一个混合模型,用于预测在第一次精神病发作后对RISPERIDONE的反应
Giovany Oliveira Costa1, Vanessa K Ota2, Matheus Rodrigues Luiz1
1Laboratório de Neurociências Integrativas (LiNC), Escola Paulista de Medicina, Universidade Federal de São Paulo (UNIFESP-EPM), São Paulo, SP, Brazil. Disciplina de Genética, Departamento de Morfologia e Genética, UNIFESP-EPM, São Paulo, SP, Brazil.
Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)
|July 29, 2024
概括
在第一发精神病 (FEP) 中预测抗精神病反应至关重要. 结合临床和遗传因素的混合模型显著改善了FEP患者里斯佩里治疗的预测准确性.
科学领域:
- 精神病学和药理学 精神病学和药理学
- 遗传学和个性化医学
- 计算生物学 计算生物学
背景情况:
- 在第一发精神病 (FEP) 中,抗精神病药物反应表现出显著的患者间变异性.
- 这种异质性可能源于临床和遗传因素.
- 需要个性化治疗方法来优化结果.
研究的目的:
- 评估临床,遗传和混合模型对FEP患者里斯佩里反应的预测性能.
- 确定治疗反应的关键临床和遗传预测因素.
主要方法:
- 141名未服用过抗精神病药物的FEP患者在RISPERIDONE治疗10周之前和之后进行了评估.
- 应答被定义为>=50%的阳性和阴性综合征量表 (PANSS) 评分的减少.
- 机器学习算法 (SVM,kNN,RF) 用于构建和测试临床,遗传和混合模型.
主要成果:
- 临床模型实现了63.3% (SVM) 的平衡精度.
- 基因模型显示了58.5% (kNN) 的平衡精度.
- 性能最好的混合模型 (RF) 整合了临床数据 (未经治疗的精神病的持续时间,CGI-S,年龄,大麻使用) 和406个单核酸变体 (SNV),达到72.9%的平衡精度.
结论:
- 结合临床和遗传预测因素的混合模型可以在FEP中更好地预测抗精神病治疗反应.
- 这种方法对精神病的个性化药物治疗具有前景.
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