Antisense and Functional Nucleic Acids in Rational Drug Development.

This review is focused on antisense and functional nucleic acid used for completely rational drug design and drug target assessment, aiming to reduce the time and money spent and increase the successful rate of drug development. Nucleic acids have unique properties that play two essential roles in drug development as drug targets and as drugs. Drug targets can be messenger, ribosomal, non-coding RNAs, ribozymes, riboswitches, and other RNAs. Furthermore, various antisense and functional nucleic acids can be valuable tools in drug discovery. Many mechanisms for RNA-based control of gene expression in both pro-and-eukaryotes and engineering approaches open new avenues for drug discovery with a critical role. This review discusses the design principles, applications, and prospects of antisense and functional nucleic acids in drug delivery and design. Such nucleic acids include antisense oligonucleotides, synthetic ribozymes, and siRNAs, which can be employed for rational antibacterial drug development that can be very efficient. An important feature of antisense and functional nucleic acids is the possibility of using rational design methods for drug development. This review aims to popularize these novel approaches to benefit the drug industry and patients.

AI-driven decision support systems and epistemic reliance: a qualitative study on obstetricians' and midwives' perspectives on integrating AI-driven CTG into clinical decision making.

BACKGROUND: Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. METHODS: This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. RESULTS: There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. CONCLUSIONS: Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice.

Prediction of Fetal Blood Pressure during Labour with Deep Learning Techniques.

Our objective is to develop a model for the prediction of minimum fetal blood pressure (FBP) during fetal heart rate (FHR) decelerations. Experimental data from umbilical occlusions in near-term fetal sheep (2698 occlusions from 57 near-term lambs) were used to train a convolutional neural network. This model was then used to estimate FBP for decelerations extracted from the final 90 min of 53,445 human FHR signals collected using cardiotocography. Minimum sheep FBP was predicted with a mean absolute error of 6.7 mmHg (25th, 50th, 75th percentiles of 2.3, 5.2, 9.7 mmHg), mean absolute percentage errors of 17.3% (5.5%, 12.5%, 23.9%) and a coefficient of determination R2=0.36. While the model was unable to clearly predict severe compromise at birth in humans, there is positive evidence that such a model could predict human FBP with further development. The neural network is capable of predicting FBP for many of the sheep decelerations accurately but performed far from satisfactory at identifying FHR segments that correspond to the highest or lowest minimum FBP. These results indicate that with further work and a larger, more variable training dataset, the model could achieve higher accuracy.

Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care.

BACKGROUND: Despite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness. METHODS: Seventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI. RESULTS: Three themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI. CONCLUSIONS: The ethical values that underscore birth parents and mothers' perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data.

Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0-10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors.

Predelivery placenta-associated biomarkers and computerized intrapartum fetal heart rate patterns.

