iPREFACE score: Integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring

The iPREFACE score is useful for predicting fetal acidemia: A retrospective cohort study of 113 patients who underwent emergency cesarean section for non-reassuring fetal status during labor

BACKGROUND

The iPREFACE score may aid in predicting fetal acidemia and neonatal asphyxia in emergency cesarean and vaginal deliveries, which may improve labor management precision in the future.

OBJECTIVE

This study aimed to assess the score use of the iPREFACE as an objective indicator of the need for rapid delivery in cases of repeated abnormal waveforms without concurrent indications for immediate medical intervention during labor.

STUDY DESIGN

This retrospective cohort study was conducted among term (37+ 0 days to 41+6 days) singleton pregnant women who underwent emergency cesarean delivery owing to a nonreassuring fetal status. The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-decision of emergency cesarean delivery score, calculated from a 30-minute cardiotocography waveform before the decision to perform emergency cesarean delivery, and the integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography transducer score, calculated from a 30-minute cardiotocography waveform before cardiotocography transducer removal, were employed. The primary outcome was the assessment of the predictive ability of these scores for fetal acidemia, whereas the secondary outcomes were differences in umbilical artery blood gas findings and postnatal outcomes between the 2 groups, divided by the cutoff values of the integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography score.

RESULTS

The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-decision of emergency cesarean delivery and integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography transducer scores demonstrated the capability to predict an umbilical artery blood pH of <7.2. The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-decision of emergency cesarean delivery and -removal of cardiotocography transducer score, with cutoff values of 37 and 46 points, respectively, exhibited an area under the receiver operating characteristic curve of 0.82 and 0.87, respectively. The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring-removal of cardiotocography transducer group with ≥46 points had higher incidence rates of an umbilical cord artery blood pH of <7.2, <7.1, and <7.0 and neonatal intensive care unit admissions for neonatal asphyxia.

CONCLUSION

The integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring, derived from cardiotocography during an emergency cesarean delivery, may enable clinicians to predict fetal acidemia in cases of nonreassuring fetal status. Improved prediction of fetal acidemia and facilitation of timely intervention hold promise for enhancing the outcomes of mothers and newborns during childbirth. Prospective studies are warranted to establish precise cutoff values and to validate the clinical application of these scores.

Optimal duration of cardiotocography assessment using the iPREFACE score to predict fetal acidemia

Cardiotocography (CTG) applicability to improve fetal outcomes remains controversial. This study aimed to determine the clinically optimal CTG assessment duration using the integrated score index to predict fetal acidemia by intrapartum fetal heart rate monitoring (iPREFACE score). This single-center, retrospective observational study included 325 normal full-term singleton vaginal deliveries at the Toho University Omori Medical Center, from September 2018 to March 2019. The iPREFACE(10), iPREFACE(30), and iPREFACE(60) scores were calculated at 10, 30, and 60 min immediately before delivery. The primary outcome was fetal acidemia (umbilical artery blood pH < 7.2). The secondary outcome was the correlation between all iPREFACE scores and the umbilical artery blood pH, base excess (BE), and lactate values. Patients without accurate CTG findings or with failure of umbilical artery blood sampling immediately after birth were excluded, leaving 145 patients in the final analysis. Of these, 16, three, and two had umbilical artery blood pH of < 7.2, < 7.1, and < 7.0, respectively. All iPREFACE scores significantly correlated with umbilical artery blood pH, BE, and lactate values. iPREFACE(30) had the highest predictive capacity for fetal acidemia, suggesting that 30 min immediately before delivery may be a useful scoring time in clinical practice.

On designing a biosignal-based fetal state assessment system: A systematic mapping study

BACKGROUND AND OBJECTIVE

The patterns present in biosignals, such as fetal heart rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis systems, the variety of approaches and technology usage impairs the decision-making for the fundamental units of the systems. There is a need for an updated overview of studies encompassing the biosignal-based fetal state assessment systems. Therefore, we propose a systematic mapping study to identify and synthesize recent research regarding the building blocks that compose these systems.

METHODS

We followed well-established guidelines to perform a systematic mapping of studies regarding the building-blocks that compose the fetal state assessment systems and published between January 2016 and January 2021. A search string was determined based on the mapping questions and the PI (population and intervention) divisions. The search string was applied in digital libraries covering the fields of computer science, engineering, and medical informatics. Then, we applied the forward snowballing technique to complement the resulting set. This process resulted in 75 primary studies selected from a total of 871 papers.

RESULTS

Selected studies were classified according to the publication types, systems design stages, datasets, and predictive capabilities. The results revealed that (i) The majority of the selected studies refer to the method as a type of publication and there is a lack of validation studies; (ii) The CTU-UHB was the most frequent biosignal-based dataset and UCI-CTG was the most frequent feature-based data; (iii) The selected studies made use of the system design stages alone or in a mixed-mode.

CONCLUSION

The results indicated that the well-established classification models achieved competitive results compared with the state-of-the-art methods in data-constrained system designs. Moreover, we identified the need for validation studies in the clinical environment.

Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section

Objective

One of the major problems with artificial intelligence (AI) is that it is generally known as a “black box”. Therefore, the present study aimed to construct an emergency cesarean section (CS) prediction system using an AI‐based rule extraction approach as a “white box” to detect the cause for the emergency CS.

Methods

Data were collected from all perinatal records of all delivery outcomes at Osaka Medical College between December 2014 and July 2019. We identified the delivery method for all deliveries after 36 gestational weeks as either (1) vaginal delivery or scheduled CS, or (2) emergency CS. From among these, we selected 52 risk factors to feed into an AI‐based rule extraction algorithm to extract rules to predict an emergency CS.

Results

We identified 1513 singleton deliveries (1285 [84.9%] vaginal deliveries, 228 emergency CS [15.1%]) and extracted 15 rules. We achieved an average accuracy of 81.90% using five‐fold cross‐validation and an area under the receiving operating characteristic curve of 71.46%.

Conclusion

To our knowledge, this is the first study to use interpretable AI‐based rule extraction technology to predict an emergency CS. This system appears to be useful for identifying hidden factors for emergency CS.

This is the first study to construct a prediction system for an emergency cesarean section using an artificial intelligence‐based “white box” rule extraction approach.