Errors and pitfalls in reading the cardiotocographic tracing

Study on the clinical value of the wearable foetal electrocardiogram monitoring system

BACKGROUND

Electronic foetal monitoring (EFM), a method to monitor foetal intrauterine conditions and foetal reserve capacity, is the most extensively used intrauterine monitoring technology in obstetrics.

OBJECTIVE

This study aims to compare the Thoth wearable foetal electrocardiogram (foetal ECG [FECG]) monitoring system with a traditional Doppler foetal heart monitoring system before labour to investigate their respective values in clinical application.

METHODS

A total of 393 pregnant women admitted to our hospital between 2020 and 2022 participated in this study. They were recruited using the convenience sampling method. We employed a paired design to assess the confusion rate, trend overlap, and foetal heart rate/ECG monitoring consistency, whereas a completely randomised design was used to measure pregnancy outcome indicators. The participants were divided into two groups using a random number table: the Thoth group (n= 196) and the traditional Doppler group (n= 197). Each group was monitored using the corresponding system.

RESULTS

The Thoth monitor demonstrated a lower confusion rate compared with the traditional Doppler monitor (0.25% vs 2.04%; χ2= 5.508, P= 0.019). The trend overlap in foetal heart rates was consistently 100%, with 91.2% of readings showing a consistency rate of ⩾ 95%. Additionally, the Thoth monitor recorded a higher cumulative interruption time in the foetal heart rate curve (12.13 ± 2.22 vs 21.02 ± 2.34; t= 18.471, P< 0.001) and more abnormal ECGs (21.21 ± 4.32 vs 18.21 ± 2.91; t= 7.582, P< 0.001) than the traditional Doppler system.

CONCLUSION

The Thoth wearable FECG monitor offers several advantages over the traditional Doppler foetal heart monitoring system. These include a reduced confusion rate, more accurate data collection, a lower rate of clinical misjudgement, reduced workload for medical staff, and enhanced comfort during vaginal delivery. The rates of emergency caesarean sections and neonatal asphyxia in the Thoth group were marginally lower than those in the Doppler group, which may be attributed to issues such as ECG disconnection or interference from the maternal heart rate.

Large-scale analysis of interobserver agreement and reliability in cardiotocography interpretation during labor using an online tool

BACKGROUND

While the effectiveness of cardiotocography in reducing neonatal morbidity is still debated, it remains the primary method for assessing fetal well-being during labor. Evaluating how accurately professionals interpret cardiotocography signals is essential for its effective use. The objective was to evaluate the accuracy of fetal hypoxia prediction by practitioners through the interpretation of cardiotocography signals and clinical variables during labor.

MATERIAL AND METHODS

We conducted a cross-sectional online survey, involving 120 obstetric healthcare providers from several countries. One hundred cases, including fifty cases of fetal hypoxia, were randomly assigned to participants who were invited to predict the fetal outcome (binary criterion of pH with a threshold of 7.15) based on the cardiotocography signals and clinical variables. After describing the participants, we calculated (with a 95% confidence interval) the success rate, sensitivity and specificity to predict the fetal outcome for the whole population and according to pH ranges, professional groups and number of years of experience. Interobserver agreement and reliability were evaluated using the proportion of agreement and Cohen's kappa respectively.

RESULTS

The overall ability to predict a pH level below 7.15 yielded a success rate of 0.58 (95% CI 0.56-0.60), a sensitivity of 0.58 (95% CI 0.56-0.60) and a specificity of 0.63 (95% CI 0.61-0.65). No significant difference in the success rates was observed with respect to profession and number of years of experience. The success rate was higher for the cases with a pH level below 7.05 (0.69) and above 7.20 (0.66) compared to those falling between 7.05 and 7.20 (0.48). The proportion of agreement between participants was good (0.82), with an overall kappa coefficient indicating substantial reliability (0.63).

CONCLUSIONS

The use of an online tool enabled us to collect a large amount of data to analyze how practitioners interpret cardiotocography data during labor. Despite a good level of agreement and reliability among practitioners, the overall accuracy is poor, particularly for cases with a neonatal pH between 7.05 and 7.20. Factors such as profession and experience level do not present notable impact on the accuracy of the annotations. The implementation and use of a computerized cardiotocography analysis software has the potential to enhance the accuracy to detect fetal hypoxia, especially for ambiguous cardiotocography tracings.

A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.