Towards Identifying Optimal NICU Admission Criteria for Late Preterm Infants

晚期早产（胎龄34-36周）婴儿占美国376万活产婴儿的7%。 每年有超过263，000名婴儿死亡。与足月儿相比，晚期早产儿 低血糖、体温不稳定和 高胆红素血症，并且经常需要在新生儿重症监护室（NICU）中进行医疗干预。因此，虽然 绝大多数足月出生的婴儿在出生时与母亲一起住在条件良好的婴儿（一级）托儿所 住院，许多晚期早产儿反而在NICU住院，在那里他们可能被分开 从他们的母亲。然而，不同医院的新生儿重症监护室入院率存在显著差异， 不能用临床疾病解释的晚期早产儿入院的临床阈值。初步数据 PI获得的结果表明，要求晚期早产儿自动入住NICU的机构标准 婴儿可以从34-37周胎龄和1500-2500克出生体重变化。这代表了晚 不同成熟度和大小的早产儿，可能并不能准确地捕捉到最高年龄的婴儿。 需要NICU级别干预的风险。该提案的目标是确定最佳的新生儿重症监护室入院标准 晚期早产儿。将对在同一机构出生的晚期早产儿进行一项大型回顾性队列研究， 收集关于入院地点以及晚期早产发生和管理的数据 病态因此，目标1将得到解决：确定婴儿中新生儿发病率的频率 在妊娠34-36周出生，以及这些疾病需要医疗干预的频率。 关于晚期早产儿发病率的文献有限，目前还没有描述 这些疾病中需要临床干预的比例。目标2：预测模型 将制定最有可能受益于自动进入NICU的晚期早产儿 在出生的时候。目标1中生成的队列将用于比较以下婴儿的临床参数： 需要至少一次NICU级别的干预，而不需要任何干预。培训和测试数据集将 建立。在训练集内使用交叉验证技术， 将选择从预测模型中导出的数据，以根据敏感性和 决策规则的具体性。该策略将在测试集上进行评估。所获得的预测模型将 为晚期早产儿提供最佳的NICU入院标准。私家侦探会接受 研究设计方法，数据分析，建模和赠款写作在此奖学金，将促进 她的职业道路走向独立的医生科学家专注于确定高价值的护理实践 在新生儿护理中安全地促进完整的母婴二元体。她将受益于世界级的 研究和临床环境，以及斯坦福大学著名的专业知识。

Late preterm (34-36 weeks gestational age) infants account for 7% of the 3.76 million live births in the United States annually, or over 263,000 infants each year. Compared to term infants, late preterm infants are at increased risk of morbidity from outcomes such as hypoglycemia, temperature instability and hyperbilirubinemia, and often require medical intervention in a neonatal intensive care unit (NICU). Thus, while the vast majority of infants born at term stay with their mothers in a well infant (level I) nursery during the birth hospitalization, many late preterm infants are instead hospitalized in the NICU where they may be separated from their mothers. However, significant variation exists amongst hospitals for NICU admission rates and clinical thresholds for admission in late preterm infants that is not explained by clinical illness. Preliminary data obtained by the PI suggests that institutional criteria for requiring automatic NICU admission in late preterm infants can vary from 34-37 weeks gestational age and 1500-2500 grams birth weight. This represents late preterm infants of varying maturity and size, and likely does not precisely capture infants who are at highest risk of needing NICU level interventions. The goal of this proposal is to identify optimal NICU admission criteria for late preterm infants. A large retrospective cohort of late preterm infants born at a single institution will be assembled, collecting data on admission locations, and occurrence and management of late preterm morbidities. With this, Aim 1 will be addressed: identify the frequency of neonatal morbidities amongst infants born at 34-36 weeks’ gestation, and the frequency of these morbidities requiring medical intervention. Literature on the frequency of morbidities in late preterm infants is limited, and none currently exists delineating the proportion of these morbidities that require clinical intervention. Subsequently, in Aim 2: a prediction model will be developed for which late preterm infants are most likely to benefit from automatic admission to a NICU at the time of birth. The cohort generated in Aim 1 will be utilized to compare clinical parameters of infants who required at least one NICU level intervention to those that did not require any. Training and test data sets will be established. Using cross-validation techniques within the training set, an optimal cut-point for a score derived from the predictive model will be chosen to drive clinical decision-making based on the sensitivity and specificity of the decision rule. The strategy will be evaluated on a test set. The obtained prediction model will be a resource towards informing optimal NICU admission criteria for late preterm infants. The PI will train in study design methodology, data analysis, modeling, and grant writing during this fellowship that will advance her career path towards an independent physician scientist focused on identifying high value care practices that safely promote an intact mother-infant dyad in newborn care. She will benefit from the world-class research and clinical environment, and renowned expertise at Stanford University.