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.

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.