Quantitative cardiotocography to improve fetal assessment during labor: a preliminary randomized controlled trial

Ignatov, P; Lutomski, Jennifer E.

doi:10.1016/j.ejogrb.2016.08.023

Cited by 20 publications

(22 citation statements)

References 22 publications

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“…41,42 The median sam ple size was 894 patients (range 100-47 062). Fifteen trials (45%) [25][26][27]31,32,36,39,41,[43][44][45][46]49,51,53 included patients at 36 weeks' ges tation or more, and the remaining included patients at 26-42 weeks' gestation. [28][29][30][33][34][35]38,40,50,52,54,56 Twothirds of trials ( 7 3 % ) i n c l u d e d p a t i e n t s w i t h h i g h r i s k p r e g n a n cies, [25][26][27][28][29][30][31][32][33][34]36,38,39,41,44,47,48,50,51,53,54 3 (9%) included patients with low risk ...…”

Section: Resultsmentioning

confidence: 99%

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Effectiveness of intrapartum fetal surveillance to improve maternal and neonatal outcomes: a systematic review and network meta-analysis

Wattar

Honess

Bunnewell

et al. 2021

CMAJ

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BACKGROUND: Cesarean delivery is the most common surgical procedure worldwide. Intrapartum fetal surveil lance is routinely offered to improve neonatal outcomes, but the effects of different methods on the risk of emergency cesarean deliveries remains uncertain. We conducted a systematic review and network metaanalysis to evaluate the effectiveness of different types of fetal surveillance. METHODS:We searched MEDLINE, Embase and CENTRAL until June 1, 2020, for randomized trials evaluating any intrapartum fetal surveillance method. We performed a network metaanalysis within a frequentist framework. We assessed the quality and network incon sistency of trials. We reported primarily INTERPRETATION: Compared with other types of fetal surveillance, intermittent auscultation seems to reduce emer gency cesarean deliveries in labour without increasing adverse neonatal and maternal outcomes.

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Section: Resultsmentioning

confidence: 99%

“…34 Two methods, cCTG and STANCTG, were evalu ated in only a single trial. 26,36 These methods were there fore excluded from the primary analysis and were investigated in sensitivity analyses. Twentyeight trials reported on all emergency cesarean deliveries in labour and 23 reported on cesarean deliveries for suspected fetal distress.…”

Section: Resultsmentioning

confidence: 99%

Effectiveness of intrapartum fetal surveillance to improve maternal and neonatal outcomes: a systematic review and network meta-analysis

Wattar

Honess

Bunnewell

et al. 2021

CMAJ

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“…A smaller RCT was conducted on a quantitative cardiotocography (qCTG) decision-support system, which enrolled 720 patients ( Ignatov and Lutomski, 2016 ). The qCTG system computes features based on three domains: FHR, FHR micro-fluctuations, and decelerations.…”

Section: Review Of Prior Art In Ai For Ctgmentioning

confidence: 99%

Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

O'Sullivan

Considine

O'Riordan

et al. 2021

Front. Artif. Intell.

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Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation.Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made.Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

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“…Current methods for prenatal detection of fetal acidaemia rely mainly on fetal heart rate (FHR) monitoring. Despite extensive study, no consensus has been reached regarding the interpretation of FHR during labour 5–16 . More recent studies using artificial intelligence technologies to enhance FHR interpretation did not show major improvements in preventing severe adverse neonatal outcomes (SANO) 11–16 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study

Guedalia

Sompolinsky

Persky

et al. 2021

BJOG

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Objective To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour. Design Retrospective Electronic‐Medical‐Record (EMR) ‐based study. Population A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.8%) deliveries with SANO. Methods A gradient boosting model was created, analysing 21 million data points from antepartum features (e.g. gravidity and parity) gathered at admission to the delivery unit, and intrapartum data (e.g. cervical dilatation and effacement) gathered during the first stage of labour. Deliveries were allocated to high‐risk and low‐risk groups based on the Youden index to maximise sensitivity and specificity. Main outcome measures SANO was defined as either umbilical cord pH levels ≤7.1 or 1‐minute or 5‐minute Apgar score ≤7. Results The model for prediction of SANO yielded an area under the receiver operating curve (AUC) of 0.761 (95% CI 0.748–0.774). A third of the cohort (33.5%, n = 24 721) were allocated to a high‐risk group for SANO, which captured up to 72.1% of these cases (odds ratio 5.3, 95% CI 4.7–6.0; high‐risk versus low‐risk groups). Conclusions Data acquired throughout the first stage of labour can be used to predict SANO during the second stage of labour using a machine learning model. Stratifying parturients at the beginning of the second stage of labour in a ‘time out’ session, can direct a personalised approach to management of this challenging aspect of labour, as well as improve allocation of staff and resources. Tweetable abstract Personalised prediction score for severe adverse neonatal outcomes in labour using machine learning model.

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Quantitative cardiotocography to improve fetal assessment during labor: a preliminary randomized controlled trial

Cited by 20 publications

References 22 publications

Effectiveness of intrapartum fetal surveillance to improve maternal and neonatal outcomes: a systematic review and network meta-analysis

Effectiveness of intrapartum fetal surveillance to improve maternal and neonatal outcomes: a systematic review and network meta-analysis

Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study

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