Interprofessional Consensus Regarding Design Requirements for Liquid-Based Perinatal Life Support (PLS) Technology

Liquid-based perinatal life support (PLS) technology will probably be applied in a first-in-human study within the next decade. Research and development of PLS technology should not only address technical issues, but also consider socio-ethical and legal aspects, its application area, and the corresponding design implications. This paper represents the consensus opinion of a group of healthcare professionals, designers, ethicists, researchers and patient representatives, who have expertise in tertiary obstetric and neonatal care, bio-ethics, experimental perinatal animal models for physiologic research, biomedical modeling, monitoring, and design. The aim of this paper is to provide a framework for research and development of PLS technology. These requirements are considering the possible respective user perspectives, with the aim to co-create a PLS system that facilitates physiological growth and development for extremely preterm born infants

Clinical use of electrohysterography during term \labor: a systematic review on diagnostic value, advantages, and limitations

Importance Real-time electrohysterography (EHG)-based technologies have recently become available for uterine monitoring during term labor. Therefore, obstetricians need to be familiar with the diagnostic value, advantages, and limitations of using EHG. Objective The aims of this study were to determine the diagnostic value of EHG in comparison to (1) the intrauterine pressure catheter (IUPC), (2) the external tocodynamometer (TOCO), and (3) in case of maternal obesity; (4) to evaluate EHG from users' and patients' perspectives; and (5) to assess whether EHG can predict labor outcome. Evidence Acquisition A systematic review was performed in the MEDLINE, EMBASE, and Cochrane library in October 2017 resulting in 209 eligible records, of which 20 were included. Results A high sensitivity for contraction detection was achieved by EHG (range, 86.0%-98.0%), which was significantly better than TOCO (range, 46.0%-73.6%). Electrohysterography also enhanced external monitoring in case of maternal obesity. The contraction frequency detected by EHG was on average 0.3 to 0.9 per 10 minutes higher compared with IUPC, which resulted in a positive predictive value of 78.7% to 92.0%. When comparing EHG tocograms with IUPC traces, an underestimation of the amplitude existed despite that patient-specific EHG amplitudes have been mitigated by amplitude normalization. Obstetricians evaluated EHG tocograms as better interpretable and more adequate than TOCO. Finally, potential EHG parameters that could predict a vaginal delivery were a predominant fundal direction and a lower peak frequency. Conclusions and Relevance Electrohysterography enhances external uterine monitoring of both nonobese and obese women. Obstetricians consider EHG as better interpretable; however, they need to be aware of the higher contraction frequency detected by EHG and of the amplitude mismatch with intrauterine pressure measurements. Target Audience Obstetricians and gynecologists, family physicians. Learning Objectives After completing this activity, the learner should be better able to interpret the physiology of uterine contractions, relate the diagnostic value of electrohysterography (EHG) traces to intrauterine pressure catheter and tocodynamometer, examine how the performance of the external uterine monitoring techniques is affected by maternal obesity, distinguish the advantages and limitations of EHG-based monitoring from users' and patients' perspectives, and propose uses for EHG uterine contraction monitoring and other (future) applications of EHG

A review study of fetal circulatory models to develop a digital twin of a fetus in a perinatal life support system

