Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov 1;182(11):1161-1170.
doi: 10.1001/jamainternmed.2022.4326.

Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia

W James Deardorff  1   2 , Deborah E Barnes  3   4 , Sun Y Jeon  1   2 , W John Boscardin  1   2   4 , Kenneth M Langa  5   6   7   8 , Kenneth E Covinsky  1   9 , Susan L Mitchell  10   11 , Elizabeth L Whitlock  12 , Alexander K Smith  1   2 , Sei J Lee  1   2
Affiliations

Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia

W James Deardorff et al. JAMA Intern Med. .

Abstract

Importance: Estimating mortality risk in older adults with dementia is important for guiding decisions such as cancer screening, treatment of new and chronic medical conditions, and advance care planning.

Objective: To develop and externally validate a mortality prediction model in community-dwelling older adults with dementia.

Design, setting, and participants: This cohort study included community-dwelling participants (aged ≥65 years) in the Health and Retirement Study (HRS) from 1998 to 2016 (derivation cohort) and National Health and Aging Trends Study (NHATS) from 2011 to 2019 (validation cohort).

Exposures: Candidate predictors included demographics, behavioral/health factors, functional measures (eg, activities of daily living [ADL] and instrumental activities of daily living [IADL]), and chronic conditions.

Main outcomes and measures: The primary outcome was time to all-cause death. We used Cox proportional hazards regression with backward selection and multiple imputation for model development. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (plots of predicted and observed mortality).

Results: Of 4267 participants with probable dementia in HRS, the mean (SD) age was 82.2 (7.6) years, 2930 (survey-weighted 69.4%) were female, and 785 (survey-weighted 12.1%) identified as Black. Median (IQR) follow-up time was 3.9 (2.0-6.8) years, and 3466 (81.2%) participants died by end of follow-up. The final model included age, sex, body mass index, smoking status, ADL dependency count, IADL difficulty count, difficulty walking several blocks, participation in vigorous physical activity, and chronic conditions (cancer, heart disease, diabetes, lung disease). The optimism-corrected iAUC after bootstrap internal validation was 0.76 (95% CI, 0.75-0.76) with time-specific AUC of 0.73 (95% CI, 0.70-0.75) at 1 year, 0.75 (95% CI, 0.73-0.77) at 5 years, and 0.84 (95% CI, 0.82-0.85) at 10 years. On external validation in NHATS (n = 2404), AUC was 0.73 (95% CI, 0.70-0.76) at 1 year and 0.74 (95% CI, 0.71-0.76) at 5 years. Calibration plots suggested good calibration across the range of predicted risk from 1 to 10 years.

Conclusions and relevance: We developed and externally validated a mortality prediction model in community-dwelling older adults with dementia that showed good discrimination and calibration. The mortality risk estimates may help guide discussions regarding treatment decisions and advance care planning.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: Dr Deardorff reported grants from National Institute on Aging T32-AG000212 during the conduct of the study. Dr Langa reported personal fees from the University of California, San Francisco (UCSF) as a consultant for this project during the conduct of the study; grants from the National Institutes of Health (NIH)/National Institute on Aging (NIA) related to epidemiology of dementia; and grants from Alzheimer's Association outside the submitted work; and served as an expert witness for a legal case related to cognitive decline and decisional capacity. Dr Covinsky reported grants from NIA during the conduct of the study. Dr Whitlock reported grants from National Center for Advancing Translational Sciences (KL2TR001870) during the conduct of the study; grants from NIA (R03AG059822), grants from Foundation for Anesthesia Education and Research, grants from UCSF Department of Anesthesia & Perioperative Care, and nonfinancial support from NIA (P30AG044281) outside the submitted work. Dr Lee reported grants from NIA (R01AG057751) during the conduct of the study and grants from VA HSR&D IIR 15-434 outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Calibration Plots For the Mortality Prediction Model in the Health and Retirement Study (HRS) and National Health and Aging Trends Study (NHATS) Cohorts
The calibration plots at the (A) 1-, (B) 2-, (C) 5-, and (D) 10-year time points indicating the agreement the agreement between the predicted mortality risk using the prediction model and observed mortality at various time points. Individual dots represent deciles of predicted risk. Perfect predictions would be at the 45-degree line. Each plot also contains a density plot displaying the distribution of predicted risk in the individual cohort. Data from the HRS are presented as blue dots and blue shaded density plots, and data from the NHATS are presented as orange dots and orange shaded density plots. Calibration in NHATS was not assessed at 10 years because the duration of follow-up was not long enough.
Figure 2.
Figure 2.. Baseline Characteristics and Median Predicted Time to Death in Years of 10 Randomly Selected Individuals With Dementia From the Health and Retirement Study Within Each Decile of Predicted Risk
A, Baseline characteristics are displayed for 10 randomly selected individuals from the Health and Retirement Study in each decile of predicted risk. The color and shading indicate the direction and magnitude of the specific characteristic’s effect on mortality risk, respectively. For example, going from light to dark pink indicates increasing hazard of mortality, whereas gray indicates lower hazard of mortality. B, The median predicted time to death in years based on the individual’s baseline characteristics is displayed. The ends of the whiskers indicate the 25th and 75th percentile of the median predicted time to death. Although age has a strong prognostic effect, individuals may have high predicted mortality risk due to multiple other factors including functional impairments and comorbidities. ADL Indicates activities of daily living; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); IADL, instrumental activities of daily living.
See this image and copyright information in PMC

Comment in

References

    1. Liang CS, Li DJ, Yang FC, et al. . Mortality rates in Alzheimer’s disease and non-Alzheimer’s dementias: a systematic review and meta-analysis. Lancet Healthy Longev. 2021;2(8):e479-e488. doi:10.1016/S2666-7568(21)00140-9 - DOI - PubMed
    1. Ganguli M, Dodge HH, Shen C, Pandav RS, DeKosky ST. Alzheimer disease and mortality: a 15-year epidemiological study. Arch Neurol. 2005;62(5):779-784. doi:10.1001/archneur.62.5.779 - DOI - PubMed
    1. Larson EB, Shadlen MF, Wang L, et al. . Survival after initial diagnosis of Alzheimer disease. Ann Intern Med. 2004;140(7):501-509. doi:10.7326/0003-4819-140-7-200404060-00008 - DOI - PubMed
    1. 2022 Alzheimer’s disease facts and figures. Alzheimers Dement. 2022;18(4):700-789. doi:10.1002/alz.12638 - DOI - PubMed
    1. Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology. 2013;80(19):1778-1783. doi:10.1212/WNL.0b013e31828726f5 - DOI - PMC - PubMed

Publication types