Reliability in cardiotocography interpretation - impact of extended on-site education in addition to web-based learning: an observational study

UK student midwives' theoretical knowledge, confidence, and experience of intermittent auscultation of the fetal heart rate during labour: An online cross-sectional survey

AIM

This study aimed to explore student midwives' theoretical knowledge of intrapartum intermittent auscultation, their confidence in, and their experience of this mode of fetal monitoring.

DESIGN AND SETTING

An online cross-section survey with closed and open questions. Descriptive statistics were used to analyse participants' intermittent auscultation knowledge, confidence, and experience. Reflexive thematic analysis was used to identify patterns within the free text about participants' experiences.

PARTICIPANTS

Undergraduate midwifery students (n = 303) from Nursing and Midwifery Council-approved educational institutions within the United Kingdom.

FINDINGS

Most participants demonstrated good theoretical knowledge. They had witnessed the technique being used in clinical practice, and when performed, the practice was reported to be in line with national guidance. In closed questions, participants reported feeling confident in their intermittent auscultation skills; however, these data contrasted with free-text responses.

CONCLUSION

This cross-sectional survey found that student midwives possess adequate knowledge of intermittent auscultation. However, reflecting individual clinical experiences, their confidence in their ability to perform intermittent auscultation varied. A lack of opportunity to practice intermittent auscultation, organisational culture, and midwives' preferences have caused student midwives to question their capabilities with this essential clinical skill, leaving some with doubt about their competency close to registration.

Machine learning on cardiotocography data to classify fetal outcomes: A scoping review

INTRODUCTION

Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation.

MATERIALS AND METHOD

We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria.

RESULTS

We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study.

CONCLUSION

ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.

Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection

Fetal hypoxia can cause damaging consequences on babies' such as stillbirth and cerebral palsy. Cardiotocography (CTG) has been used to detect intrapartum fetal hypoxia during labor. It is a non-invasive machine that measures the fetal heart rate and uterine contractions. Visual CTG suffers inconsistencies in interpretations among clinicians that can delay interventions. Machine learning (ML) showed potential in classifying abnormal CTG, allowing automatic interpretation. In the absence of a gold standard, researchers used various surrogate biomarkers to classify CTG, where some were clinically irrelevant. We proposed using Apgar scores as the surrogate benchmark of babies' ability to recover from birth. Apgar scores measure newborns' ability to recover from active uterine contraction, which measures appearance, pulse, grimace, activity and respiration. The higher the Apgar score, the healthier the baby is.We employ signal processing methods to pre-process and extract validated features of 552 raw CTG. We also included CTG-specific characteristics as outlined in the NICE guidelines. We employed ML techniques using 22 features and measured performances between ML classifiers. While we found that ML can distinguish CTG with low Apgar scores, results for the lowest Apgar scores, which are rare in the dataset we used, would benefit from more CTG data for better performance. We need an external dataset to validate our model for generalizability to ensure that it does not overfit a specific population.Clinical Relevance- This study demonstrated the potential of using a clinically relevant benchmark for classifying CTG to allow automatic early detection of hypoxia to reduce decision-making time in maternity units.

Analysis of antepartum cardiotocography based on S/k proportions and probability in 20 minutes

Abstract Objectives: although mortality and perinatal asphyxia in newborns have been considerably reduced, there are still deficiencies in screening and diagnosis methods for intrapartum fetal well being that aim to detect its early alterations. Therefore, the purpose of this research was to apply a methodology based on probability and entropy and confirm its capacity to detect normal and abnormal fetal cardiac dynamics from 20-minute cardiotocographic tracings. Methods: 80 cardiotocographic tracings of pregnant women in the last trimester were collected, of which the minimum and maximum fetal heart rate were evaluated every 10 seconds, as well as its repetitions along with their probability and the diagnostic S/k ratio. Finally, the statistical analysis was carried out to establish the diagnostic capacity of the method concerning the clinical evaluation and interpretation of the cardiotocographic tracing, taken as the Gold Standard. Results: it was confirmed that S/k ratio values differentiated normal from abnormal fetal cardiac dynamics with sensitivity and specificity values of 100% and a Kappa coefficient of 1. Conclusion: the applicability of a diagnostic mathematical method of cardiotocography was confirmed, which suggests its implementation in the clinical context to detect alterations in fetal well-being in 20 minutes.

Training in the use of intrapartum electronic fetal monitoring with cardiotocography: systematic review and meta‐analysis

Background

Sub‐optimal classification, interpretation and response to intrapartum electronic fetal monitoring using cardiotocography are known problems. Training is often recommended as a solution, but there is lack of clarity about the effects of training and which type of training works best.

Objectives

Systematic review of the effects of training healthcare professionals in intrapartum cardiotocography (PROSPERO protocol: CRD42017064525).

Search strategy

CENTRAL, Cochrane Library, MEDLINE, EMBASE, PsycINFO, British Nursing Database, CINAHL, ERIC, Scopus, Web of Science, ProQuest, grey literature and ongoing clinical trials were searched.

Selection criteria

Primary studies that reported impact of training healthcare professionals in intrapartum cardiotocography. Title/abstract, full‐text screening and quality assessment were conducted in duplicate.

Data collection and analysis

Data were synthesised both narratively and using meta‐analysis. Risk of bias and overall quality were assessed with the Mixed Methods Appraisal Tool and GRADE.

