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Neonatal sepsis accounts for significant morbidity and mortality, particularly among premature infants in the Neonatal Intensive Care Unit. Abnormal vital sign patterns serve as physiomarkers of sepsis and provide early warning of illness before overt clinical decompensation. The systemic inflammatory response to pathogens signals the autonomic nervous system, leading to changes in temperature, respiratory rate, heart rate, and blood pressure. In infants with comorbidities of prematurity, vital sign abnormalities often occur in the absence of infection, which confounds sepsis diagnosis. This review will cover the mechanisms of vital sign changes in neonatal sepsis, including the cholinergic anti-inflammatory pathway mediated by the vagus nerve, which is critical to the host response to infectious and inflammatory insults. We will also review the clinical implications of vital sign changes in neonatal sepsis, including their use in early warning scores and systems to direct clinicians to the bedside of infants with physiologic changes that might be due to sepsis.ImpactThis manuscript summarizes and reviews the relevant literature on the physiological manifestations of neonatal sepsis and how we monitor and analyze these through vital signs and advanced analytics.

Artificial intelligence may have a role in the early detection of sepsis in neonates. Machine learning can identify patterns that predict high or increasing risk for clinical deterioration from a sepsis-like illness. In developing this potential addition to NICU care, careful consideration should be given to the data and methods used to develop, validate, and evaluate prediction models. When an AI system alerts clinicians to a change in a patient’s condition that warrants a bedside evaluation, human intelligence and experience come into play to determine an appropriate course of action: evaluate and treat or wait and watch closely. With intelligently developed, validated, and implemented AI sepsis systems, both clinicians and patients stand to benefit. Impact This narrative review highlights the application of AI in neonatal sepsis prediction. It describes issues in clinical prediction model development specific to this population. This article reviews the methods, considerations, and literature on neonatal sepsis model development and validation. Challenges of AI technology and potential barriers to using sepsis AI systems in the NICU are discussed.

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

BackgroundBradycardia and oxygen desaturation episodes are common among preterm very low birth weight (VLBW) infants in the Neonatal Intensive Care Unit (NICU), and their association with adverse outcomes such as bronchopulmonary dysplasia (BPD) is unclear.MethodsFor 502 VLBW infants we quantified bradycardias (HR < 100 for ≥ 4 s) and desaturations (SpO2 < 80% for ≥ 10 s), combined bradycardia and desaturation (BD) events, and percent time in events in the first 4 weeks after birth (32 infant-years of data). We tested logistic regression models of clinical risks (including a respiratory acuity score incorporating FiO2 and level of respiratory support) to estimate the risks of BPD or death and secondary outcomes. We then tested the additive value of the bradycardia and desaturation metrics for outcomes prediction.ResultsBPD occurred in 187 infants (37%). The clinical risk model had ROC area for BPD of 0.874. Measures of desaturation, but not bradycardia, significantly added to the predictive model. Desaturation metrics also added to clinical risks for prediction of severe intraventricular hemorrhage, retinopathy of prematurity and prolonged length of stay in the NICU.ConclusionsOxygen desaturations in the first month of the NICU course are associated with risk of BPD and other morbidities in VLBW infants.

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 (SpO 2 ) 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 SpO 2 , and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO 2 features alone for comparison with HR-SpO 2 models. Results Performance, feature importance, and calibration were similar among modeling methods. All models had favorable external validation performance. The HR-SpO 2 model performed better than models using either HR or SpO 2 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 SpO 2 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 SpO 2 , 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 SpO 2 features provides the best dynamic risk prediction. The results increase understanding of physiologic signatures of neonatal sepsis.

Background: Subtle changes in vital signs and their interactions occur in preterm infants prior to overt deterioration from late-onset septicemia (LOS) or necrotizing enterocolitis (NEC). Optimizing predictive algorithms may lead to earlier treatment. Methods: For 1,065 very-low-birth-weight (VLBW) infants in two neonatal intensive care units (NICUs), mean, SD, and cross-correlation of respiratory rate, heart rate (HR), and oxygen saturation (SpO 2 ) were analyzed hourly (131 infant-years’ data). Cross-correlation (cotrending) between two vital signs was measured allowing a lag of ± 30 s. Cases of LOS and NEC were identified retrospectively ( n = 186) and vital sign models were evaluated for ability to predict illness diagnosed in the ensuing 24 h. Results: The best single illness predictor within and between institutions was cross-correlation of HR-SpO 2 . The best combined model (mean SpO 2 , SDHR, and cross-correlation of HR-SpO 2 ,) trained at one site with ROC area 0.695 had external ROC area of 0.754 at the other site, and provided additive value to an established HR characteristics index for illness prediction (Net Reclassification Improvement: 0.205; 95% confidence interval (CI): 0.113, 0.328). Conclusion: Despite minor inter-institutional differences in vital sign patterns of VLBW infants, cross-correlation of HR-SpO 2 and a 3-variable vital sign model performed well at both centers for preclinical detection of sepsis or NEC.

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. 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. 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. 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.