Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study.

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.