根据传统的风险因素和常规的生化标志物,制定个人抑郁风险评分
Simeng Ma1, Dan Xiang1, Zhiyi Hu2
1Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China.
Journal of affective disorders
|November 13, 2024
概括
使用传统因素的抑郁风险评分模型有效识别高风险个体. 在这项大型英国生物库研究中,纳入生物化学标记物并没有显著提高预测性能.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 早期识别抑郁风险对于干预至关重要.
- 这项研究旨在制定个人抑郁风险评分.
- 英国生物库的数据被用来探索传统和生物化学标记.
研究的目的:
- 使用名ograms构建一个个人抑郁风险得分.
- 评估传统风险因素和生化标志物对抑郁风险评估的有用性.
- 为了比较带有和没有生物化学标记的模型的性能.
主要方法:
- 利用英国生物库数据 (369,407名参与者) 分为培训和测试集.
- 采用LASSO回归来从16个传统风险因素和28个生化标志物中选择预测变量.
- 开发了两个多变量逻辑回归模型,并生成了用于风险评分的诺莫грам.
主要成果:
- 选择了12个传统的风险因素和9个生化标志物.
- 模型1 (仅使用传统因子) 实现AUC0.913;模型2 (使用生化标志物) 实现AUC0.914.
- 模型1的灵敏度:81.99%,特异性:83.76%;模型2的灵敏度:81.54%,特异性:84.31%.
结论:
- 使用传统因素的抑郁风险评分模型表明了良好的临床性能.
- 整合常规的生化标志物并没有显著提高模型的预测能力.
- 需要外部验证来确认可通用性.
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