LAUSR

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.