Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Infants Born Extremely Preterm.

OBJECTIVE: The objective of this study was to examine the association of cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, with late-onset sepsis for extremely preterm infants (<29 weeks of gestational age) on vs off invasive mechanical ventilation. STUDY DESIGN: This is a retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in 5 level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean gestational age: 26.4 weeks, SD 1.71). Monitoring data were available and analyzed for 719 infants (47 512 patient-days); of whom, 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72 hours after birth and ≥5-day antibiotics). RESULTS: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer events with oxygen saturation <80% (IH80) and more bradycardia events before sepsis. IH events were associated with higher sepsis risk but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model including postmenstrual age, cardiorespiratory variables (apnea, periodic breathing, IH80, and bradycardia), and ventilator status predicted sepsis with an area under the receiver operator characteristic curve of 0.783. CONCLUSION: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

Apnea, Intermittent Hypoxemia, and Bradycardia Events Predict Late-Onset Sepsis in Extremely Preterm Infants.

Objectives: Detection of changes in cardiorespiratory events, including apnea, periodic breathing, intermittent hypoxemia (IH), and bradycardia, may facilitate earlier detection of sepsis. Our objective was to examine the association of cardiorespiratory events with late-onset sepsis for extremely preterm infants (<29 weeks' gestational age (GA)) on versus off invasive mechanical ventilation. Study Design: Retrospective analysis of data from infants enrolled in Pre-Vent (ClinicalTrials.gov identifier NCT03174301), an observational study in five level IV neonatal intensive care units. Clinical data were analyzed for 737 infants (mean GA 26.4w, SD 1.71). Monitoring data were available and analyzed for 719 infants (47,512 patient-days), of whom 109 had 123 sepsis events. Using continuous monitoring data, we quantified apnea, periodic breathing, bradycardia, and IH. We analyzed the relationships between these daily measures and late-onset sepsis (positive blood culture >72h after birth and ≥5d antibiotics). Results: For infants not on a ventilator, apnea, periodic breathing, and bradycardia increased before sepsis diagnosis. During times on a ventilator, increased sepsis risk was associated with longer IH80 events and more bradycardia events before sepsis. IH events were associated with higher sepsis risk, but did not dynamically increase before sepsis, regardless of ventilator status. A multivariable model predicted sepsis with an AUC of 0.783. Conclusion: We identified cardiorespiratory signatures of late-onset sepsis. Longer IH events were associated with increased sepsis risk but did not change temporally near diagnosis. Increases in bradycardia, apnea, and periodic breathing preceded the clinical diagnosis of sepsis.

The Effects of Health Disparities on Neonatal Outcomes.

The history of racism in the United States was established with slavery, and the carry-over effect continues to impact health care through structural and institutional racism. Racial segregation and redlining have impacted access to quality health care, thereby impacting prematurity and infant mortality rates. Health disparities also impact neonatal morbidities such as intraventricular hemorrhage and necrotizing enterocolitis and the family care experience including the establishment of breastfeeding and health care provider interactions.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Gabapentin as Adjunctive Therapy in Neonatal Opioid Withdrawal Syndrome: A Case Series.

OBJECTIVE: We describe a single center experience with gabapentin as adjunctive therapy in infants with neonatal opioid withdrawal syndrome (NOWS). METHODS: We performed a retrospective chart review of infants receiving gabapentin for NOWS. Data points collected included patient's sex, gestational age, maternal opioid exposure, NOWS medication dosing and length of therapy, number of failed wean attempts, time to successful morphine wean and duration of morphine wean, length of stay in the neonatal intensive care unit (NICU), and NOWS medications at discharge. RESULTS: Six infants received gabapentin as adjunctive treatment for NOWS. All infants failed 2-4 morphine weans before initiation of gabapentin despite the addition of clonidine. All infants that received gabapentin were successfully weaned off morphine. The time to wean off morphine after gabapentin initiation varied from 4-35 days. Maximum gabapentin doses ranged from 15 - 42.7 mg/kg/day. Five infants were discharged from the NICU on gabapentin. CONCLUSIONS: Gabapentin appeared to facilitate successful morphine weans in six patients with NOWS who were previously unable to wean despite the initiation of clonidine.

Deep Learning to Optimize Magnetic Resonance Imaging Prediction of Motor Outcomes After Hypoxic-Ischemic Encephalopathy.

BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for outcome prediction after hypoxic-ischemic encephalopathy (HIE). Published scoring systems contain duplicative or conflicting elements. METHODS: Infants ≥36 weeks gestational age (GA) with moderate to severe HIE, therapeutic hypothermia treatment, and T1/T2/diffusion-weighted imaging were identified. Adverse motor outcome was defined as Bayley-III motor score <85 or Alberta Infant Motor Scale <10th centile at 12 to 24 months. MRIs were scored using a published scoring system. Logistic regression (LR) and gradient-boosted deep learning (DL) models quantified the importance of clinical and imaging features. The cohort underwent 80/20 train/test split with fivefold cross validation. Feature selection eliminated low-value features. RESULTS: A total of 117 infants were identified with mean GA = 38.6 weeks, median cord pH = 7.01, and median 10-minute Apgar = 5. Adverse motor outcome was noted in 23 of 117 (20%). Putamen/globus pallidus injury on T1, GA, and cord pH were the most informative features. Feature selection improved model accuracy from 79% (48-feature MRI model) to 85% (three-feature model). The three-feature DL model had superior performance to the best LR model (area under the receiver-operator curve 0.69 versus 0.75). CONCLUSIONS: The parsimonious DL model predicted adverse HIE motor outcomes with 85% accuracy using only three features (putamen/globus pallidus injury on T1, GA, and cord pH) and outperformed LR.

Preterm Infants off Positive Pressure Respiratory Support Have a Higher Incidence of Occult Cerebral Hypoxia.

OBJECTIVE: To use cerebral near-infrared spectroscopy (NIRS) to quantify occult cerebral hypoxia across respiratory support modes in preterm infants. STUDY DESIGN: In this prospective, longitudinal, observational study, infants ≤32 weeks gestation underwent serial pulse oximetry (oxygen saturation [SpO2]) and cerebral NIRS monitoring (4-6 hours per session) following a standardized recording schedule (daily for 2 weeks, every other day for 2 weeks, then weekly until 35 weeks corrected gestational age). Four calculations were made: median cerebral saturation, median cerebral hypoxia burden (proportion of NIRS samples below the hypoxia threshold [<67%]), median systemic saturation, and median systemic hypoxia burden (proportion of SpO2 samples below the desaturation threshold [<85%]). During each recording session, respiratory support mode was noted (room air, low-flow nasal cannula, high-flow nasal cannula, noninvasive positive pressure ventilation, continuous positive airway pressure, and invasive ventilation). RESULTS: There were 1013 recording sessions made from 174 infants with a median length of 6.9 hours. Although the systemic (SpO2) hypoxia burden was significantly greater for infants on the highest respiratory support (invasive and noninvasive positive pressure ventilation), the cerebral hypoxia burden was significantly greater during recording sessions made on the lowest respiratory support (8% for room air; 29% for low-flow nasal cannula). CONCLUSIONS: Premature infants on the highest levels of respiratory support have less cerebral hypoxia than those on lower respiratory support. These results raise concern about unrecognized cerebral hypoxia during lower acuity periods of neonatal intensive care unit hospitalization and adverse outcomes.

Neighborhood Deprivation and Association With Neonatal Intensive Care Unit Mortality and Morbidity for Extremely Premature Infants.

Importance: Socioeconomic status affects pregnancy and neurodevelopment, but its association with hospital outcomes among premature infants is unknown. The Area Deprivation Index (ADI) is a validated measure of neighborhood disadvantage that uses US Census Bureau data on income, educational level, employment, and housing quality. Objective: To determine whether ADI is associated with neonatal intensive care unit (NICU) mortality and morbidity in extremely premature infants. Design, Setting, and Participants: This retrospective cohort study was performed at 4 level IV NICUs in the US Northeast, Mid-Atlantic, Midwest, and South regions. Non-Hispanic White and Black infants with gestational age of less than 29 weeks and born between January 1, 2012, and December 31, 2020, were included in the analysis. Addresses were converted to census blocks, identified by Federal Information Processing Series codes, to link residences to national ADI percentiles. Exposures: ADI, race, birth weight, sex, and outborn status. Main Outcomes and Measures: In the primary outcome, the association between ADI and NICU mortality was analyzed using bayesian logistic regression adjusted for race, birth weight, outborn status, and sex. Risk factors were considered significant if the 95% credible intervals excluded zero. In the secondary outcome, the association between ADI and NICU morbidities, including late-onset sepsis, necrotizing enterocolitis (NEC), and severe intraventricular hemorrhage (IVH), were also analyzed. Results: A total of 2765 infants with a mean (SD) gestational age of 25.6 (1.7) weeks and mean (SD) birth weight of 805 (241) g were included in the analysis. Of these, 1391 (50.3%) were boys, 1325 (47.9%) reported Black maternal race, 498 (18.0%) died before NICU discharge, 692 (25.0%) developed sepsis or NEC, and 353 (12.8%) had severe IVH. In univariate analysis, higher median ADI was found among Black compared with White infants (77 [IQR, 45-93] vs 57 [IQR, 32-77]; P < .001), those who died before NICU discharge vs survived (71 [IQR, 45-89] vs 64 [IQR, 36-86]), those with late-onset sepsis or NEC vs those without (68 [IQR, 41-88] vs 64 [IQR, 35-86]), and those with severe IVH vs those without (69 [IQR, 44-90] vs 64 [IQR, 36-86]). In a multivariable bayesian logistic regression model, lower birth weight, higher ADI, and male sex were risk factors for mortality (95% credible intervals excluded zero), while Black race and outborn status were not. The ADI was also identified as a risk factor for sepsis or NEC and severe IVH. Conclusions and Relevance: The findings of this cohort study of extremely preterm infants admitted to 4 NICUs in different US geographic regions suggest that ADI was a risk factor for mortality and morbidity after adjusting for multiple covariates.

