Trends in Resources for Neonatal Intensive Care at Delivery Hospitals for Infants Born Younger Than 30 Weeks' Gestation, 2009-2020

Survival of Infants Born at 22 to 25 Weeks' Gestation Receiving Care in the NICU: 2020-2022

OBJECTIVE

To provide contemporary data on infants inborn at 22 to 25 weeks' gestation and receiving care at level 3 and 4 neonatal intensive care units in the United States.

METHODS

Vermont Oxford Network members submitted data on infants born at 22 to 25 weeks' gestation at a hospital with a level 3 or 4 NICU from 2020 to 2022. The primary outcome was survival to hospital discharge. Secondary outcomes included survival without severe complications, length of stay, and technology dependence.

RESULTS

Overall, 22 953 infants at 636 US hospitals were included. Postnatal life support increased from 68.0% at 22 weeks to 99.8% at 25 weeks. The proportion of infants born at 22 weeks receiving postnatal life support increased from 61.6% in 2020 to 73.7% in 2022. For all infants, survival ranged from 24.9% at 22 weeks to 82.0% at 25 weeks. Among infants receiving postnatal life support, survival ranged from 35.4% at 22 weeks to 82.0% at 25 weeks. Survival without severe complications ranged from 6.3% at 22 weeks to 43.2% at 25 weeks. Median length of stay ranged from 160 days at 22 weeks to 110 days at 25 weeks. Among survivors, infants born at 22 weeks had higher rates of technology dependence at discharge home than infants born at later gestational ages.

CONCLUSIONS

Survival ranged from 24.9% at 22 weeks to 82.1% at 25 weeks, with low proportions of infants surviving without complications, prolonged lengths of hospital stay, and frequent technology dependence at all gestational ages.

Variation in NICU utilization: a narrative review and path forward

Utilization of the Neonatal Intensive Care Unit (NICU) varies widely in the United States. Over recent decades, there has been a growth in NICUs, that varies by region, and has not been correlated to changes in demand or illness severity. Unnecessary NICU admissions are costly, stressful to families, may increase the risk of hospital acquired morbidities, and decrease breast feeding. Most of the variation in NICU utilization is based on the care of late preterm, early term, and term babies and is related to hospital level factors, including financial incentives, driving utilization. Improvement strategies to reduce variation include regionalization of care, certificate of need legislation, improving discharge processes, and caring for babies with some conditions such as Neonatal Opioid Withdrawal Syndrome or those with risk factors for sepsis outside of the NICU.

The Impact of Hospital Delivery Volumes of Newborns Born Very Preterm on Mortality and Morbidity

OBJECTIVE

To examine if the annual patient volume of infants born very preterm (VPT, gestational age <32 weeks) at a hospital is associated with neonatal mortality and morbidity.

STUDY DESIGN

We performed an observational, secondary data analysis using a 20-year panel of birth certificates linked to hospital discharge abstracts, including transfers in California, Michigan, Missouri, Oregon, Pennsylvania, and South Carolina from 1996 through 2015. The study included all in-hospital VPT deliveries (n = 208 261). Study outcomes were in-hospital mortality or serious morbidity (intraventricular hemorrhage, necrotizing enterocolitis, retinopathy of prematurity, or bronchopulmonary dysplasia), attributed to the hospital of birth. Poisson regression models estimated the risk-adjusted relative risk (RR) for mortality and serious morbidity across different patient volume categories within a given hospital using hospital fixed effects.

RESULTS

The risk of mortality and serious morbidity for VPT infants increased as the number of infants born VPT at a hospital decreased. Compared with VPT delivery volumes >100 infants per year, the risk of mortality increased when a given hospital had VPT delivery volumes < 60 per year, ranging from a RR of 1.13 (95% C.I. 1.02-1.25) for volumes between 50 to 59 and 1.39 (1.19-1.62) for VPT volumes <10, and the risk of mortality or serious morbidity increased when a given hospital had VPT volumes <100, ranging from a RR of 1.05 (1.02-1.08) for volumes between 90 to 99 and 1.27 (1.19-1.36) for VPT volumes <10.

CONCLUSIONS

These results suggest that, for VPT infants, the risk of both mortality and mortality or serious morbidity is increased as the VPT volume within a given hospital declines.

Defining Levels of US Hospitals' Pediatric Capabilities

Importance

Classifying hospitals across a wide range of pediatric capabilities, including medical, surgical, and specialty services, would improve understanding of access and outcomes.

Objective

To develop a classification system for hospitals' pediatric capabilities.

Design, Setting, and Participants

This cross-sectional study included data from 2019 on all acute care hospitals with emergency departments in 10 US states that treated at least 1 child per day. Statistical analysis was performed from September 2023 to February 2024.

Exposure

Pediatric hospital capability level, defined using latent class analysis. The latent class model parameters were the presence or absence of 26 functional capabilities, which ranged from performing laceration repairs to performing organ transplants. A simplified approach to categorization was derived and externally validated by comparing each hospital's latent class model classification with its simplified classification using data from 3 additional states.

Main Outcomes and Measures

Health care utilization and structural characteristics, including inpatient beds, pediatric intensive care unit (PICU) beds, and referral rates (proportion of patients transferred among patients unable to be discharged).