Main results

Sixty‐four studies were included. Overall, training and reporting were heterogeneous, the outcomes evaluated varied widely and study quality was low. Five randomised controlled trials reported that training improved knowledge of maternity professionals compared with no training, but evidence was of low quality. Evidence for the impact of cardiotocography training on neonatal and maternal outcomes was limited, showed inconsistent effects, and was of low overall quality. Evidence for the optimal content and method of delivery of training was very limited.

Conclusions

Given the scale of harm and litigation claims associated with electronic fetal monitoring, the evidence‐base for training requires improvement. It should address intervention design, evaluation of clinical outcomes and system‐wide contexts of sub‐optimal practice.

Tweetable abstract

Training in fetal monitoring: systematic review finds little evidence of impact on neonatal outcomes.

Training in fetal monitoring: systematic review finds little evidence of impact on neonatal outcomes.

The Development and Psychometric Evaluation of the Electronic Fetal Monitoring Knowledge Scale

BACKGROUND

The aim of this study was to develop and assess the psychometric properties of the Electronic Fetal Monitoring Knowledge Scale (EFMKS), a self-report and short instrument measuring knowledge concerning Electronic Fetal Monitoring (EFM). Methods. The EFMKS was developed in a three-phase process by using an integrated mixed-methods approach that included literature reviews, professional focus groups, expert consultations and a psychometric survey evaluation. The psychometric evaluation was conducted by recruiting a sample of 128 professionals (midwives and doctors). Content validity, exploratory factor analysis, discriminant and construct validity, test-retest reliability and internal consistency were explored.

RESULTS

The expert panel determined that the content validity was satisfactory. The final 10-item scale consisted of three factors explaining 73% of the total variance in the data. Discriminant validity was satisfactory. Internal consistency reliability (α = 0.89) and test-retest reliability (0.85) were satisfactory. The majority of the midwives and the obstetricians had a good level of knowledge while approximately one third of them had a low level of knowledge in EFM.

CONCLUSION

The EFMK demonstrated good content validity, an easily interpretable three-factor structure, high internal consistency, high test-retest reliability, and satisfactory discriminant and construct validity with sample characteristics. The EFMKS may be used for evaluating the EFM knowledge of health professionals and for identifying the areas of their knowledge gap. Based on study findings, an annual multi-professional CTG training is necessary for all intrapartum staff and in particular for the midwives and doctors with shorter clinical experience in the labor ward.

[Fetal heart rate analysis: Evaluation of an in situ training program on cardiotocography interpretation during labor in the Auvergne-Rhône-Alpes region (France)]

OBJECTIVES

To evaluate an in situ training program on caradiotocography interpretation during labor in the Auvergne-Rhône-Alpes region (France).

METHODS

Fifteen hospital maternity unit took part to an "outreach visit" training on fetal cardiotocography interpretation between November 2011 and 2015. Professionals were asked to answer to a 10 questions test based on the French classification of fetal heart rate, at inclusion (Test 0: T0), immediately after (Test 1: T1), and long time after the training (Test 2: T2). The mean score for each maternity (T0, T1, T2) was compared individually. Subgroup analysis considered the level of perinatal care of each maternity (level 1 or 2) and the type of practice (public or private).

RESULTS

The study included 332 healthcare professionals belonging to 8 level 1 (53.5%) and 7 level 2 (47.7%) maternity units. The T0 mean score was 4.79 (IC 95% [4.54; 5.02]) instead of 6.71(IC 95% [6.49; 6.93]) at T1 (P<0.05). Seventeen professionals (22.9%) answered T2 with a mean time of 35.2 months (Median value: 40 months) and a mean score of 5.32. The mean score was significantly higher at T2 than at T0 (5.32-IC 95%[4.94-5.70] (P<0.001) but lower than the score at T1 (P<0.05).

CONCLUSION

An "outreach visit" training on fetal cardiotocography interpretation improves theknowledge of healthcare professionals at short and long term.

Interobserver agreement in analysis of cardiotocograms recorded during trial of labor after cesarean

Introduction: To examine interobserver agreement in intrapartum cardiotocography (CTG) classification in women undergoing trial of labor after a cesarean section (TOLAC) at term with or without complete uterine rupture. Materials and methods: Nineteen blinded and independent Danish obstetricians assessed CTG tracings from 47 women (174 individual pages) with a complete uterine rupture during TOLAC and 37 women (133 individual pages) with no uterine rupture during TOLAC. Individual pages with CTG tracings lasting at least 20 min were evaluated by three different assessors and counted as an individual case. The tracings were analyzed according to the modified version of the Federation of Gynaecology and Obstetrics (FIGO) guidelines elaborated for the use of STAN (ST-analysis). Occurrence of defined abnormalities was recorded and the tracings were classified as normal, suspicious, pathological, or preterminal. The interobserver agreement was evaluated using Fleiss' kappa. Results: Agreement on classification of a preterminal CTG was almost perfect. The interobserver agreement on normal, suspicious or pathological CTG was moderate to substantial. Regarding the presence of severe variable decelerations, the agreement was moderate. No statistical difference was found in the interobserver agreement between classification of tracings from women undergoing TOLAC with and without complete uterine rupture. Conclusions: The interobserver agreement on classification of CTG tracings from high-risk deliveries during TOLAC is best for assessment of a preterminal CTG and the poorest for the identification of severe variable decelerations.