ArticleAlzheimer disease and associated disorders
Predictors of Dementia in the Oldest Old: A Novel Machine Learning Approach.
Article in Alzheimer disease and associated disorders. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Prospective Effects of Self-Rated Health on Dementia Risk in Two Twin Studies of Aging.Behavior genetics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundIncidence of dementia increases exponentially with age; little is known about its risk factors in the ninth and 10th decades of life. We identified predictors of dementia with onset after age 85 years in a longitudinal population-based cohort.
methodsOn the basis of annual assessments, incident cases of dementia were defined as those newly receiving Clinical Dementia Rating (CDR) ≥1. We used a machine learning method, Markov modeling with hybrid density-based and partition-based clustering, to identify variables associated with subsequent incident dementia.
resultsOf 1439 participants, 641 reached age 85 years during 10 years of follow-up and 45 of these became incident dementia cases. Using hybrid density-based and partition-based, among those aged 85+ years, probability of incident dementia was associated with worse self-rated health, more prescription drugs, subjective memory complaints, heart disease, cardiac arrhythmia, thyroid disease, arthritis, reported hypertension, higher systolic and diastolic blood pressure, and hearing impairment. In the subgroup aged 85 to 89 years, risk of dementia was also associated with depression symptoms, not currently smoking, and lacking confidantes.
conclusionsAn atheoretical machine learning method revealed several factors associated with increased probability of dementia after age 85 years in a population-based cohort. If independently validated in other cohorts, these findings could help identify the oldest-old at the highest risk of dementia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.