Objective
The objective of the study was to compare interpretability of 2 intrapartum abdominal fetal heart rate–monitoring strategies. We hypothesized that an external fetal electrocardiography monitoring system, a newer technology using wireless abdominal pads, would generate more interpretable fetal heart rate data compared with standard external Doppler fetal heart rate monitoring (standard external monitoring).
Study Design
We conducted a randomized controlled trial at 4 Utah hospitals. Patients were enrolled at labor admission and randomized in blocks based on body mass index to fetal electrocardiography or standard external monitoring. Two reviewers, blinded to study allocation, reviewed each fetal heart rate tracing. The primary outcome was the percentage of interpretable minutes of fetal heart rate tracing. An interpretable minute was defined as >25% fetal heart rate data present and no more than 25% continuous missing fetal heart rate data or artifact present. Secondary outcomes included the percentage of interpretable minutes of fetal heart rate tracing obtained while on study device only, the number of device adjustments required intrapartum, clinical outcomes, and patient/provider device satisfaction. We determined that 100 patients per arm (200 total) would be needed to detect a 5% difference in interpretability with 95% power.
Results
A total of 218 women were randomized, 108 to fetal electrocardiography and 110 to standard external monitoring. Device setup failure occurred more often in the fetal electrocardiography group (7.5% [8 of 107] vs 0% [0 of 109] for standard external monitoring). There were no differences in the percentage of interpretable tracing between the 2 groups. However, fetal electrocardiography produced more interpretable fetal heart rate tracing in subjects with a body mass index ≥30 kg/m 2 . When considering the percentage of interpretable minutes of fetal heart rate tracing while on study device only, fetal electrocardiography outperformed standard external monitoring for all subjects, regardless of maternal body mass index. Maternal demographics and clinical outcomes were similar between arms. In the fetal electrocardiography group, more device changes occurred compared with standard external monitoring (51% vs 39%), but there were fewer nursing device adjustments (2.9 vs 6.2 mean adjustments intrapartum, P < .01). There were no differences in physician device satisfaction scores between groups, but fetal electrocardiography generated higher patient satisfaction scores.
Conclusion
Fetal electrocardiography performed similarly to standard external monitoring when considering percentage of interpretable tracing generated in labor. Furthermore, patients reported overall greater satisfaction with fetal electrocardiography in labor. Fetal electrocardiography may be particularly useful in patients with a body mass index ≥30 kg/m 2 .
Electronic fetal heart rate (FHR) monitoring in labor is a common intrapartum obstetric procedure. Continuous FHR monitoring is used to identify fetuses at risk for intrapartum metabolic compromise and allow for intervention. , The quality of the FHR signal is critical for the appropriate interpretation of characteristics that identify a fetus at risk.
Why was this study conducted?
To compare the interpretability of 2 abdominal fetal heart rate–monitoring devices (fetal electrocardiography vs standard external monitoring).
Key findings
In this randomized clinical comparison, fetal heart rate interpretability was similar between groups, but fetal electrocardiography generated more interpretable data compared with standard external monitoring in women with a body mass index ≥30 kg/m 2 and resulted in higher patient satisfaction.
What does this add to what is known?
Fetal electrocardiography was better accepted by patients and may be a preferable fetal monitoring device in obese patients.
Electronic FHR monitoring devices commonly used in current obstetric practice have several limitations, in particular that the quality of the data obtained is dependent on fetal position, maternal position, and maternal body habitus. Continuous, high-quality data are difficult to obtain in women with high body mass index (BMI). Studies have described the average percentage of time during labor with Doppler ultrasound FHR signal loss to range from 10% to 40%. The variation in signal loss is attributed to changes in maternal and fetal position, stage of labor, and improvement in FHR Doppler signal acquisition over time.
Internal fetal monitors (ie, the fetal scalp electrode) may be used only when amniotic membranes are ruptured and the cervix is sufficiently dilated. These devices are contraindicated in the setting of certain maternal infections and fetal conditions (eg, fetal bleeding diathesis ). These device characteristics may limit the utility of internal monitoring device use in certain patient populations. Similar problems are encountered during attempts to monitor the frequency, strength, and duration of uterine contractions.
Newer devices such as fetal electrocardiography (ECG) and electromyography that utilize technology to capture both the electrical activity of the fetal heart and uterine contractions are available. , These devices assess the fetal R-R interval utilizing wireless, adhesive pads (like ECG pads) in 4–5 locations on the maternal abdomen. This wireless method for FHR monitoring is not dependent upon maternal position, body habitus, or fetal position; thus, the patient may theoretically move to any position without loss of signal. , An additional benefit to this type of device is the continuous maternal heart rate display simultaneous with FHR tracing, thus decreasing maternal and fetal heart rate confusion. ,
Uterine contraction electrical activity is recorded to determine contraction frequency and duration utilizing an external electromyography. These new monitors may require less nursing intervention, avoid invasive monitoring, and may allow for freedom of maternal movement and positioning, which may increase patient satisfaction with their labor experience. A recent, small qualitative study has suggested that the external fetal ECG may be an acceptable method for longer-term fetal monitoring in the ambulatory setting. Whether this translates to the labor unit is unknown.