Background: Preterm birth is the main cause of neonatal deaths with increasing mortality and morbidity rates with decreasing GA at time of birth. Currently, premature infants are treated in neonatal intensive care units to support further development. However, the organs of, especially, extremely premature infants (born before 28 weeks of GA) are not mature enough to function optimally outside the womb. This is seen as the main cause of the high morbidity and mortality rates in this group. A liquid-filled incubator, a so-called PLS system, could potentially improve these numbers for extremely premature infants, since this system is designed to mimic the environment of the natural womb. To support the development and implementation of such a complex system and to interpret vital signals of the fetus during a PLS system operation, a digital twin is proposed. This mathematical model is connected with a manikin representing the digital and physical twin of the real-life PLS system. Before developing a digital twin of a fetus in a PLS system, its functional and technical requirements are defined and existing mathematical models are evaluated. Method and results: This review summarizes existing 0D and 1D fetal circulatory models that potentially could be (partly) adopted for integration in a digital twin of a fetus in a PLS system based on predefined requirements. The 0D models typically describe hemodynamics and/or oxygen transport during specific events, such as the transition from fetus to neonate. Furthermore, these models can be used to find hemodynamic differences between healthy and pathological physiological states. Rather than giving a global description of an entire cardiovascular system, some studies focus on specific organs or vessels. In order to analyze pressure and flow wave profiles in the cardiovascular system, transmission line or 1D models are used. As for now, these models do not include oxygen transport. Conclusion: This study shows that none of the models identified in literature meet all the requirements relevant for a digital twin of a fetus in a PLS system. Nevertheless, it does show the potential to develop this digital twin by integrating (parts) of models into a single model

A review study of fetal circulatory models to develop a digital twin of a fetus in a perinatal life support system

Background: Preterm birth is the main cause of neonatal deaths with increasing mortality and morbidity rates with decreasing GA at time of birth. Currently, premature infants are treated in neonatal intensive care units to support further development. However, the organs of, especially, extremely premature infants (born before 28 weeks of GA) are not mature enough to function optimally outside the womb. This is seen as the main cause of the high morbidity and mortality rates in this group. A liquid-filled incubator, a so-called PLS system, could potentially improve these numbers for extremely premature infants, since this system is designed to mimic the environment of the natural womb. To support the development and implementation of such a complex system and to interpret vital signals of the fetus during a PLS system operation, a digital twin is proposed. This mathematical model is connected with a manikin representing the digital and physical twin of the real-life PLS system. Before developing a digital twin of a fetus in a PLS system, its functional and technical requirements are defined and existing mathematical models are evaluated. Method and results: This review summarizes existing 0D and 1D fetal circulatory models that potentially could be (partly) adopted for integration in a digital twin of a fetus in a PLS system based on predefined requirements. The 0D models typically describe hemodynamics and/or oxygen transport during specific events, such as the transition from fetus to neonate. Furthermore, these models can be used to find hemodynamic differences between healthy and pathological physiological states. Rather than giving a global description of an entire cardiovascular system, some studies focus on specific organs or vessels. In order to analyze pressure and flow wave profiles in the cardiovascular system, transmission line or 1D models are used. As for now, these models do not include oxygen transport. Conclusion: This study shows that none of the models identified in literature meet all the requirements relevant for a digital twin of a fetus in a PLS system. Nevertheless, it does show the potential to develop this digital twin by integrating (parts) of models into a single model

The use of a stronger instructional design by implementing repetitive practice in simulation-based obstetric team training: trainees' satisfaction

Objective: This study compares satisfaction levels from multiprofessional obstetric care teams about simulation-based obstetric team training courses with and without the instructional design feature repetitive practice. Methods: The present study is part of a multicentre cluster-randomised controlled trial (TOSTI trial) that investigated the effectiveness of a 1 day, multiprofessional, simulation-based obstetric team training. The initial training group received a training which was designed based on best practice. After 1 year, the control group received a training course in which the instructional design was changed by providing repetitive practice. All participants were asked to fill in a 29-item evaluation form with seven questions about baseline characteristics and 22 questions about training features. The questions about training features could be rated on a scale of 1 to 5. Finally, all participants were asked to rate the total training day on a scale of 1-10. Results: The best practice group consisted of 471 trainees and the repetitive practice group of 549, including gynaecologists, residents, midwives and nurses. The best practice group rated the total training day significantly higher than the repetitive practice group (mean 8.8, SD 0.6 and mean 8.7, SD 0.6; p<0.003, Cohen's d=0.19). Several training features were also scored higher in the best practice group. Conclusion: This study showed that obstetric healthcare professionals rated a simulation-based obstetric team training course, with and without repetition of scenarios, both high. The training without the repetitive elements gained higher scores for the total training dayand several, and several training features were scored higher. The difference between the mean scores and the effect sizes for the training features were small. This implies that repetitive practice can be integrated in simulation-based team training to optimise learning effects, with small effects on trainees satisfaction

