A proof-of-concept study to construct Bayesian network decision models for supporting the categorization of sudden unexpected infant death.

Sudden infant death syndrome (SIDS) remains a leading cause of infant death in high-income countries. Supporting models for categorization of sudden unexpected infant death into SIDS/non-SIDS could reduce mortality. Therefore, we aimed to develop such a tool utilizing forensic data, but the reduced number of SIDS cases renders this task inherently difficult. To overcome this, we constructed Bayesian network models according to diagnoses performed by expert pathologists and created conditional probability tables in a proof-of-concept study. In the diagnostic support model, the data of 64 sudden unexpected infant death cases was employed as the training dataset, and 16 known-risk factors, including age at death and co-sleeping, were added. In the validation study, which included 8 new cases, the models reproduced experts' diagnoses in 4 or 5 of the 6 SIDS cases. Next, to confirm the effectiveness of this approach for onset prediction, the data from 41 SIDS cases was employed. The model predicted that the risk of SIDS in 0- to 2-month-old infants exposed to passive smoking and co-sleeping is eightfold higher than that in the general infant population, which is comparable with previously published findings. The Bayesian approach could be a promising tool for constructing SIDS prevention models.