Macroperiodic Oscillations: A Potential Novel Biomarker of Outcome in Neonatal Encephalopathy.

PURPOSE: Neonatal encephalopathy (NE) is a common cause of neurodevelopmental morbidity. Tools to accurately predict outcomes after therapeutic hypothermia remain limited. We evaluated a novel EEG biomarker, macroperiodic oscillations (MOs), to predict neurodevelopmental outcomes. METHODS: We conducted a secondary analysis of a randomized controlled trial of neonates with moderate-to-severe NE who underwent standardized clinical examination, magnetic resonance (MR) scoring, video EEG, and neurodevelopmental assessment with Bayley III evaluation at 18 to 24 months. A non-NE cohort of neonates was also assessed for the presence of MOs. The relationship between clinical examination, MR score, MOs, and neurodevelopmental assessment was analyzed. RESULTS: The study included 37 neonates with 24 of whom survived and underwent neurodevelopmental assessment (70%). The strength of MOs correlated with severity of clinical encephalopathy. MO strength and spread significantly correlated with Bayley III cognitive percentile (P = 0.017 and 0.046). MO strength outperformed MR score in predicting a combined adverse outcome of death or disability (P = 0.019, sensitivity 100%, specificity 77% vs. P = 0.079, sensitivity 100%, specificity 59%). CONCLUSIONS: MOs are an EEG-derived, quantitative biomarker of neurodevelopmental outcome that outperformed a comprehensive validated MRI injury score and a detailed systematic discharge examination in this small cohort. Future work is needed to validate MOs in a larger cohort and elucidate the underlying pathophysiology of MOs.

Variable Association of Physiologic Changes With Electrographic Seizure-Like Events in Infants Born Preterm.

OBJECTIVES: To determine the incidence of seizure-like events in a cohort of infants born preterm as well as the prevalence of associated vital sign changes (heart rate [HR], respiratory rate, and pulse oximetry [SpO2]). STUDY DESIGN: We performed prospective conventional video electroencephalogram monitoring on infants born at 23-30 weeks of gestational age during the first 4 postnatal days. For detected seizure-like events, simultaneously captured vital sign data were analyzed during the pre-event baseline and during the event. Significant vital sign changes were defined as HR or respiratory rate >±2 SD from the infant's own baseline physiologic mean, derived from a 10-minute interval before the seizure-like event. Significant change in SpO2 was defined as oxygen desaturation during the event with a mean SpO2 <88%. RESULTS: Our sample included 48 infants with median gestational age of 28 weeks (IQR 26-29) and birth weight of 1125 g (IQR 963-1265). Twelve (25%) infants had seizure-like discharges with a total of 201 events; 83% (10/12) of infants had vital sign changes during these events, and 50% (6/12) had significant vital sign changes during the majority of the seizure-like events. Concurrent HR changes occurred the most frequently. CONCLUSIONS: Individual infant variability was observed in the prevalence of concurrent vital sign changes with electroencephalographic seizure-like events. Physiologic changes associated with preterm electrographic seizure-like events should be investigated further as a potential biomarker to assess the clinical significance of such events in the preterm population.

Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.

