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针对SCD的法医预测的基于队列的诺米图:单中心试点研究的单中心试点研究

Zihan Liao^1,2,3, Gaohan Chen^1,2,3, Xingrui Cao^1,2,3

¹Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China.

Forensic science, medicine, and pathology

|January 11, 2025

概括

法医病理学家现在可以使用新的名图来利用尸检数据预测心脏突然死亡 (SCD) 和缺血性心脏病诱导的SCD (IHD诱导的SCD) 的概率. 这些经过验证的工具在法医实践中提供了更好的诊断准确性.

科学领域:

法医病理学法医病理学
心血管病理学心血管病理学
生物统计学生物统计学

背景情况:

突然心脏死亡 (SCD) 诊断在法医实践中至关重要.
准确预测SCD,特别是缺血性心脏病诱导的SCD (IHD诱导的SCD),是一个挑战.
现有的诊断方法可以从定量预测工具中受益.

研究的目的:

开发和验证用于预测SCD概率的nomograms.
专门开发和验证用于预测IHD诱导的SCD概率的nomograms.
通过网络计算器将这些名ogram集成到实际的法医案例工作中.

主要方法:

利用了3322个病例的大量队列,随机分为培训 (n=2325) 和验证 (n=997) 组.
采用多变量逻辑回归与LASSO或脊回归用于变量选择.
基于模型的建立名录,在验证队列中,从接收器操作特征 (ROC) 分析中获得最高的曲线下面面积 (AUC).

主要成果:

在SCD预测名录中,AUC达到0.751 (训练) 和0.735 (验证).
由IHD诱导的SCD预测名录实现了0.742 (训练) 和0.738 (验证) 的AUC.
网络计算器是为了在常规的法医调查中轻松访问和应用而创建的.

结论:

开发并验证了使用尸检变量预测SCD和IHD诱导的SCD概率的实用名录.
命名图表显示了有效的歧视和校准,为死亡原因的确定提供了一种新的方法.
这些工具将成为SCD诊断的常规法医实践中的宝贵资产.

关键词:

法医病理学法医病理学最小绝对收缩和操作员的选择.这个名字叫做Nomogram.预测模型的预测模型.坡回归的回归方法突然的心脏病死亡.

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