Prediction of pre-eclampsia by maternal characteristics : A case-controlled validation study of a Bayesian network model for risk identification of pre-eclampsia

Brief Introduction: Pre-eclampsia (PE) is worldwide a leading and rising cause of maternal and perinatal morbidity and mortality. As PE remains a serious and poorly understood complication of pregnancy, it is necessary to recognize the disease before it threatens the survival of mother and fetus. A validated tool that allows real-time maternal risk stratification is needed to guide care. A recent advanced model for pre-eclampsia presented in Velikova et al. 2014 provides that potential. In contrast to the previous study where the testing with this model was done with data for high-risk pregnancies, in this study we aimed at evaluating the capability of the BN model for pre-eclampsia, by looking at the predictions for normal and pre-eclamptic pregnancies. Materials &amp; Methods: The model is based on a Bayesian network methodology, which has been successfully applied for clinical problems. A Bayesian network (BN) is a statistical model that represents a set of variables (e.g. risk factors, diseases and symptoms) and their dependencies by means of a graph and probability distributions. The advantage of a Bayesian network is that it can be used to make personalized predictions, for example for the development or presence of a disease, by entering patient-specific data. We validated the BN model for PE (PE model) in a retrospective case-control study. 10 women diagnosed with PE admitted to the obstetric high care ward of a tertiary care center were enrolled. Their characteristics were matched with 10 pregnant women without any illness. We collected pregnancy data that was relevant for the model, including: (i) risk factors: age; BMI; smoking; parity; twin pregnancy; family history of PE; previous history of PE; preexisting vascular disease; preexisting renal disease; anti-phospholipid syndrome; diabetes mellitus, (ii) medication and measurements from 10 standard check-ups during the pregnancy: blood pressure, protein-to-creatinine ratio, serum creatinine and hemoglobin and medication and (iii) the outcome variable: whether or not preeclampsia is present. PE risk estimation from the model for each patient was compared to PE development. Model performance was assessed by means of the area under the receiver operating characteristic (ROC) curve (AUC). Clinical Cases or Summary Results: Throughout pregnancy, the PE model predicted a high absolute risk for PE in 9 out of 10 PE patients versus 2 out of 10 non-preeclamptic women. This is shown in Figure 1. Each pregnancy week shows the number of patients that has not delivered yet, the color indicating whether they are ultimately diagnosed with PE (red) or not (green). The predicted risk by the model is indicated with a percentage (y-axis) for each patient at each gestational week (x-axis). ROC curves and PE-risk cut-offs were calculated for different gestational weeks. This resulted in AUC score at 24 weeks of 0.895, at 28 weeks of 1.000, at 32 weeks of 0.986, at 36 weeks 1.000, at 38 weeks of 0.375, at 40 weeks of 1.000. Concluding, we find high AUC scores, except for the prediction at 38 weeks of gestation, due to missing data. Therefore no cut-off value was calculated for week 38. Sensitivity, specificity, the positive predictive value (PPV) and negative predictive value (NPV) of the PE model are as well calculated per week of gestation, except for week 38. The sensitivity of the PE model is 100% at each pregnancy week. We find a specificity of 83%, 100%, 91%, 100% and 100% at 24, 28, 32, 36 and 40 weeks, respectively. Thus, 8-17% of women without PE will be screened as having an increased risk. The PPV is calculated as 33%, 100%, 83%, 100% and 100% at 24, 28, 32, 36 and 40 weeks, respectively. The NPV is calculated as 100% at each pregnancy week. Conclusions: When data is available in early pregnancy, the PE model is able to distinguish between PE and non-PE pregnant women and able to predict a higher risk for the diagnosed patients. In particular, at gestational week 12 the chance for PE was twice or higher for PE patients than for 8 of the non-PE pregnant women, and for weeks 16-24 this chance for PE patients was up to eight times higher for the PE patients. This is a particularly important result given the aim of a timely identification of women at risk, which is to facilitate much targeted monitoring. Despite the fact not all data was available for all pregnancy checkups, the PE model was still able to compute the individual, absolute risk for pre-eclampsia. Although for some patients PE was only predicted late in pregnancy, this was for all patients before or at latest at the same moment of clinical diagnosis. However, we expect that prediction will improve when all measurements are available from the pregnancy checkups. From the model it follows that a dynamic cut-off is needed that increases with pregnancy duration. Current results are promising. We propose to perform an RCT with a larger number of patients to establish this cut-off curve with more accuracy and to validate the PE model prospectively. Once validated, the model can assist in early PE diagnosis and thus allow early treatment of PE. The PE model can be integrated in e-health applications to allow real-time monitoring of pregnant women anywhere. By this way we can personalize the healthcare during pregnancy. (Figure presented

