预测轻度认知障碍的退伍军人的5年痴呆转化
Chase Irwin1,2, Donna Tjandra1,3, Chengcheng Hu2,4
1Phoenix Veterans Affairs Health Care System Phoenix Arizona USA.
Alzheimer's & dementia (Amsterdam, Netherlands)
|March 28, 2024
概括
一个电子健康记录模型准确地预测了退伍军人在5年内轻度认知障碍 (MCI) 到全因痴呆 (ACD) 的转化. 关键的危险因素包括年龄和血管疾病,而高BMI和睡眠呼吸暂停显示出保护作用.
科学领域:
- 老年学是一门学科.
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 识别患有轻度认知障碍 (MCI) 的患有痴呆症高风险的患者对于及时干预至关重要.
- 电子健康记录 (EHR) 为开发疾病进展的预测模型提供了宝贵的资源.
研究的目的:
- 开发和验证基于EHR的模型,用于预测MCI在5年内转化为所有原因痴呆症 (ACD).
- 在退伍军人群体中确定与MCI转化为ACD相关的关键人口和临床因素.
主要方法:
- 考克斯的比例危险模型被用来分析来自一大群被诊断为MCI的退伍军人的EHR数据.
- 模型性能经过严格评估,使用了在单独的验证数据集上的接收器操作特征曲线 (AUC) 下的面积和Brier分数等指标.
- 该研究还探讨了合成数据对模型开发的实用性,将其性能与使用真实患者数据构建的模型进行比较.
主要成果:
- 在59,782名MCI患者中,15,420人 (25.8%) 在5年内发展为ACD.
- 预测模型表现出强大的区分能力 (AUC = 0.73) 和良好的校准 (布赖尔分数 = 0.18).
- 转换的重要风险因素包括晚年,中风病史,脑血管疾病,心肌梗塞,高血压和糖尿病. 相反,较高的体重指数,滥用酒精和睡眠呼吸暂停与转化风险降低有关.
结论:
- 开发的基于EHR的预测模型在识别可能在5年内转换为ACD的MCI个体方面表现强.
- 这种模型在临床应用中具有显著的潜力,用于对患者进行分组,并为患有痴呆症风险较高的人提供早期干预.
- 使用合成数据的模型的可比性能突显了其在加快研究的潜力,同时保持患者隐私.
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