The objective of this study was to compare the percentage of interpretable FHR tracing data generated by a wireless fetal ECG device (fECG) to standard external monitoring (SEM) approaches (ie, external fetal Doppler ultrasound and tocometer). Our primary hypothesis was that use of fECG would produce more interpretable FHR data when compared with SEM in term, laboring, singleton pregnancies.
Materials and Methods
Study setting and patients
We conducted a pragmatic, randomized controlled trial comparing 2 FHR monitoring strategies: fECG (the Monica Novii wireless patch system (General Electric Company, Milwaukee, WI), a Food and Drug Administration–approved and commercially available device) and conventional Doppler external FHR monitoring and tocometry (SEM). We compared the percentage of interpretable FHR generated from the time of randomization until delivery using both monitoring strategies at 4 Intermountain Healthcare hospitals in Utah (Intermountain Medical Center, LDS Hospital, McKay Dee Hospital, Utah Valley Hospital, Salt Lake City, UT).
Patients eligible for enrollment included pregnant women ≥18 years of age with singleton gestation of ≥37 weeks admitted to labor and delivery for planned vaginal delivery. Patients were excluded if the initial triage assessment revealed fetal distress (category II tracing based on National Institutes of Health consensus criteria), excessive or abnormal vaginal bleeding prior to monitor placement, a history of a prior cesarean delivery, cardiac pacemaker, or skin sensitivity to adhesive. Patients were screened for possible study enrollment at the time of labor and delivery admission or at the time of a routine prenatal care clinic visit. Written informed consent was obtained.
The protocol was approved by the Intermountain Healthcare Institutional Review Board (Salt Lake City, UT) institutional review board number 1050411 and was registered with ClinicalTrials.gov ( ClinicalTrials.gov number: NCT03156608 ).
Randomization and blinding
Randomization occurred in blocks based on BMI (BMI <30 or BMI ≥30 kg/m 2 ) to control for the potential effect of BMI. Women were randomized to either the fECG device or SEM (external Doppler and tocometry) using a computer-generated randomization scheme through REDCap (Research Electronic Data Capture), an electronic data collection tool hosted at Intermountain Healthcare.
Given the distinctly different appearance of the fetal monitoring devices utilized in this study, participants and respective health care providers were not blinded to study device during data collection. However, deidentified FHR tracings were similar in appearance ( Supplemental Figure 1 ). Members of the Intermountain Healthcare Maternal-Fetal Medicine Division assigned to review FHR tracings for interpretability were blinded to participant study arm allocation, intrapartum monitor changes. and labor outcomes.
Study outcomes
The primary outcome was the percentage of interpretable minutes generated on study device during the entire intrapartum period (number of interpretable minutes on study device per total minutes in labor), during which any time spent on an alternative, nonstudy device was considered not interpretable (an intention-to-treat analysis). Secondary outcomes included the percentage of interpretable minutes generated on study device while on study device only (number of interpretable minutes on study device per total minutes on study device only).
The percentage of interpretable 10 minute FHR tracing segments generated in labor and on study device only was also assessed (eg, [number of interpretable 10 minute segments on study device per total 10 minute segments in labor] and [number of interpretable 10 minute segments on study device per number of 10 minute FHR segments on study device only]).
Additional secondary outcomes included the number of study device adjustments by nursing; the need for an alternative monitoring device; and physician, nursing and patient satisfaction with study device as determined by immediate postdelivery Likert scale survey. Maternal outcomes (labor length, labor analgesia, delivery mode) and neonatal outcomes (birthweight, neonatal sex, Apgar score [1 and 5 minutes], newborn intensive care unit admission, and umbilical cord blood gases [when available]) were abstracted from the medical chart following delivery.
Procedures
Maternal demographic information was collected at study enrollment. Study device was placed following randomization, with assigned device remaining in place until delivery or until provider request for device change. In our study, only nurses were trained in study device placement.
In the fECG group, providers could transition to SEM or internal monitoring at their discretion. In the SEM group, providers could switch to internal monitoring (fetal scalp electrode and/or intrauterine pressure catheter) at their discretion. Reason for device change was not protocolized but was recorded via short provider questionnaire.