Table_1_A review study of fetal circulatory models to develop a digital twin of a fetus in a perinatal life support system.pdf

BackgroundPreterm birth is the main cause of neonatal deaths with increasing mortality and morbidity rates with decreasing GA at time of birth. Currently, premature infants are treated in neonatal intensive care units to support further development. However, the organs of, especially, extremely premature infants (born before 28 weeks of GA) are not mature enough to function optimally outside the womb. This is seen as the main cause of the high morbidity and mortality rates in this group. A liquid-filled incubator, a so-called PLS system, could potentially improve these numbers for extremely premature infants, since this system is designed to mimic the environment of the natural womb. To support the development and implementation of such a complex system and to interpret vital signals of the fetus during a PLS system operation, a digital twin is proposed. This mathematical model is connected with a manikin representing the digital and physical twin of the real-life PLS system. Before developing a digital twin of a fetus in a PLS system, its functional and technical requirements are defined and existing mathematical models are evaluated.Method and resultsThis review summarizes existing 0D and 1D fetal circulatory models that potentially could be (partly) adopted for integration in a digital twin of a fetus in a PLS system based on predefined requirements. The 0D models typically describe hemodynamics and/or oxygen transport during specific events, such as the transition from fetus to neonate. Furthermore, these models can be used to find hemodynamic differences between healthy and pathological physiological states. Rather than giving a global description of an entire cardiovascular system, some studies focus on specific organs or vessels. In order to analyze pressure and flow wave profiles in the cardiovascular system, transmission line or 1D models are used. As for now, these models do not include oxygen transport.ConclusionThis study shows that none of the models identified in literature meet all the requirements relevant for a digital twin of a fetus in a PLS system. Nevertheless, it does show the potential to develop this digital twin by integrating (parts) of models into a single model.</p

Visualization of contractions:Evaluation of a new experience design concept to enhance the childbirth experience

BACKGROUND: The purpose of this study was to develop and evaluate an innovative design proposition intended to help enhance the childbirth experience. The innovation consists of a smartphone application for birth preparation during pregnancy with information and coaching, in addition to a wall projection at the labor ward that visualizes the progress of labor based on uterine monitoring data.METHODS: We conducted a randomized controlled clinical pilot study. Singleton pregnant people pursuing a vaginal birth were recruited between 28 and 32 weeks of gestation and allocated to the intervention group (mobile application during the third trimester and wall projection at the labor ward) or to care as usual. Childbirth expectations and experiences were measured with validated questionnaires, which were completed at 32 and 36 weeks of gestation, immediately after birth, and at 4 weeks postpartum. Quantitative outcomes were analyzed and feedback about the proposition was evaluated using qualitative methods.RESULTS: The qualitative feedback from patients was largely positive; however, we could not detect any significant differences between the intervention and control groups about fear of childbirth and other outcome measures.CONCLUSION: In this pilot study, we evaluated a new experience design proposition for pregnancy and childbirth. This study generated data that will help to further improve and evaluate similar innovations in the future. This application may facilitate participatory care, promoting active involvement of parents in the healthcare processes of pregnancy and childbirth.</p