts ranged from 1.13 to 1.18 million beneficiaries.
Measurements:
The outcome was 5-year all-cause mortality. We assessed clinical indicators in the 12 months before the qualifying office visit and estimated predicted 5-year mortality for each beneficiary in the nationwide sample by applying estimates derived in the original NC cohort. Model performance was assessed by quantifying discrimination, calibration, and reclassification metrics compared with a model fit on a comorbidity score.
Results:
Across the annual cohorts, 5-year mortality ranged from 24.4% to 25.5%. The model had strong discrimination (C-statistics ranged across cohorts from .823 to .826). Reclassification measures showed improvement over a comorbidity score model for beneficiaries who died but reduced performance among beneficiaries who survived. The calibration slope ranged from .83 to .86; the model generally predicted a higher risk than observed.
Conclusion:
The Lund-Lewis model showed strong and consistent discrimination in a national U.S. Medicare sample, although calibration indicated slight overfitting. Future work should investigate methods for improving model calibration and evaluating performance within specific disease settings.
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