Device setup was considered successful if the device was placed, initiated appropriately, and no device change was required prior to delivery. Device set up failure occurred when assigned study device did not generate any interpretable tracing at the time of placement despite troubleshooting. All subjects with device setup failures required an alternative monitoring device intrapartum.
Labor nurses recorded the total number of monitor adjustments performed to improve signal quality while on study device. A Likert scale survey assessing user satisfaction with the assigned device was distributed to staff and study participants following delivery.
A complete copy of the intrapartum FHR tracing was collated and deidentified following delivery. A study identification assigned at enrollment was associated with each FHR tracing and was used to link FHR tracing data and clinical data. All FHR tracings were reviewed in blinded fashion by at least 2 members of the Maternal-Fetal Medicine Division.
Each minute of FHR tracing was considered individually. An interpretable minute was defined as >25% FHR data present with no more than 25% continuous missing FHR data or artifact present ( Supplemental Figure 2 ). The FHR tracing was also evaluated in 10 minute segments, in which a 10 minute segment was considered interpretable if baseline heart rate, variability, and periodic changes could be adequately determined by the reviewer. These definitions of interpretable minute and 10 minute segment terminologies were determined via a priori consensus.
Maternal and neonatal outcome data were abstracted from the medical records by trained research staff.
Statistical analysis
Sample size and power estimates
A sample size calculation was performed using the assumption that SEM would yield an average of 90% interpretable tracing. This value was based on previously published literature and after review of 30 complete FHR tracings from our own institution. We assumed fECG would generate 95% interpretable tracing based on preliminary observational data. We determined that a sample size of 100 subjects per group (200 total) would result in 95% power to detect a difference in the percentage of interpretable tracing both devices generated.
Data analysis
Outcomes in the fECG and SEM groups were compared using both univariate and multivariate analysis as appropriate. A Fisher exact test was used to compare categorical variables, and a Student t test was utilized for comparison of continuous variables. A generalized linear mixed effects model was used for comparing individual FHR tracing interpretations using binomial outcomes for readability and other binary outcomes and normal outcomes for numerical responses.
The fixed effect was defined as the device that the patient was randomly assigned, and random effects included the patient and reader providing FHR interpretation to account for correlation within those groupings. The intraclass correlation coefficient was measured to evaluate FHR tracing reviews for interobserver variability. All analyses were performed using R statistical software (version 3.4.3) with lme4 package for R. ,
A brief post hoc analysis of device success plotted across the study period was performed to evaluate for study device learning curve.
Role of the funding source
This was an investigator-initiated, industry-sponsored study (GE Healthcare). GE Healthcare played no role in data collection, analysis, or presentation of results.
Results
Study enrollment occurred between March 6 and June 28, 2017. Of the 289 eligible women approached, 218 women consented to the study. A total of 108 women were randomized to fECG and 110 to SEM as depicted in the trial flow chart ( Figure 1 ). In both groups, a single study participant withdrew prior to device setup. These participants were excluded from final analysis.
There were no statistical differences in maternal demographics between study arms ( Table 1 ). Mean BMI in both groups was approximately 32 kg/m 2 , and labor induction occurred in 80% and 79% of subjects randomized to fECG and SEM, respectively. There were no differences in maternal or neonatal clinical outcomes between study arms including length of labor, delivery mode, and previously defined maternal or fetal complications ( Table 2 ).
| Participant characteristics | Fetal ECG (n =107) | Standard external monitoring (n = 109) |
|---|---|---|
| Age, y, mean (SD) | 29.2 (4.9) | 28.6 (4.9) |
| BMI, kg/m 2 , mean (SD) | 31.7 (5.7) | 31.7 (5.9) |
| Gestational age, wks, mean (SD) | 39.4 (1.1) | 39.4 (1.0) |
| First pregnancy, % | 27.8% | 37.3% |
| Race, % | ||
| White | 87.96% | 91.82% |
| Asian | 2.78% | 0.91% |
| Pacific Islander | 4.63% | 4.55% |
| African American | 0.93% | 0.0% |
| Other | 3.7% | 2.73% |
| Ethnicity, % | 9.3% Hispanic | 9.1% Hispanic |
| Pregnancy complications, % | ||
| None | 83% | 81% |
| Preeclampsia | 1.9% | 1.8% |
| Chronic hypertension | 2.8% | 2.7% |
| Gestational hypertension | 9.3% | 6.4% |
| Gestational diabetes | 3.7% | 6.4% |
| Preexisting diabetes | 0% | 0% |
| Intrauterine growth restriction | 0.93% | 1.8% |
| Oligohydramnios | 9.3% | 1.8% |
| Labor type | ||
| Induced | 86 (80%) | 87 (79%) |
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