通过结合临床和脑部扫描数据,改进了对躁狂复发风险的估计
Pol Palau1, Aleix Solanes2, Merce Madre3
1FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Benito Menni CASM - Hospital General de Granollers, Germanes Hospitalàries, Barcelona, Spain; Institute of Neurosciences, University of Barcelona, Barcelona, Spain.
Spanish journal of psychiatry and mental health
|October 15, 2023
概括
将临床数据与结构磁共振成像 (MRI) 结合起来,可以更好地预测躁狂复发风险. 这种综合方法为个性化精神病治疗策略提供了更准确的估计.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 医疗成像医学成像
背景情况:
- 准确估计躁狂复发风险对于个性化精神病治疗至关重要.
- 以前的研究仅依赖于基线临床数据.
- 整合结构磁共振成像 (MRI) 数据以改善风险预测的潜力仍然未被探索.
研究的目的:
- 评估是否将结构性MRI数据添加到临床信息中可以提高躁狂复发风险的估计.
- 开发和验证躁狂复发的预测模型.
主要方法:
- 一组78名患有躁狂发作 (双极性I或精神分裂症) 的患者被跟踪长达10年.
- 在交叉验证框架内使用了Cox lasso模型.
- 该模型整合了临床数据和结构性MRI数据,以预测躁狂复发风险.
主要成果:
- 综合临床和MRI数据模型准确估计了躁狂复发风险 (HR=2.35,p=0.027;AUC=0.65).
- 关键预测因素包括精神分裂症诊断,冲动控制不良,不寻常的思想内容和小脑体积减少.
- 仅使用临床或仅使用MRI数据的模型产生了较差的估计结果.
结论:
- 将临床数据与结构性MRI数据相结合,有望改善对躁狂复发风险的估计.
- 提供了一个公开可访问的网站,以促进模型复制和潜在的临床翻译.
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