Advanced illness index: Predictive modeling to stratify elders using self-report data

Kathleen K. Brody, Nancy A. Perrin, Richard Dellapenna

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Objectives: Develop a prediction model to identify persons who have an increased risk of dying within the next 36 months, in order to focus additional resources and assessment in areas related to advanced care planning. Design: Retrospective study with a 3-year observation period. Setting: Integrated, not-for-profit managed care organization. Participants: Beneficiaries aged 65-105 responding to an annual survey (n = 4888). Measurements: Survey instrument includes physical function, geriatric syndromes, health care utilization, special equipment use, self-care deficits, caregiving responsibilities, and general health problems. Results: An 11-variable model changed the baseline χ2 from 315.71 (df = 1) to 742.511 (df = 11). The percent of subjects correctly classified was 74.3% and the negative predictive value was 92.2%. Conclusion: Advanced Illness Index (All) model is stable. Characteristic variables used are not easily reversed: the 1997 cohort classified as at-risk consistently remained at risk or died in the subsequent years (1998, 92%; and 1999, 96%) and 92% of those not at-risk survived the next 36 months. Persons at high risk should at a minimum be made aware of the types of integrated home and community-based services available to them should it be needed. They also should be targeted for elicitation of treatment preferences, values, designation of health care proxy, planning, and advanced care directives.

Original languageEnglish (US)
Pages (from-to)1310-1319
Number of pages10
JournalJournal of palliative medicine
Issue number6
StatePublished - Dec 1 2006
Externally publishedYes

ASJC Scopus subject areas

  • Nursing(all)
  • Anesthesiology and Pain Medicine


Dive into the research topics of 'Advanced illness index: Predictive modeling to stratify elders using self-report data'. Together they form a unique fingerprint.

Cite this