Incremental Value of Multidomain Risk Factors for Dementia Prediction: A Machine Learning Approach.

IF 4.4 2区 医学 Q1 GERIATRICS & GERONTOLOGY
Wei Ying Tan, Carol Anne Hargreaves, Gavin S Dawe, Wynne Hsu, Mong Li Lee, Ashwati Vipin, Nagaendran Kandiah, Saima Hilal
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引用次数: 0

Abstract

Objective: The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk prediction model (DRPM) for predicting incident dementia in older adults.

Design: Population-based, prospective cohort study.

Setting: UK Biobank study.

Participants: Individuals aged 60 or older without dementia.

Measurements: Fifty-five dementia-related predictors were gathered and categorized into clinical and medical history, questionnaire, cognition, polygenetic risk, and neuroimaging domains. Incident dementia (all-cause) and the subtypes, Alzheimer's disease (AD) and vascular dementia (VaD), were determined through hospital and death registries. Ensemble machine learning (ML) DRPMs were employed for prediction. The incremental values of risk predictors were assessed using the percent change in Area Under the Curve (∆AUC%) and the net reclassification index (NRI).

Results: The simple DRPM which included age, body mass index, sex, education, diabetes, hyperlipidaemia, hypertension, depression, smoking, and alcohol consumption yielded an AUC of 0.711 (± 0.008 SD). The five predictor domains exhibited varying levels of incremental value over the basic model when predicting all-cause dementia and the two subtypes. Neuroimaging markers provided the highest incremental value in predicting all-cause dementia (∆AUC% +9.6%) and AD (∆AUC% +16.5%) while clinical and medical history data performed the best at predicting VaD (∆AUC% +12.2%). Combining clinical and medical history, and questionnaire data synergistically enhanced ML DRPM performance.

Conclusion: Combining predictors from different domains generally results in better predictive performance. Selecting predictors involves trade-offs, and while neuroimaging markers can significantly enhance predictive accuracy, they may pose challenges in terms of cost or accessibility.

多域风险因素对痴呆症预测的增量价值:机器学习方法
目的:目前关于不同预测域如何有助于预测痴呆症发病的证据仍不明确。本研究旨在评估在简单痴呆症风险预测模型(DRPM)中加入五个预测域对预测老年人痴呆症事件的增量价值:设计:基于人群的前瞻性队列研究:环境:英国生物库研究:测量:55 种与痴呆症相关的指标:收集了 55 个与痴呆症相关的预测因子,并将其分为临床和病史、问卷调查、认知、多基因风险和神经影像领域。通过医院和死亡登记确定了痴呆症(全因)的发病情况和亚型,即阿尔茨海默病(AD)和血管性痴呆(VaD)。采用集合机器学习(ML)DRPM进行预测。使用曲线下面积变化百分比(ΔAUC%)和净再分类指数(NRI)评估风险预测因子的增量值:简单的 DRPM 包括年龄、体重指数、性别、教育程度、糖尿病、高脂血症、高血压、抑郁、吸烟和饮酒,其 AUC 为 0.711(± 0.008 SD)。在预测全因痴呆和两种亚型痴呆时,五个预测域与基本模型相比表现出不同程度的增量价值。神经影像标记物在预测全因痴呆(∆AUC% +9.6%)和注意力缺失症(∆AUC% +16.5%)方面的增量价值最高,而临床和病史数据在预测VaD方面的表现最好(∆AUC% +12.2%)。结合临床、病史和问卷数据可协同提高 ML DRPM 的性能:结论:将来自不同领域的预测因子结合起来,通常会获得更好的预测效果。选择预测因子需要权衡利弊,虽然神经影像标记物可以显著提高预测准确性,但它们可能会在成本或可及性方面带来挑战。
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来源期刊
CiteScore
13.00
自引率
4.20%
发文量
381
审稿时长
26 days
期刊介绍: The American Journal of Geriatric Psychiatry is the leading source of information in the rapidly evolving field of geriatric psychiatry. This esteemed journal features peer-reviewed articles covering topics such as the diagnosis and classification of psychiatric disorders in older adults, epidemiological and biological correlates of mental health in the elderly, and psychopharmacology and other somatic treatments. Published twelve times a year, the journal serves as an authoritative resource for professionals in the field.
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