Adapting the Vulnerable Elders Survey-13 to Predict Mortality Using Responses to the Medicare Health Outcomes Survey

• Autores: Megan K. Beckett, Marc N. Elliott, Douglas Ritenour, Laura A. Giordano, Susan C. Grace, Rochelle Malinoff, Debra Saliba
• Localización: Journal of the American Geriatrics Society, ISSN 0002-8614, Vol. 65, Nº. 5, 2017, págs. 1051-1055
• Idioma: inglés
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• Resumen

  • Objectives To use items from the Medicare Health Outcomes Survey (HOS) to adapt or validate a simple method for identifying community-dwelling older adults at greater risk of death and to extend the method to identify a very high-risk group.

    Design Analysis of longitudinal data.

    Setting National sample of beneficiaries from Medicare Advantage plans with 500 or more enrollees.

    Participants Medicare beneficiaries aged 65 and older responding to 2009 baseline and 2011 follow-up HOS (N = 238,687).

    Measurements Bivariate and multivariate analyses of the HOS; adaptation and validation of a previously validated Vulnerable Elders Survey-13 (VES-13) scoring system that uses age and self-reported function to predict mortality.

    Results A modified predictive model, that uses substitutes for several items in the previously validated VES-13, predicted 2-year mortality; 10.6% of those scoring 3 or more, and 2.4% of those scoring less than 3 died within 2 years (relative risk of death 4.4, similar to 4.2 for the original VES-13 sample), and 15.5% of those scoring 7 or more died within 2 years (relative risk of death (relative to scores <3) of 6.5). Sixteen percent of HOS beneficiaries were missing some data; 2-year mortality for those with missing items was 9.5%, versus 7.1% for those with no missing items (P < .001). Imputation of median values for missing VES-13 items results in valid predictions of mortality for those with partially missing data.

    Conclusion The VES-13 algorithm is robust to substitution of functional items and can be used to identify very high-risk older adults. Multiple imputation of missing items reduces loss-to-follow-up bias and increases sample size.