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.
Competing Interests: Declaration of Competing Interest Some authors have financial conflicts of interest. JRM and DEL own stock in Medical Prediction Sciences Corporation. JRM is a consultant for Nihon Kohden Digital Health Solutions, proceeds donated to the University of Virginia. ZAV is a consultant for Medtronic. All other authors have no financial conflicts to disclose. No authors have any nonfinancial conflicts of interest to disclose. Funding Support: We acknowledge the following NIH grants for funding the work presented in this manuscript. University of Virginia (NCT03174301): U01 HL133708, K23 HD097254, HL133708-05S1. Case Western Reserve University: U01 HL133643. Northwestern University: U01 HL133704. University of Alabama at Birmingham: U01 HL133536, K23 HL157618. University of Miami: U01 HL133689. Washington University: U01 HL133700, F.
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