Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability.

Objectives: Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever. Methods: Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women (n = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats. Results: Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly (p < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs. Conclusions: HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring.

Efficacy of continuous monitoring of maternal temperature during labor using wireless axillary sensors.

Neonatal early onset sepsis (EOS) occurs in 0.5-0.8/1000 live births and is a major cause of morbidity and mortality. Its presenting signs in newborns are non-specific, so risk assessment before birth is essential. Maternal fever during labor is the strongest predictor of EOS, but the current standard is for infrequent manual determinations of temperature. We aimed to determine whether continuous measurement of temperature during labor is feasible, accurate, and more effective than manual measurements for detecting fever. Women were recruited on admission in labor at > 35 weeks gestational age, with < 6 cm cervical dilation. Sensors were affixed in the axilla, which transmitted every 4 minutes by Bluetooth to a dedicated tablet. Conventional temperature measurements were taken every 3-6 hours per routine. Of 336 subjects recruited, 155 had both > 4 hours of continuous data and > 2 manual temperature measurements and were included for analysis. Continuous recordings were feasible and correlated well with manual measurements independent of mean temperature. Of 15 episodes of fever > 38 °C detected by both methods, 13 were detected earlier by continuous (9 of those more than 1 hour earlier). Manual measurements missed 32 fevers > 38 °C and 13 fevers > 38.5 °C that were identified by continuous. Continuous measurement of maternal temperature for the duration of labor is practical and accurate. It may be more sensitive for identifying infants at risk for EOS than the current practice, enabling earlier and more effective targeted treatment of affected infants.