Factors associated with intrapartum ZigZag pattern of fetal heart rate: A retrospective one-year cohort study of 5150 singleton childbirths

Computer Vision for Identification of Increased Fetal Heart Variability in Cardiotocogram

INTRODUCTION

Increased fetal heart rate variability (IFHRV), defined as fetal heart rate (FHR) baseline amplitude changes of >25 beats per minute with a duration of ≥1 min, is an early sign of intrapartum fetal hypoxia. This study evaluated the level of agreement of machine learning (ML) algorithms-based recognition of IFHRV patterns with expert analysis.

METHODS

Cardiotocographic recordings and cardiotocograms from 4,988 singleton term childbirths were evaluated independently by two expert obstetricians blinded to the outcomes. Continuous FHR monitoring with computer vision analysis was compared with visual analysis by the expert obstetricians. FHR signals were graphically processed and measured by the computer vision model labeled SALKA.

RESULTS

In visual analysis, IFHRV pattern occurred in 582 cardiotocograms (11.7%). Compared with visual analysis, SALKA recognized IFHRV patterns with an average Cohen's kappa coefficient of 0.981 (95% CI: 0.972-0.993). The sensitivity of SALKA was 0.981, the positive predictive rate was 0.822 (95% CI: 0.774-0.903), and the false-negative rate was 0.01 (95% CI: 0.00-0.02). The agreement between visual analysis and SALKA in identification of IFHRV was almost perfect (0.993) in cases (N = 146) with neonatal acidemia (i.e., umbilical artery pH <7.10).

CONCLUSIONS

Computer vision analysis by SALKA is a novel ML technique that, with high sensitivity and specificity, identifies IFHRV features in intrapartum cardiotocograms. SALKA recognizes potential early signs of fetal distress close to those of expert obstetricians, particularly in cases of neonatal acidemia.

Is the fetus fit for labor? Introducing fast-and-frugal trees (FFTrees) to simplify triage of women for STAN monitoring: An interobserver agreement comparison with traditional classification

INTRODUCTION

It is a shortcoming of traditional cardiotocography (CTG) classification table formats that CTG traces are frequently classified differently by different users, resulting in poor interobserver agreements. A fast-and-frugal tree (FFTree) flow chart may help provide better concordance because it is straightforward and has clearly structured binary questions with understandable "yes" or "no" responses. The initial triage to determine whether a fetus is suitable for labor when utilizing fetal ECG ST analysis (STAN) is very important, since a fetus with restricted capacity to respond to hypoxic stress may not generate STAN events and therefore may become falsely negative. This study aimed to compare physiology-focused FFTree CTG interpretation with FIGO classification for assessing the suitability for STAN monitoring.

MATERIAL AND METHODS

A retrospective study of 36 CTG traces with a high proportion of adverse outcomes (17/36) selected from a European multicenter study database. Eight experienced European obstetricians evaluated the initial 40 minutes of the CTG recordings and judged whether STAN was a suitable fetal surveillance method and whether intervention was indicated. The experts rated the CTGs using the FFTree and FIGO classifications at least 6 weeks apart. Interobserver agreements were calculated using proportions of agreement and Fleiss' kappa (κ).

RESULTS

The proportions of agreement for "not suitable for STAN" were for FIGO 47% (95% confidence interval [CI] 42%-52%) and for FFTree 60% (95% CI 56-64), ie a significant difference; the corresponding figures for "yes, suitable" were 74% (95% CI 71-77) and 70% (95% CI 67-74). For "intervention needed" the figures were 52% (95% CI 47-56) vs 58% (95% CI 54-62) and for "expectant management" 74% (95% CI 71-77) vs 72% (95% CI 69-75). Fleiss' κ agreement on "suitability for STAN" was 0.50 (95% CI 0.44-0.56) for the FIGO classification and 0.57 (95% CI 0.51-0.63) for the FFTree classification; the corresponding figures for "intervention or expectancy" were 0.53 (95% CI 0.47-0.59) and 0.57 (95% CI 0.51-0.63).

CONCLUSIONS

The proportion of agreement among expert obstetricians using the FFTree physiological approach was significantly higher compared with the traditional FIGO classification system in rejecting cases not suitable for STAN monitoring. That might be of importance to avoid false negative STAN recordings. Other agreement figures were similar. It remains to be shown whether the FFTree simplicity will benefit less experienced users and how it will work in real-world clinical scenarios.

Increased variability of fetal heart rate during labour: a review of preclinical and clinical studies

Increased fetal heart rate variability (FHRV) in intrapartum cardiotocographic recording has been variably defined and poorly understood, limiting its clinical utility. Both preclinical (animal) and clinical (human) evidence support that increased FHRV is observed in the early stage of intrapartum fetal hypoxaemia but can also be observed in a subset of fetuses during the preterminal stage of repeated hypoxaemia. This review of available evidence provides data and expert opinion on the pathophysiology of increased FHRV, its clinical significance and a stepwise approach regarding the management of this pattern, and propose recommendations for standardisation of related terminology.

Intrapartal cardiotocographic patterns and hypoxia-related perinatal outcomes in pregnancies complicated by gestational diabetes mellitus

AIMS

In previous reports, cardiotocographic (CTG) fetal heart rate (FHR) monitoring has shown only limited benefits in decreasing adverse perinatal outcomes in pregnancies complicated by gestational diabetes mellitus (GDM). The aim of the present study was to evaluate whether an association exists between the recently reported ZigZag pattern (FHR baseline amplitude changes of > 25 bpm with a duration of 2-30 min) and asphyxia-related neonatal outcomes in GDM pregnancies.

METHODS

Intrapartal CTGs were recorded in a one-year cohort of 5150 singleton childbirths. The following CTG changes were evaluated: ZigZag pattern, saltatory pattern, late decelerations, episodes of tachycardia and bradycardia, reduced variability, and uterine tachysystole. The cohort was divided into three groups: women with GDM, women with normal oral glucose tolerance test (OGTT), and women with no OGTT performed. Umbilical artery (UA) blood gases, Apgar scores, neonatal respiratory distress, and neonatal encephalopathy were used as outcome variables.

RESULTS

GDM was diagnosed in 624 (12.1%), OGTT was normal in 4115 (79.9%), and OGTT was not performed in 411 (8.0%) women. Hypoxia-related ZigZag patterns (OR 1.94, 95% CI 1.64-2.34) and late decelerations (OR 1.65, 95% CI 1.27-2.13) of FHR, as well as a greater risk of fetal asphyxia (UA pH < 7.10 and/or UA BE < -12.0 meq/L and/or Apgar scores < 7 at 5-min) (OR 6.64, 95% CI 1.84-12.03) were observed in those with GDM compared with those without GDM.

CONCLUSIONS

GDM is associated with intrapartal ZigZag pattern and late decelerations, cord blood acidemia and low 5-min Apgar scores at birth indicating increased occurrence of fetal hypoxia in GDM pregnancies.