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Predicting 12-Month Mortality for Persons With Dementia

Departments of ¹ Social &
Behavioral Science, ² Medicine
⁴ Psychiatry, University of California, San Francisco.
³ Clay Software and Statistics, Ashland, Oregon.

Address correspondence to Robert Newcomer, Department of Social & Behavioral Sciences, University of California, 3333 California Street, Suite 455, San Francisco, CA 94118. E-mail: rjn{at}itsa.ucsf.edu

Objectives. We develop and test a model of 12-month mortality among persons () with organic dementia.

Methods. Data are from caregiver interviews and claims records for persons enrolled in the Medicare Alzheimer's Disease Demonstration Evaluation. Information covers the year prior to enrollment through 36 months. We used Proportional hazards models to predict time to death. We estimated two starting points: first, the date of randomization, and second, the date of maximum difficulty in performing two or more activities of daily living (ADLs).

Results. The final model includes age, gender, ADL difficulty, medical conditions, prior year hospitalizations, and whether a daughter was the primary caregiver. We combined hazard ratios to produce a cumulative mortality risk score. Model discrimination is reasonable for both models (c statistics of.72 and.69, respectively), and calibration tests were nonsignificant.

Discussion. The model's efficiency, as measured by the ratio of false positives (those predicted to die, but who lived) to true positives (those predicted to die and who did die) ranged from fewer than 1:1 to more than 4:1 as the model's sensitivity increased. This ratio was lower in the two or more ADL difficulty model. A validation test of the prediction model found comparable sensitivity and specificity (c statistic of.69) to the reference model.

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