BACKGROUND: Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO2) data contain signatures that improve sepsis risk prediction over HR or demographics alone. METHODS: We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500 g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10 m: mean, standard deviation, skewness, kurtosis of HR and SpO2, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO2 features alone for comparison with HR-SpO2 models. RESULTS: Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO2 model performed better than models using either HR or SpO2 alone. Demographics improved the discrimination of all physiologic data models but dampened dynamic performance. CONCLUSIONS: Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction. IMPACT: Heart rate characteristics aid early detection of late-onset sepsis, but respiratory data contain signatures of illness due to infection. Predictive models using both heart rate and respiratory data may improve early sepsis detection. A cardiorespiratory early warning score, analyzing heart rate from electrocardiogram or pulse oximetry with SpO2, predicts late-onset sepsis within 24 h across multiple NICUs and detects sepsis better than heart rate characteristics or demographics alone. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis.

Use of an electronic medical record to optimize a neonatal sepsis score for mortality prediction.

OBJECTIVE: Late-onset sepsis (LOS) is a significant cause of mortality in preterm infants. The neonatal sequential organ failure assessment (nSOFA) provides an objective assessment of sepsis risk but requires manual calculation. We developed an EMR pipeline to automate nSOFA calculation for more granular analysis of score performance and to identify optimal alerting thresholds. METHODS: Infants born <33 weeks of gestation with LOS were included. A SQL-based pipeline calculated hourly nSOFA scores 48 h before/after sepsis evaluation. Sensitivity analysis identified the optimal timing and threshold of nSOFA for LOS mortality. RESULTS: Eighty episodes of LOS were identified (67 survivors, 13 non-survivor). Non-survivors had persistently elevated nSOFA scores, markedly increasing 12 h prior to culture. At sepsis evaluation, the AUC for nSOFA >2 was 0.744 (p = 0.0047); thresholds of >3 and >4 produced lower AUCs. CONCLUSIONS: nSOFA is persistently elevated for infants with LOS mortality compared to survivors with an optimal alert threshold >2.

Early Vital Sign Differences in Very Low Birth Weight Infants with Severe Intraventricular Hemorrhage.

OBJECTIVE: Severe intraventricular hemorrhage (sIVH, grades 3 and 4) is a serious complication for very low birth weight (VLBW) infants and is often clinically silent requiring screening cranial ultrasound (cUS) for detection. Abnormal vital sign (VS) patterns might serve as biomarkers to identify risk or occurrence of sIVH. STUDY DESIGN: This retrospective study was conducted in VLBW infants admitted to two level-IV neonatal intensive care units (NICUs) between January 2009 and December 2018. Inclusion criteria were: birth weight <1.5 kg and gestational age (GA) <32 weeks, at least 12 hours of systemic oxygen saturation from pulse oximetry (SpO2) data over the first 24 hours and cUS imaging. Infants were categorized as early sIVH (sIVH identified in the first 48 hours), late sIVH (sIVH identified after 48 hours and normal imaging in the first 48 hours), and no IVH. Infants with grades 1 and 2 or unknown timing IVH were excluded. Mean heart rate (HR), SpO2, mean arterial blood pressure (MABP), number of episodes of bradycardia (HR < 100 bpm), and desaturation (SpO2 < 80%) were compared. RESULTS: A total of 639 infants (mean: 27 weeks' gestation) were included (567 no IVH, 34 early sIVH, and 37 late sIVH). In the first 48 hours, those with sIVH had significantly higher HR compared with those with no IVH. Infants with sIVH also had lower mean SpO2 and MABP and more desaturations <80%. No significant differences in VS patterns were identified in early versus late sIVH. Logistic regression identified higher HR and greater number of desaturations <80% as independently associated with sIVH. CONCLUSION: VLBW infants who develop sIVH demonstrate VS differences with significantly lower SpO2 and higher mean HR over the first 48 hours after birth compared with VLBW infants with no IVH. Abnormalities in early VS patterns may be a useful biomarker for sIVH. Whether VS abnormalities predict or simply reflect sIVH remains to be determined. KEY POINTS: · A higher HR in the first 48 hours is seen in infants with severe IVH.. · Infants with sIVH have lower blood pressure in the first 48 hours.. · Infants with sIVH have more oxygen desaturations in the first 48 hours..

Improving child health through Big Data and data science.

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

The Impact of Dexmedetomidine Initiation on Cardiovascular Status and Oxygenation in Critically ill Neonates.

