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. Cases: Infants with SUID or an unsafe sleep event over 5½ years in a single county. Controls: All births in the same county. 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. Infants with SUID ( = 62) or an unsafe sleep event ( = 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, < 0.001); 50 % of cases had a PRM score 17–20 vs. 16 % of controls ( < 0.001). 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.

中文翻译：

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使用自动预测风险模型识别有意外猝死风险的婴儿

婴儿意外死亡（SUID）是婴儿死亡的常见原因。我们评估了预测风险模型（PRM）——Hello Baby，该模型是根据进入寄养机构的风险对儿童进行分层而开发的，是否也能识别出发生 SUID 和非致命不安全睡眠事件风险最高的婴儿。案例：单个县患有 SUID 或超过 5.5 年不安全睡眠事件的婴儿。控制：同一县的所有出生。回顾性病例对照研究。收集人口统计和临床数据并分配 Hello Baby PRM 评分。计算了描述性统计数据和 PRM 分数 20 的预测值。将患有 SUID ( = 62) 或不安全睡眠事件 ( = 37) 的婴儿（病例）与 23,366 名新生儿（对照组）进行比较。病例和对照的所有人口统计和临床数据均相似，但发生不安全睡眠事件的婴儿年龄较大。病例的中位 PRM 评分高于对照（17.5 vs. 10，< 0.001）； 50% 的病例 PRM 评分为 17-20，而对照组为 16% (< 0.001)。 Hello Baby PRM 可以识别患有 SUID 和非致命不安全睡眠事件高风险的新生儿。在出现负面结果之前识别高危新生儿的能力可以对高危家庭进行个体化评估，以找出可能需要干预的可改变的危险因素。这种方法受到以下事实的限制：并非所有县都可以自动计算 PRM 或类似分数。