SynthesisAlzheimer's & dementia : the journal of the Alzheimer's Association2025
Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.
Synthesis in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Article
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- Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.BMC medical informatics and decision making · 2026Article
- CogStrat: web-based tools for predicting incident cognitive impairment in cognitively normal older adults.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Mild cognitive impairment-to-Alzheimer's dementia progression risk: the contribution of the Interceptor project.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.GeroScience · 2026Article
- Association of oral frailty and its dimensions with cognitive impairment among older adults in China: a cross-sectional study.BMC oral health · 2026Observational
- Screen, Sample, Stratify: Biomarkers and Machine Learning Compress Dementia Pathways.Biomedicines · 2026Article
- A Machine Learning and Traditional Chinese Medicine Constitution-Based Prediction Model for Mild Cognitive Impairment in Community-Dwelling Older Adults.Neuropsychiatric disease and treatment · 2026Article
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
- Machine learning-based risk prediction model for cognitive dysfunction in elderly individuals.PloS one · 2025Article
- A Comprehensive Review of Predictive Precision in Scar Medicine: From Molecular Predictors to Machine Learning Models.Clinical, cosmetic and investigational dermatology · 2025Review
Corrections and comments
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7 authors.
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Abstract
Prediction models have been developed to identify mild cognitive impairment (MCI) cases likely to convert to dementia. This systematic review summarizes multi-source prediction models for MCI to dementia conversion. PubMed and Embase were searched for model development and validation studies from inception up to January 18 2024. Models were assessed for included predictors, predictive performance, risk of bias, and generalizability. 62 studies were included: 41 machine learning models, 11 regression models, and 5 disease state indexes. The number of predictors in the models ranged from 2 to 60; magnetic resonance imaging (MRI) and cognitive scores were the most common sources. Performance measures indicate reasonable predictive capabilities (area under the curve [AUC] range: 0.58-0.98, accuracy range: 66.1-96.3%); however, most studies are at high risk of bias and 47 studies lack external validation. Currently, no highly valid prediction model is available for MCI to dementia conversion risk due to limited generalizability and high risk of bias in most studies. HIGHLIGHTS: Numerous models have been developed to predict the likelihood of conversion to dementia in individuals with MCI. Prediction models seem to have a reasonably good performance in predicting conversion to dementia, however, external validation and generalizability is often lacking. There is no prediction model available with a low risk for bias and that has been externally validated to accurately predict the risk of MCI to dementia conversion. For MCI to dementia conversion prediction models, more emphasis should be directed towards external validation, generalizability, and clinical applicability.
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