在危急疾病期间的妄想的数据衍生子类型
Kelly M Potter1, Jason N Kennedy1, Chukwudi Onyemekwu2
1Center for Research, Investigation, and Systems Modeling of Acute Illness (CRISMA), Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
EBioMedicine
|January 3, 2024
概括
这项研究使用机器学习在重症患者中确定了四种不同类型的妄想. 这些亚型在临床特征和结果上有所不同,需要进一步验证,并可能指导未来的治疗策略.
科学领域:
- 关键护理医学 关键护理医学
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
背景情况:
- 以前的妄想研究依赖于心理运动症状或风险因素来识别亚型.
- 机器学习已经在其他急性疾病中确定了对治疗有反应的亚型,但不是痴呆症.
- 了解妄异质性对于有针对性的干预至关重要.
研究的目的:
- 在重症患者中识别出不同的数据衍生狂妄症亚型.
- 为了比较这些已识别的亚型的临床特征和结果.
- 探索数据衍生子类型和先前定义的妄想分类之间的关系.
主要方法:
- 对一项大型多中心前性队列研究的二次分析,该研究涉及呼吸衰竭或休克的ICU患者.
- 无监督潜伏类型建模使用前妄数据来识别子类型.
- 人口统计数据,临床特征和已识别的狂妄症亚型的结果的比较.
主要成果:
- 一个四类模型最适合数据,识别了不同的妄想亚型.
- 亚型2的死亡率更高,而亚型4的 Delirium 和昏迷持续时间更长.
- 所有确定的亚型在随访时都表现出显著的认知障碍,不论是亚型.
结论:
- 确定了四种数据衍生狂妄症亚型,需要在独立队列中进行验证.
- 这些亚型可能代表不同的治疗效果,为未来的临床试验提供信息.
- 结果可以指导机理学研究和开发有针对性的妄想治疗策略.
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