Liao Zihan, Chen Gaohan, Cao Xingrui, Liu Longqiao, Li Jiatong, Zhu Baoli, Cao Zhipeng
Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China.
Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China.
Forensic Sci Med Pathol. 2025 Jan 11. doi: 10.1007/s12024-024-00920-6.
Forensic diagnosis of sudden cardiac death (SCD) is an extremely important part of routine forensic practice. The present study aimed to develop and validate nomograms for predicting the probability of SCD with special regards to ischemic heart disease-induced SCD (IHD-induced SCD) based on multiple autopsy variables. A total of 3322 cases, were enrolled and randomly assigned into a training cohort (n = 2325) and a validation cohort (n = 997), respectively. Prediction models of SCD and IHD-induced SCD were developed through multivariable logistic regression based on variables selected by LASSO regression or ridge regression, and prediction model with higher area under the curve (AUC) of the receiver operating characteristic (ROC) curve in the validation cohort was used to establish nomograms. For SCD prediction, discrimination of the nomogram was determined based on the ROC with AUC of 0.751 (95% CI, 0.726-0.775) and 0.735 (95% CI, 0.696-0.774) in the training cohort and validation cohort respectively. The AUC of IHD-induced SCD prediction nomogram in the training cohort and validation cohort were 0.742 (95% CI, 0.716-0.768) and 0.738 (95% CI, 0.698-0.777). To facilitate the use of nomograms in routine casework in forensic practice, web calculators ( https://forensic.shinyapps.io/Forensic_SCD/ , https://forensic.shinyapps.io/Forensic_IHDinducedSCD/ ) were constructed. In conclusion, the present study developed and validated simple and practical nomograms for predicting the probability of SCD and IHD-induced SCD based on multiple autopsy variables. The nomograms have certain efficiency for discrimination and calibration to provide a novel approach to diagnose cause of death, and may become a valuable tool in forensic practice.
心脏性猝死（SCD）的法医学诊断是常规法医学实践中极为重要的一部分。本研究旨在基于多个尸检变量，开发并验证用于预测SCD概率的列线图，特别关注缺血性心脏病所致SCD（IHD所致SCD）。总共纳入3322例病例，并分别随机分配到训练队列（n = 2325）和验证队列（n = 997）。通过基于LASSO回归或岭回归选择的变量进行多变量逻辑回归，建立SCD和IHD所致SCD的预测模型，并使用验证队列中受试者工作特征（ROC）曲线下面积（AUC）较高的预测模型来建立列线图。对于SCD预测，训练队列和验证队列中列线图的区分度分别根据AUC为0.751（95%CI，0.726 - 0.775）和0.735（95%CI，0.696 - 0.774）的ROC来确定。训练队列和验证队列中IHD所致SCD预测列线图的AUC分别为0.742（95%CI，0.716 - 0.768）和0.738（95%CI，0.698 - 0.777）。为便于在法医学实践的常规案件工作中使用列线图，构建了网络计算器（https://forensic.shinyapps.io/Forensic_SCD/，https://forensic.shinyapps.io/Forensic_IHDinducedSCD/）。总之，本研究基于多个尸检变量开发并验证了用于预测SCD和IHD所致SCD概率的简单实用列线图。这些列线图在区分和校准方面具有一定效率，为死因诊断提供了一种新方法，可能成为法医学实践中的一个有价值工具。
