Identification of cognitively impaired patients at risk for development of Alzheimer’s disease dementia: an analysis of US Medicare claims data

ABSTRACT

Background

Identifying factors associated with transitioning from mild cognitive impairment (MCI) to dementia due to Alzheimer’s disease (AD dementia) or dementia due to any cause (all-cause dementia) may inform economic assessments of disease and early care planning.

Research Design and Methods

A multivariate logistic regression approach identified potential predictors of progression to AD dementia or all-cause dementia in individuals with MCI or cognitive impairment (CI). Eligible patients and variables of interest were identified using claims data from the Medicare Advantage Patient Database, by Optum.

Results

Predictors of an AD dementia diagnosis included age (odds ratio [OR], 1.71) and use of antipsychotics (OR, 2.50) and hypertension medication (OR, 1.25). Medication use for comorbid conditions was a better indicator of risk than comorbidity coding. Diagnosis of CI by a neurologist increased the odds of an AD dementia diagnosis. Possible protective factors for progression included the use of anxiolytics (OR, 0.76), inpatient status at time of diagnosis (OR, 0.49), and a history of stroke (OR, 0.87). None of these factors differentiated AD dementia from all-cause dementia.

Conclusions

Identifying patients at risk for AD dementia allows for improved system-level planning to guide policy and optimize economic and clinical outcomes for patients, caregivers, and society.

Keywords:

• Alzheimer’s disease
• cognitive impairment
• disease progression
• mild cognitive impairment
• risk factors

Acknowledgments

The authors thank John McAna (Jefferson College of Population Health, Thomas Jefferson University) for critical input on the manuscript development. Writing and editorial support, under direction of the authors, was provided by Katie Partrick at MediTech Media and was funded by Biogen. Permission for adaptation of tables and figures was granted by the copyright holder, Michele Potashman.

Author contributions

Conception and design: M Potashman and R Stefanacci

Analysis and interpretation of data: Q Hou, M Potashman, J Zhou

Critical revision of the paper for intellectual content: Q Hou, B Parcher, M Potashman, R Stefanacci, and J Zhou

All authors agree to be accountable for all aspects of this work and approve the final version of the manuscript to be published.

All authors agree to ensure that questions related to the accuracy or integrity of this work are appropriately investigated and resolved and that the resolution is documented in the literature.

Declaration of interest

M Potashman, B Parcher, and J Zhou were employees of Biogen at the time of this study. Q Hou is an employee and shareholder of Biogen. R Stefanacci is employed by both Jefferson College of Population Health, Thomas Jefferson University and EVERSANA, although in neither role did R Stefanacci receive compensation for this work. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

This work was supported by Biogen and by Jefferson College of Population Health, Thomas Jefferson University .