The risk of dementia or cognitive impairment in patients with cataracts: a systematic review and meta-analysis

Wang, Luping; Sang, Bowen; Zheng, Zuyan

doi:10.1080/13607863.2023.2226616

Cited by 1 publication

(1 citation statement)

References 74 publications

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“…Only tokens with more than 10 occurrences were included to avoid outliers. Several established and previously postulated risk factors of AD/ADRD/MCI emerge from these explanations, including vascular risk factors (such as heart disease, vascular disease, type 2 diabetes, hyperlipidemia, overweight) 33 , weight loss 34 , chronic kidney disease 35 , gait abnormality 36 , cataracts 37 , sleep apnea 38 , convulsions/seizures 39 , and mood disorder (including anxiety and depression) 40,41 . In addition to them, our model was able to recognize symptoms or diseases known to be precursors to AD/ADRD syndromes, including urinary incontinence, falls, and parkinsonism / Parkinson's disease.…”

Section: Model Interpretationmentioning

confidence: 99%

Predicting Risk of Alzheimer’s Diseases and Related Dementias with AI Foundation Model on Electronic Health Records

Zhu,

Tang,

Zhang

et al. 2024

Preprint

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Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.

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