Towards Identifying Optimal NICU Admission Criteria for Late Preterm Infants

晚期早产儿(胎龄34-36周)占美国376万活产婴儿的7% 美国每年有263,000多名婴儿。与足月儿相比，晚期早产儿的年龄 低血糖、体温不稳定和 高胆红素血症，通常需要在新生儿重症监护病房(NICU)进行医疗干预。因此，虽然 绝大多数足月出生的婴儿在出生期间和他们的母亲一起住在一个健康的婴儿(I级)托儿所。 住院，许多晚期早产儿改为在NICU住院，在那里他们可能会被分开 从他们的母亲那里。然而，不同医院之间的NICU入院率和 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，要求早产晚期自动入院NICU的机构标准 婴儿的胎龄为34-37周，出生体重为1500-2500克。这表示迟到了 不同成熟度和大小的早产儿，可能不能准确地捕捉到最高年龄的婴儿 需要NICU水平干预的风险。这项提案的目标是确定最佳的NICU入院标准 适用于晚期早产儿。在一家机构出生的晚期早产儿的大型回顾性队列将是 收集、收集有关入学地点的数据，以及晚期早产的发生和管理 病态。为此，目标1将得到解决：确定婴儿中新生儿发病率 在怀孕34-36周时出生，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。随后，在目标2中：一个预测模型 晚期早产儿最有可能从自动进入NICU受益的情况下发生 在出生的时候。在目标1中产生的队列将被用来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。训练和测试数据集将 被建立起来。使用训练集内的交叉验证技术，得分的最佳切入点 从预测模型派生的将被选择来驱动基于敏感度和 决策规则的特殊性。该策略将在测试集上进行评估。得到的预测模型将 为晚期早产儿提供最佳的NICU入院标准。私家侦探将在 在此奖学金期间，学习设计方法、数据分析、建模和拨款写作，这将是进步的 她的职业道路是成为一名独立的内科科学家，专注于识别高价值的护理实践 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，以及斯坦福大学的知名专业知识。

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.