在第一次精神病发作后,功能性结果的多变量预测:在EUFEST和PSYSCAN中采用交叉验证方法
Margot I E Slot1, Maria F Urquijo Castro2, Inge Winter-van Rossum3,4,5
1Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands. I.E.Slot-3@umcutrecht.nl.
Schizophrenia (Heidelberg, Germany)
|October 7, 2024
概括
预测第一发精神病 (FEP) 的结果需要外部验证. 在一个精神分裂症谱系障碍队列上开发的模型对另一个队列的概括性不佳,强调需要独立测试和数据协调.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 预测模型的预测建模
背景情况:
- 对于第一发精神病 (FEP) 存在多变量预后模型,但它们对独立人群的概括性不确定.
- 预测精神分裂症谱系障碍 (FES) 的功能结果对于患者护理至关重要.
研究的目的:
- 在FES中开发和外部验证FEP功能结果的预测模型.
- 评估机器学习模型在不同群体中的可传输性.
主要方法:
- 利用来自两个大型国际队列 (EUFEST,PSYSCAN FES) 的人口和临床基线预测因素.
- 使用与非线性支向量机 (SVM) 分类器的交叉验证方法.
- 在12个月的二分化全球功能评估 (GAF) 评分来定义差 (<65) 和好 (≥65) 的结果.
主要成果:
- 预测不良结果的交叉验证平衡准确率 (BAC) 在队列内为65-66%.
- 当应用到独立队列时,模型性能显著下降 (BAC 50-56%).
- 对合并样本的离场分析提高了业绩 (BAC 72%),表明数据协调的好处.
结论:
- 在独立样本中对预后模型的外部验证对于确定其真正的临床实用性至关重要.
- 在没有仔细的验证和潜在的数据协调的情况下,FEP预测中的模型可运输性是有限的.
- 未来的研究应该集中在外部验证研究和协调数据收集在FEP研究.
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