Towards Identifying Optimal NICU Admission Criteria for Late Preterm Infants

确定晚期早产儿最佳 NICU 入院标准

基本信息

批准号：
10536584
负责人：
NEHA SHIRISH JOSHI
金额：
$ 8.11万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-27 至 2025-07-26
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10536584
关键词：
37 weeks gestation Address Admission activity Birth Birth Weight Breast Feeding Career Choice Caring Clinical Data Data Analyses Data Set Environment Evaluation Event Fellowship Frequencies Gestational Age Goals Grant Healthcare Systems Hospitalization Hospitals Hyperbilirubinemia Hypoglycemia Incidence Infant Infant Care Institution Intervention K-Series Research Career Programs Lead Life Literature Live Birth Location Medical Methodology Methods Modeling Morbidity - disease rate Mothers Neonatal Intensive Care Units Newborn Infant Nurseries Outcome Patients Pediatric Hospitals Physicians Pregnancy Premature Infant Protocols documentation Reporting Research Research Design Resources Respiratory distress Retrospective cohort Risk Savings Scientist Sensitivity and Specificity Severity of illness Site Stress Symptoms Techniques Temperature Testing Time Training United States Universities Validation Variant Writing base career classification trees clinical decision-making cohort cost evidence base high risk infection risk lens neonatal morbidity predictive modeling regression trees skills

项目摘要

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.

在美国 376 万活产婴儿中，晚期早产儿（胎龄 34-36 周）占 7% 各州每年有超过 263,000 名婴儿与足月婴儿相比，晚期早产儿处于这一水平。低血糖、体温不稳定等结果导致发病的风险增加高胆红素血症，通常需要在新生儿重症监护室 (NICU) 进行医疗干预。绝大多数足月出生的婴儿在出生期间与母亲一起住在健康婴儿（一级）托儿所住院治疗，许多晚期早产儿被送往新生儿重症监护病房（NICU）住院，在那里他们可能会被隔离然而，不同医院的新生儿重症监护病房 (NICU) 入院率和入院率存在显着差异。无法用临床疾病解释的晚期早产儿入院的临床阈值。 PI 获得的结果表明，要求早产晚期自动进入 NICU 的机构标准婴儿的胎龄为 34-37 周，出生体重为 1500-2500 克，这代表晚期。不同成熟度和大小的早产儿，并且可能无法准确捕获最高的婴儿需要 NICU 级别干预的风险该提案的目标是确定最佳 NICU 入院标准。对于在同一机构出生的晚期早产儿的大型回顾性队列。集合、收集有关入院地点以及晚期早产的发生和管理的数据由此，目标 1 将得到解决：确定婴儿中新生儿发病的频率。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频率的文献有限，目前尚无文献描述随后，目标 2：预测模型。将开发晚期早产儿最有可能从自动进入新生儿重症监护病房（NICU）中受益的方法目标 1 中生成的队列将用于比较出生时的婴儿的临床参数。对于那些不需要任何培训和测试数据集的人来说，需要至少一次 NICU 级别的干预。使用训练集中的交叉验证技术，建立分数的最佳切点。将选择从预测模型导出的数据来根据敏感性和敏感性来推动临床决策决策规则的特异性将在测试集上进行评估。成为告知晚期早产儿最佳 NICU 入院标准的资源 PI 将接受培训。在此奖学金期间学习设计方法、数据分析、建模和资助写作，这将推进她成为独立医师科学家的职业道路专注于识别高价值护理实践她将受益于世界一流的新生儿护理。斯坦福大学的研究和临床环境以及著名的专业知识。