# Towards Identifying Optimal NICU Admission Criteria for Late Preterm Infants

> **NIH NIH F32** · STANFORD UNIVERSITY · 2024 · $22,165

## Abstract

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...

## Key facts

- **NIH application ID:** 10868664
- **Project number:** 5F32HD106763-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** NEHA SHIRISH JOSHI
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $22,165
- **Award type:** 5
- **Project period:** 2022-07-27 → 2024-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10868664

## Citation

> US National Institutes of Health, RePORTER application 10868664, Towards Identifying Optimal NICU Admission Criteria for Late Preterm Infants (5F32HD106763-03). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10868664. Licensed CC0.

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