Continuous Electronic Fetal Monitoring during Labor: A Critique and a Reply to Contemporary Proponents

Medical Malpractice and the Neurologist: An Introduction

This article provides an overview of current malpractice trends in neurology as well as non-malpractice and forensic liability concerns. It is more important for clinicians to recognize the common patient care scenarios that are likely to precipitate lawsuits rather than memorize arcane legal principles. Therefore, this article offers an introduction to malpractice jurisprudence as well as a general overview of current litigation trends and a review of the role and duties of a neurologist serving as an expert witness. The next article highlights mitigation strategies for the most prevalent neurologic misadventures.

Resistance to Change

Advances in medical technology do not follow a smooth process and are highly variable. Implementation can occasionally be rapid, but often faces varying degrees of resistance resulting at the very least in delayed implementation. Using qualitative comparative analysis, we have evaluated numerous technological advances from the perspective of how they were introduced, implemented, and opposed. Resistance varies from benign - often happening because of inertia or lack of resources to more active forms, including outright opposition using both appropriate and inappropriate methods to resist/delay changes in care. Today, even public health has become politicized, having nothing to do with the underlying science, but having catastrophic results. Two other corroding influences are marketing pressure from the private sector and vested interests in favor of one outcome or another. This also applies to governmental agencies. There are a number of ways in which papers have been buried including putting the thumb on the scale where reviewers can sabotage new ideas. Unless we learn to harness new technologies earlier in their life course and understand how to maneuver around the pillars of obstruction to their implementation, we will not be able to provide medical care at the forefront of technological capabilities.

The social organisation of decision-making about intrapartum fetal monitoring: An Institutional Ethnography

BACKGROUND

International guidelines recommend intrapartum cardiotocograph (CTG) monitoring for women at risk for poor perinatal outcome. Research has not previously addressed how midwives and obstetricians enable or hinder women's decision-making regarding intrapartum fetal monitoring and how this work is structured by external organising factors.

AIM

To examine impacts of policy and research texts on midwives' and obstetricians' work with labouring women related to intrapartum fetal monitoring decision-making.

METHODS

We used a critical feminist qualitative methodology known as Institutional Ethnography (IE). The research was conducted in an Australian tertiary maternity service. Data collection included interviews, observation, and texts relating to midwives' and obstetricians' work with the fetal monitoring system. Textual mapping was used to explain how midwives' and obstetricians' work was organised to happen the way it was.

FINDINGS

CTG monitoring was initiated predominantly by midwives applying mandatory policy. Midwives described reluctance to inform labouring women that they had a choice of fetal monitoring method. Discursive approaches used in a national fetal surveillance guideline, a Cochrane systematic review, and the largest randomised controlled trial regarding CTG monitoring in labour generated and reproduced assumptions that clinicians, not labouring women, were the appropriate decision-maker regarding fetal monitoring in labour.

DISCUSSION AND CONCLUSION

Guidelines structured midwives' and obstetricians' work in a manner that undermined women's participation in decisions about fetal monitoring method. Intrapartum fetal monitoring guidelines should be critically reviewed to ensure they encourage and enable midwives and obstetricians to support women to make decisions about intrapartum care.

Changing Perspectives of Electronic Fetal Monitoring

The delivery of healthy babies is the primary goal of obstetric care. Many technologies have been developed to reduce both maternal and fetal risks for poor outcomes. For 50 years, electronic fetal monitoring (EFM) has been used extensively in labor attempting to prevent a large proportion of neonatal encephalopathy and cerebral palsy. However, even key opinion leaders admit that EFM has mostly failed to achieve this goal. We believe this situation emanates from a fundamental misunderstanding of differences between screening and diagnostic tests, considerable subjectivity and inter-observer variability in EFM interpretation, failure to address the pathophysiology of fetal compromise, and a tunnel vision focus. To address these suboptimal results, several iterations of increasingly sophisticated analyses have intended to improve the situation. We believe that part of the continuing problem is that the focus of EFM has been too narrow ignoring important contextual issues such as maternal, fetal, and obstetrical risk factors, and increased uterine contraction frequency. All of these can significantly impact the application of EFM to intrapartum care. We have recently developed a new clinical approach, the Fetal Reserve Index (FRI), contextualizing EFM interpretation. Our data suggest the FRI is capable of providing higher accuracy and earlier detection of emerging fetal compromise. Over time, artificial intelligence/machine learning approaches will likely improve measurements and interpretation of FHR characteristics and other relevant variables. Such future developments will allow us to develop more comprehensive models that increase the interpretability and utility of interfaces for clinical decision making during the intrapartum period.

Current Resources for Evidence-Based Practice, July 2021

An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of how fetal monitoring may lead to overuse of birth-related interventions, commentaries on reviews focused on bedsharing, and women's lifetime estrogen exposure and risk of cardiovascular mortality.

Total intrapartum fetal reperfusion time (fetal resilience) and neonatal acidemia

OBJECTIVE

The main objective is to study the predictive capacity of intrapartum total fetal reperfusion (fetal resilience) by itself or in combination with other parameters as a predictor of neonatal acidemia.

STUDY DESIGN

A retrospective case-control study was carried out at the Miguel Servet University Hospital (Zaragoza, Spain) on a cohort of 5694 pregnant women between June 2017 and October 2018. Maternal, perinatal, and cardiotocographic records were collected. Two reviewers blindly described the monitors with the American College of Obstetricians and Gynecologists (ACOG) categorizations and parameters and the non-ACOG parameters. Neonatal acidemia was defined as pH <7.10. The parameters analyzed to predict acidemia were evaluated using the sensitivity for specificity 90% value, and the area under the receiver operating characteristic curve.

RESULTS

We recorded 192 infants with acidemia, corresponding to a global acidemia rate of 3.4%. Of these, 72 were excluded for lack of criteria, leaving 120 patients with arterial acidemia included in the study and 258 in the control group. The sensitivity (specificity 90%) of detection of acidemia was 42% for the ACOG III categorization (AUC, 0.524: 95% CI, 0.470-0.578), 24% for fetal reperfusion (AUC, 0.704: 95% CI, 0.649-0.759), 27% for total area of decelerations (AUC, 0.717: 95% CI, 0.664-0.771) and 50% for the multivariate model built from total reperfusion time (AUC, 0.826: 95% CI, 0.783-0.869). The total reperfusion time corresponding to a false negative rate of 10% is 23.75 min, with 28% of fetuses above this time. The AUC and sensitivity for a false negative rate of 10% are equivalent for deceleration area and time of reperfusion (p = .504).

CONCLUSION

The total reperfusion time (fetal resilience) and total deceleration area are non-ACOG parameters with a good predictive ability for neonatal acidemia, higher than the ACOG III classification and without statistical differences between them. The discrimination ability of total reperfusion time can be improved using a multivariate model. As a cutoff for its use we suggest 23.75 min in 30 min corresponding to an acidemic classification rate of 90%. New parameters in combination with other maternal, obstetrics, or fetal variables, are required for the interpretation of fetal well-being.

Current Resources for Evidence-Based Practice, November 2020

An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of diversity in the maternity care workforce and commentaries on reviews focused on burnout in midwifery and a cross-national comparison of guidelines for uncomplicated childbirth.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.