Dexmedetomidine is being increasingly used as a primary or adjunctive sedative agent in neonates. There are a paucity of high-quality, high-resolution physiologic data during administration, despite significant potential cardiorespiratory effects. Term and preterm infants admitted between January 2018 and July 2020 were screened for dexmedetomidine exposure. Prospectively recorded vital signs (heart rate, oxygenation, arterial blood pressure) were cross-matched with pharmacy records to identify infants with data available 24 h before and 48 h after drug initiation. Vital sign data were processed via a standardized pipeline to (a) remove missing data, (b) obtain baseline averages of vital signs for 24 h preceding dexmedetomidine, and (c) calculate the hourly mean deviation from the baseline for the 48 h following initiation of dexmedetomidine. Infants were clustered by postmenstrual age (preterm ≤ 35 weeks; term > 35 weeks). 72 infants were identified with mean gestational age of 32 weeks and mean ± SD birth weight of 1976 ± 1341 g. Although both groups of infants experienced bradycardia, heart rate in term infants dropped faster and reached a nadir 5 beats per minute lower, before converging at a common deviation of - 10 beats per minute. No hypo- or hypertension was noted in either group. Unexpected instability of oxygenation occurred in a subset of preterm infants, requiring escalation of respiratory support. Administration of dexmedetomidine results in differential timing and magnitude of bradycardia in term and preterm infants, no major impact on blood pressure, and a surprising instability of oxygenation in preterm infants, requiring increased ventilatory support. Further investigation is warranted.

Seizure burden in preterm infants and smaller brain volume at term-equivalent age.

BACKGROUND: Seizures are underrecognized in preterm infants, and little is known about their impact on brain growth. We aimed to define the association between early seizures and subsequent brain growth. METHODS: Infants <30 weeks gestation underwent 72 h of prospective amplitude-integrated electroencephalography (aEEG) monitoring, term-equivalent age (TEA) magnetic resonance imaging (MRI), and 2-year neurodevelopmental testing. Seizures were defined as trains of sharp waves >10 s, evolving in frequency/amplitude/morphology, and identified using automated algorithms with manual review. Using T2-weighted images, cortical surface area (CSA) and gyrification index (GI) were calculated and volumes were segmented into five tissue classes: cerebrospinal fluid, gray matter, white matter (WM), deep nuclear gray matter, and cerebellum. Correlations between total seizure burden and tissue-specific volumes were evaluated, controlling for clinical variables of interest. RESULTS: Ninety-nine infants underwent aEEG/MRI assessments (mean GA = 26.3 weeks, birthweight = 899 g). Seizure incidence was 55% with a median of two events; median length = 66 s and mean burden = 285 s. Greater seizure burden was associated with smaller CSA and volumes across all tissue types, most prominently in WM (R2 = -0.603, p < 0.01), even after controlling for confounders. There was no association with GI. CONCLUSIONS: Seizures in preterm infants are common and associated with smaller TEA brain volumes. This relationship was strongest for WM and independent of clinical factors. IMPACT: Seizures in preterm infants are common. Little is known about the association between early seizures and later brain growth. Greater seizure burden is linked with smaller volumes of all brain tissue types, most prominently the WM. This relationship is true even controlling for other factors. Additional study is needed to identify the optimal EEG monitoring and seizure treatment strategy for improved brain growth and neurodevelopmental outcomes.

Antibiotic spectrum index: A new tool comparing antibiotic use in three NICUs.

BACKGROUND: Antibiotics are widely used in very low-birth-weight infants (VLBW, <1500 g), and excess exposure, particularly to broad-spectrum antibiotics, is associated with significant morbidity. An antibiotic spectrum index (ASI) quantifies antibiotic exposure by relative antimicrobial activity, adding information to exposure measured by days of therapy (DOT). We compared ASI and DOT across multiple centers to evaluate differences in antibiotic exposures. METHODS: We extracted data from patients admitted to 3 level-4 NICUs for 2 years at 2 sites and for 1 year at a third site. We calculated the ASI per antibiotic days and DOT per patient days for all admitted VLBW infants <32 weeks gestational age. Clinical variables were compared as percentages or as days per 1,000 patient days. We used Kruskal-Wallis tests to compare continuous variables across the 3 sites. RESULTS: Demographics were similar for the 734 VLBW infants included. The site with the highest DOT per patient days had the lowest ASI per antibiotic days and the site with the highest mortality and infection rates had the highest ASI per antibiotic days. Antibiotic utilization varied by center, particularly for choice of broad-spectrum coverage, although the organisms causing infection were similar. CONCLUSION: An antibiotic spectrum index identified differences in prescribing practice patterns among 3 NICUs unique from those identified by standard antibiotic use metrics. Site differences in infection rates and unit practices or guidelines for prescribing antibiotics were reflected in the ASI. This comparison uncovered opportunities to improve antibiotic stewardship and demonstrates the utility of this metric for comparing antibiotic exposures among NICU populations.