基于Dryad数据库的Nomogram模型用于预测患有各种精神障碍的患者的药物坚持
Xiaoxian Pei1, Xiangdong Du2, Dan Liu1
1Department of Psychiatric, The Fourth People's Hospital of Zhangjiagang City, Suzhou, Jiangsu, China.
BMJ open
|November 14, 2024
概括
预测精神病患者的药物服药性对治疗结果至关重要. 一个新的预测模型确定了低的药物态度库存-10分数,高的简要精神病学评分表分数,以及住院史作为不遵守的关键风险因素.
科学领域:
- 精神病学是一个精神病学.
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
背景情况:
- 遵守治疗显著影响精神病患者的疾病结果.
- 评估和预测患者的合规性仍然是管理精神病障碍的关键挑战.
研究的目的:
- 建立一种预测模型,用于精神病患者的药物服药.
- 为早期干预治疗不遵守行为的提供工具.
主要方法:
- 利用来自Dryad数据库的451名患有精神疾病的患者的临床数据.
- 采用最小绝对缩小和选择操作员 (LASSO) 和后勤回归来构建预测模型.
- 使用引导重新抽样验证了模型,并通过对应统计数据,Brier分数,ROC曲线和决策曲线分析评估了其性能.
主要成果:
- 最终的模型包括432名患者,总体合规率为61.3%.
- 发现的不合规的独立风险因素包括低的药物态度清单-10 (DAI-10) 评分,高的简短精神病学评分表 (BPRS) 评分,一年内多次住院,以及之前使用长效注射剂.
- 诺米图实现了0.709的一致统计和0.716的ROC曲线下的面积,表明了良好的预测能力.
结论:
- 低的DAI-10分数,高的BPRS分数,频繁的住院和以前的长期注射使用是精神疾病中药物不服药的重要预测因素.
- 开发的诺米图表显示出良好的灵敏度和特异性来预测治疗坚持,为临床实践提供了有价值的工具.
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