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

Koppel, Robert; Debnath, Shubham; Zanos, Theodoros P.; Saadi, Nafeesa; Potak, Debra; Meirowitz, Natalie; Weinberger, Barry

doi:10.1007/s10877-020-00625-5

Cited by 1 publication

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 12 , 13 We have shown that they can detect dynamic physiological changes during labor in real time, while correlating well with manual assessments. 14 Of note, we found that continuous measurements often detect fevers earlier than routine manual measurements by nurses. In addition, continuous measurements detect transient fevers that are missed by episodic measurements.…”

Section: Introductionmentioning

confidence: 67%

“…Our previously published work has shown that episodes of fever are often unrecognized or missed by manual data collection, and using continuous vital sign data during labor allows detection of those cases, justifying the relatively high incidence of low-grade fevers in this randomly enrolled cohort. 14 In this work, we demonstrate the use of HRV to augment this fever detection further, for up to 5 hours before the continuous temperature measurements alone. This model can facilitate early identification of newborns at risk for EOS, because maternal fever during labor is the strongest known predictor of EOS.…”

Section: Discussionmentioning

confidence: 85%

“…Prior work details the validation of the continuously measured vital signs, compared to the corresponding episodically measured vital signs, and also quantifies the number fevers detected by either or both vital measurement methods. 14 …”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 85%