Unique Clusters of Sudden Unexpected Infant Death (SUID) Descendants Revealed Through Machine Learning Epidemiological Analyses

Abstract

Sudden unexpected infant death (SUID) rates have substantially declined due to public health prevention efforts as well as mechanistic research over the past thirty years. Following an extensive medico-legal investigation, the decedent under one year of age is assigned as the cause of death: sudden infant death syndrome (SIDS), accidental suffocation and strangulation, or other ill-defined or unspecified cause of mortality. However, the extent to which these diseases manifest with an underlying neuropathological mechanism is highly controversial due to the heterogeneity of findings. In addition, substantial disparities are present when comparing SUID rates amongst race/ethnicity in the United States (US). The intersection of an underlying unknown biological abnormality and health disparities in the role of SUID is not well understood. Cluster analysis within heterogenous diseases has been highly instructive in neuropathology by identifying unique patient subtypes. Using maternal and infantile characteristics, we sought to identify unique groups of SUID decedents indicating distinct risk factors and neurodevelopmental causes. Using the US Period Linked Birth/Infant Mortality Files from 1990-2017 (excluding 1992-1994), we performed unsupervised machine learning dimensionality reduction techniques. Following identification of unique groups, we analyzed SUID rates in these clusters at the state level. We were able to identify three groups of SIDS decedents, each with a unique mean age at death when adjusting for length of gestation and performed similar analysis with the remaining SUID causes. We found that the implementation of clinical guidelines has had a distinct impact in decreasing the SUID rate in the unique clusters across geographical regions, however not all are experiencing a rapid reduction. The unexpected death of a previously seemingly healthy infant places a hard emotional burden on families. Through identifying unique risk factors and developmental timepoints of distinct SUID clusters, our findings can assist with public health infant mortality prevention policies and aid basic mechanistic research.