Identifying infants at risk of sudden unexpected death with an automated predictive risk model

Abstract

Background/objective: Sudden unexpected infant death (SUID) is a common cause of infant death. We evaluated whether a predictive risk model (PRM) - Hello Baby - which was developed to stratify children by risk of entry into foster care could also identify infants at highest risk of SUID and non-fatal unsafe sleep events.

Participants and setting: Cases: Infants with SUID or an unsafe sleep event over 5½ years in a single county.

Controls: All births in the same county.

Methods: Retrospective case-control study. Demographic and clinical data were collected and a Hello Baby PRM score was assigned. Descriptive statistics and the predictive value of a PRM score of 20 were calculated.

Results: Infants with SUID (n = 62) or an unsafe sleep event (n = 37) (cases) were compared with 23,366 births (controls). Cases and controls were similar for all demographic and clinical data except that infants with unsafe sleep events were older. Median PRM score for cases was higher than controls (17.5 vs. 10, p < 0.001); 50 % of cases had a PRM score 17-20 vs. 16 % of controls (p < 0.001).

Conclusions: The Hello Baby PRM can identify newborns at high risk of SUID and non-fatal unsafe sleep events. The ability to identify high-risk newborns prior to a negative outcome allows for individualized evaluation of high-risk families for modifiable risk factors which are potentially amenable to intervention. This approach is limited by the fact that not all counties can calculate a PRM or similar score automatically.

Keywords: Infant death; Predictive risk model; SIDS; Sleep.