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Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular
Zhengsheng Gu1, Shuang Liu2, Huijuan Ma2
1Department of Neurology, First Affiliated Hospital of Naval Medical University, Shanghai, China.
Insights
This study developed a machine learning model to predict dementia risk in atherosclerotic cardiovascular disease (ASCVD) patients. The model uses clinical and sociodemographic data, offering valuable insights for patient and clinician awareness.
Area of Science:
- Cardiovascular Medicine
- Neurology
- Artificial Intelligence in Healthcare
Background:
- Atherosclerotic cardiovascular disease (ASCVD) is linked to dementia, but specific risk factors remain unclear.
- Accurate prediction models for dementia in ASCVD patients are needed.
Purpose of the Study:
- To develop a machine learning model for predicting dementia risk in ASCVD patients.
- Utilize clinical and sociodemographic data for risk prediction.
Main Methods:
- A prognostic study using UK Biobank data from 2006-2010, with follow-up until April 2023.
- Identified 10 key predictors from 316 variables using a data-driven strategy.
- Developed a light gradient boosting machine model to predict incident dementia, Alzheimer's disease, and vascular dementia.
Main Results:
- Included 29,561 ASCVD patients; 4.51% developed dementia over a median 10.3-year follow-up.
- The UK Biobank ASCVD risk prediction model, using 10 predictors, achieved high performance metrics (AUC 0.866, accuracy 0.883).
- The model demonstrated good calibration and robust performance across cohorts, with potential clinical utility.
Conclusions:
- Predictive modeling can effectively inform patients and clinicians about dementia risk in ASCVD.
- Machine learning offers a promising approach for early identification and management of dementia in ASCVD populations.
Background:
The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASCVD remain unclear, necessitating the development of accurate prediction models.
Objective:
The aim of the study is to develop a machine learning model for use in patients with ASCVD to predict dementia risk using available clinical and sociodemographic data.
Methods:
This prognostic study included patients with ASCVD between 2006 and 2010, with registration of follow-up data ending on April 2023 based on the UK Biobank. We implemented a data-driven strategy, identifying predictors from 316 variables and developing a machine learning model to predict the risk of incident dementia, Alzheimer disease, and vascular dementia within 5, 10, and longer-term follow-up in patients with ASCVD.
Results:
A total of 29,561 patients with ASCVD were included, and 1334 (4.51%) developed dementia during a median follow-up time of 10.3 (IQR 7.6-12.4) years. The best prediction model (UK Biobank ASCVD risk prediction model) was light gradient boosting machine, comprising 10 predictors including age, time to complete pairs matching tasks, mean time to correctly identify matches, mean sphered cell volume, glucose levels, forced expiratory volume in 1 second z score, C-reactive protein, forced vital capacity, time engaging in activities, and age first had sexual intercourse. This model achieved the following performance metrics for all incident dementia: area under the receiver operating characteristic curve: mean 0.866 (SD 0.027), accuracy: mean 0.883 (SD 0.010), sensitivity: mean 0.637 (SD 0.084), specificity: mean 0.914 (SD 0.012), precision: mean 0.479 (SD 0.031), and F1-score: mean 0.546 (SD 0.043). Meanwhile, this model was well-calibrated (Kolmogorov-Smirnov test showed goodness-of-fit P value>.99) and maintained robust performance across different temporal cohorts. Besides, the model had a beneficial potential in clinical practice with a decision curve analysis.
Conclusions:
The findings of this study suggest that predictive modeling could inform patients and clinicians about ASCVD at risk for dementia.
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