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The age-specific comorbidity burden of mild cognitive impairment: a US claims database study
by
Mattke, Soeren
, Hampel, Harald
, Li, Gang
, Henley, David
, Cho, Min
, Batrla, Richard
, Devanarayan, Viswanath
, Ferrante, Matteo
, Galvin, James E.
, Boccato, Tommaso
, De Santi, Susan
, Frech, Feride
, Toschi, Nicola
in
Age Factors
/ Age factors in disease
/ Age group
/ Age groups
/ Aged
/ Alzheimer Disease - diagnosis
/ Alzheimer's disease
/ Biomedical and Life Sciences
/ Biomedicine
/ Cognition disorders
/ Cognitive ability
/ Cognitive Dysfunction - diagnosis
/ Cognitive Dysfunction - epidemiology
/ Comorbidity
/ Concussion
/ Dementia
/ Demographic aspects
/ Disease Progression
/ Electronic health records
/ Geriatric Psychiatry
/ Geriatrics/Gerontology
/ Gum disease
/ Humans
/ Ischemia
/ Machine learning
/ MCI comorbidities
/ MCI risk prediction
/ Medicare
/ Metabolism
/ Mild cognitive impairment
/ Mild cognitive impairment (MCI)
/ Neurological research
/ Neurology
/ Neurosciences
/ Retrospective Studies
/ Sensitivity and Specificity
/ Statistics
/ Substance abuse treatment
/ Traumatic brain injury
2023
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The age-specific comorbidity burden of mild cognitive impairment: a US claims database study
by
Mattke, Soeren
, Hampel, Harald
, Li, Gang
, Henley, David
, Cho, Min
, Batrla, Richard
, Devanarayan, Viswanath
, Ferrante, Matteo
, Galvin, James E.
, Boccato, Tommaso
, De Santi, Susan
, Frech, Feride
, Toschi, Nicola
in
Age Factors
/ Age factors in disease
/ Age group
/ Age groups
/ Aged
/ Alzheimer Disease - diagnosis
/ Alzheimer's disease
/ Biomedical and Life Sciences
/ Biomedicine
/ Cognition disorders
/ Cognitive ability
/ Cognitive Dysfunction - diagnosis
/ Cognitive Dysfunction - epidemiology
/ Comorbidity
/ Concussion
/ Dementia
/ Demographic aspects
/ Disease Progression
/ Electronic health records
/ Geriatric Psychiatry
/ Geriatrics/Gerontology
/ Gum disease
/ Humans
/ Ischemia
/ Machine learning
/ MCI comorbidities
/ MCI risk prediction
/ Medicare
/ Metabolism
/ Mild cognitive impairment
/ Mild cognitive impairment (MCI)
/ Neurological research
/ Neurology
/ Neurosciences
/ Retrospective Studies
/ Sensitivity and Specificity
/ Statistics
/ Substance abuse treatment
/ Traumatic brain injury
2023
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The age-specific comorbidity burden of mild cognitive impairment: a US claims database study
by
Mattke, Soeren
, Hampel, Harald
, Li, Gang
, Henley, David
, Cho, Min
, Batrla, Richard
, Devanarayan, Viswanath
, Ferrante, Matteo
, Galvin, James E.
, Boccato, Tommaso
, De Santi, Susan
, Frech, Feride
, Toschi, Nicola
in
Age Factors
/ Age factors in disease
/ Age group
/ Age groups
/ Aged
/ Alzheimer Disease - diagnosis
/ Alzheimer's disease
/ Biomedical and Life Sciences
/ Biomedicine
/ Cognition disorders
/ Cognitive ability
/ Cognitive Dysfunction - diagnosis
/ Cognitive Dysfunction - epidemiology
/ Comorbidity
/ Concussion
/ Dementia
/ Demographic aspects
/ Disease Progression
/ Electronic health records
/ Geriatric Psychiatry
/ Geriatrics/Gerontology
/ Gum disease
/ Humans
/ Ischemia
/ Machine learning
/ MCI comorbidities
/ MCI risk prediction
/ Medicare
/ Metabolism
/ Mild cognitive impairment
/ Mild cognitive impairment (MCI)
/ Neurological research
/ Neurology
/ Neurosciences
/ Retrospective Studies
/ Sensitivity and Specificity
/ Statistics
/ Substance abuse treatment
/ Traumatic brain injury
2023
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The age-specific comorbidity burden of mild cognitive impairment: a US claims database study
Journal Article
The age-specific comorbidity burden of mild cognitive impairment: a US claims database study
2023
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Overview
Background
Identifying individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer’s disease and related dementia disorders (ADRD) would facilitate the development of individualized prevention plans. We investigated the association between MCI and comorbidities of ADRD. We examined the predictive potential of these comorbidities for MCI risk determination using a machine learning algorithm.
Methods
Using a retrospective matched case-control design, 5185 MCI and 15,555 non-MCI individuals aged ≥50 years were identified from MarketScan databases. Predictive models included ADRD comorbidities, age, and sex.
Results
Associations between 25 ADRD comorbidities and MCI were significant but weakened with increasing age groups. The odds ratios (MCI vs non-MCI) in 50–64, 65–79, and ≥ 80 years, respectively, for depression (4.4, 3.1, 2.9) and stroke/transient ischemic attack (6.4, 3.0, 2.1). The predictive potential decreased with older age groups, with ROC-AUCs 0.75, 0.70, and 0.66 respectively. Certain comorbidities were age-specific predictors.
Conclusions
The comorbidity burden of MCI relative to non-MCI is age-dependent. A model based on comorbidities alone predicted an MCI diagnosis with reasonable accuracy.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
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