Results

Using data from 1061 hospitals (716 metropolitan [67.5%]) with a median of 2934 pediatric ED encounters per year (IQR, 1367-5996), the latent class model revealed 4 pediatric levels, with a median confidence of hospital assignment to level of 100% (IQR, 99%-100%). Of 26 functional capabilities, level 1 hospitals had a median of 24 capabilities (IQR, 21-25), level 2 hospitals had a median of 13 (IQR, 11-15), level 3 hospitals had a median of 8 (IQR, 6-9), and level 4 hospitals had a median of 3 (IQR, 2-3). Pediatric level 1 hospitals had a median of 66 inpatient beds (IQR, 42-86), level 2 hospitals had a median of 16 (IQR, 9-22), level 3 hospitals had a median of 0 (IQR, 0-6), and level 4 hospitals had a median of 0 (IQR, 0-0) (P < .001). Level 1 hospitals had a median of 19 PICU beds (IQR, 10-28), level 2 hospitals had a median of 0 (IQR, 0-5), level 3 hospitals had a median of 0 (IQR, 0-0), and level 4 hospitals had a median of 0 (IQR, 0-0) (P < .001). Level 1 hospitals had a median referral rate of 1% (IQR, 1%-3%), level 2 hospitals had a median of 25% (IQR, 9%-45%), level 3 hospitals had a median of 70% (IQR, 52%-84%), and level 4 hospitals had a median of 100% (IQR, 98%-100%) (P < .001).

Conclusions and Relevance

In this cross-sectional study of hospitals from 10 US states, a system to classify hospitals' pediatric capabilities in 4 levels was developed and was associated with structural and health care utilization characteristics. This system can be used to understand and track national pediatric acute care access and outcomes.

Accurate prediction of neurologic changes in critically ill infants using pose AI

Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes in the neonatal intensive care unit (NICU). We collected 4,705 hours of video linked to electroencephalograms (EEG) from 115 infants. We trained a deep learning pose algorithm that accurately predicted anatomic landmarks in three evaluation sets (ROC-AUCs 0.83-0.94), showing feasibility of applying pose AI in an ICU. We then trained classifiers on landmarks from pose AI and observed high performance for sedation (ROC-AUCs 0.87-0.91) and cerebral dysfunction (ROC-AUCs 0.76-0.91), demonstrating that an EEG diagnosis can be predicted from video data alone. Taken together, deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.

Racial, ethnic, and socio-economic disparities in neonatal ICU admissions among neonates born with cyanotic CHD in the United States, 2009-2018

INTRODUCTION

Disparities in CHD outcomes exist across the lifespan. However, less is known about disparities for patients with CHD admitted to neonatal ICU. We sought to identify sociodemographic disparities in neonatal ICU admissions among neonates born with cyanotic CHD.

MATERIALS & METHODS

Annual natality files from the US National Center for Health Statistics for years 2009-2018 were obtained. For each neonate, we identified sex, birthweight, pre-term birth, presence of cyanotic CHD, and neonatal ICU admission at time of birth, as well as maternal age, race, ethnicity, comorbidities/risk factors, trimester at start of prenatal care, educational attainment, and two measures of socio-economic status (Special Supplemental Nutrition Program for Women, Infants, and Children [WIC] status and insurance type). Multivariable logistic regression models were fit to determine the association of maternal socio-economic status with neonatal ICU admission. A covariate for race/ethnicity was then added to each model to determine if race/ethnicity attenuate the relationship between socio-economic status and neonatal ICU admission.

RESULTS

Of 22,373 neonates born with cyanotic CHD, 77.2% had a neonatal ICU admission. Receipt of WIC benefits was associated with higher odds of neonatal ICU admission (adjusted odds ratio [aOR] 1.20, 95% CI 1.1-1.29, p < 0.01). Neonates born to non-Hispanic Black mothers had increased odds of neonatal ICU admission (aOR 1.20, 95% CI 1.07-1.35, p < 0.01), whereas neonates born to Hispanic mothers were at lower odds of neonatal ICU admission (aOR 0.84, 95% CI 0.76-0.93, p < 0.01).

CONCLUSION

Maternal Black race and low socio-economic status are associated with increased risk of neonatal ICU admission for neonates born with cyanotic CHD. Further work is needed to identify the underlying causes of these disparities.

Transfer Patterns Among Infants Born at 28 to 34 Weeks' Gestation

BACKGROUND

Although postnatal transfer patterns among high-risk (eg, extremely preterm or surgical) infants have been described, transfer patterns among lower-risk populations are unknown. The objective was to examine transfer frequency, indication, timing, and trajectory among very and moderate preterm infants.

METHODS

Observational study of the US Vermont Oxford Network all NICU admissions database from 2016 to 2021 of inborn infants 280/7 to 346/7 weeks. Infants' first transfer was assessed by gestational age, age at transfer, reason for transfer, and transfer trajectory.

RESULTS

Across 467 hospitals, 294 229 infants were eligible, of whom 12 552 (4.3%) had an initial disposition of transfer. The proportion of infants transferred decreased with increasing gestational age (9.6% [n = 1415] at 28 weeks vs 2.4% [n = 2646] at 34 weeks) as did the median age at time of transfer (47 days [interquartile range 30-73] at 28 weeks vs 8 days [interquartile range 3-16] at 34 weeks). The median post menstrual age at transfer was 34 or 35 weeks across all gestational ages. The most common reason for transfer was growth or discharge planning (45.0%) followed by medical and diagnostic services (30.2%), though this varied by gestation. In this cohort, 42.7% of transfers were to a higher-level unit, 10.2% to a same-level unit, and 46.7% to a lower-level unit, with indication reflecting access to specific services.

CONCLUSIONS

Over 4% of very and moderate preterm infants are transferred. In this population, the median age of transfer is later and does not reflect immediate care needs after birth, but rather the provision of risk-appropriate care.