Background: Increasing syncytiotrophoblast stress in term and postdate placentas is reflected by increasing antiangiogenic dysregulation in the maternal circulation, with low "proangiogenic" placental growth factor concentrations and increased "antiangiogenic" soluble fms-like tyrosine kinase-1 concentrations. Imbalances in these placenta-associated proteins are associated with intrapartum fetal compromise and adverse pregnancy and delivery outcome. Cardiotocography is widely used to assess fetal well-being during labor, but it is insufficient on its own for predicting adverse neonatal outcome. Development of improved surveillance tools to detect intrapartum fetal stress are needed to prevent neonatal adverse outcome. Objective: This study aimed to assess whether predelivery circulating maternal angiogenic protein concentrations are associated with intrapartum computerized fetal heart rate patterns, as calculated by the Oxford System for computerized intrapartum monitoring (OxSys) 1.7 prototype. We hypothesized that in pregnancies with low "proangiogenic" placental growth factor levels, increased "antiangiogenic" soluble fms-like tyrosine kinase-1 levels, and increased soluble fms-like tyrosine kinase-1-placental growth factor ratio, the OxSys 1.7 prototype will generate more automated alerts, indicating fetal compromise. Our secondary objective was to investigate the relationship between maternal circulating placenta-associated biomarkers and rates of automated alerts in pregnancies with and without adverse neonatal outcome. Study Design: This was an observational prospective cohort study conducted at a single tertiary center from September 2016 to March 2020. Of 1107 singleton pregnancies (gestational week ≥37+0), 956 had available prelabor and predelivery placental growth factor and soluble fms-like tyrosine kinase-1 concentrations and intrapartum cardiotocography recordings. All neonatal and delivery outcomes were externally reviewed and categorized into 2 groups-the "complicated" group (n=32) and the "uncomplicated" group (n=924)-according to predefined adverse neonatal outcome. Eight different cardiotocography features were calculated by OxSys 1.7: baseline at start of cardiotocography, baseline at end of cardiotocography, short-term variation at start, short-term variation at end, nonreactive initial trace, and throughout the entire cardiotocography, maximum decelerative capacity, total number of prolonged decelerations, and OxSys 1.7 alert. OxSys 1.7 triggered an alert if the initial trace was nonreactive or if decelerative capacity and/or the number of prolonged decelerations exceeded a predefined threshold. Included women and attending clinicians were blinded to both biomarker and OxSys 1.7 results. Results: Mean maternal placental growth factor concentration was lower in the group with OxSys 1.7 alert compared with the group without the alert (151 vs 169 pg/mL; P=.04). There was a weak negative correlation between predelivery high soluble fms-like tyrosine kinase-1 and low short-term variation start (r s=-0.068; 95% confidence interval, -0.131 to -0.004; P=.036), predelivery high soluble fms-like tyrosine kinase-1 and low short-term variation end (r s=-0.068; 95% confidence interval, -0.131 to -0.005; P=.036), and high soluble fms-like tyrosine kinase-1-placental growth factor ratio and low short-term variation end (r s=-0.071; 95% confidence interval, -0.134 to -0.008; P=.027). The rate of decelerative capacity alerts increased more rapidly as placental growth factor decreased in the "complicated" compared with the "uncomplicated" group (0% to 17% vs 4% to 8%). Conclusion: More automated alerts indicative of fetal distress were generated by OxSys 1.7 in pregnancies with low maternal predelivery placental growth factor level, in line with likely increasing placental stress toward the end of the pregnancy. An antiangiogenic predelivery profile (lower placental growth factor) increased the rates of alerts more rapidly in pregnancies with adverse neonatal outcome compared with those without. We suggest that future studies developing and testing prediction tools for intrapartum fetal compromise include predelivery maternal placental growth factor measurements.

Versatile tools of synthetic biology applied to drug discovery and production.

Although synthetic biology is an emerging research field, which has come to prominence within the last decade, it already has many practical applications. Its applications cover the areas of pharmaceutical biotechnology and drug discovery, bringing essential novel methods and strategies such as metabolic engineering, reprogramming the cell fate, drug production in genetically modified organisms, molecular glues, functional nucleic acids and genome editing. This review discusses the main avenues for synthetic biology application in pharmaceutical biotechnology. The authors believe that synthetic biology will reshape drug development and drug production to a similar extent as the advances in organic chemical synthesis in the 20th century. Therefore, synthetic biology already plays an essential role in pharmaceutical, biotechnology, which is the main focus of this review.

Cardiotocography and Clinical Risk Factors in Early Term Labor: A Retrospective Cohort Study Using Computerized Analysis With Oxford System.

Objective: The role of cardiotocography (CTG) in fetal risk assessment around the beginning of term labor is controversial. We used routinely collected clinical data in a large tertiary hospital to investigate whether infants with "severe compromise" at birth exhibited fetal heart rate abnormalities in their first-hour CTGs and/or other clinical risks, recorded as per routine care. Materials and Methods: Retrospective data from 27,927 parturitions (single UK tertiary site, 2001-2010) were analyzed. Cases were included if the pregnancy was singleton, ≥36 weeks' gestation, cephalic presentation, and if they had routine intrapartum CTG as per clinical care. Cases with congenital abnormalities, planned cesarean section (CS), or CS for reasons other than "presumed fetal compromise" were excluded. We analyzed first-hour intrapartum CTG recordings, using intrapartum Oxford System (OxSys) computer-based algorithms. To reflect the effect of routine clinical care, the data was stratified into three exclusive groups: infants delivered by CS for "presumed fetal compromise" within 2 h of starting the CTG (Emergency CS, n = 113); between 2 and 5 h of starting the CTG (Urgent CS, n = 203); and the rest of deliveries (Others, n = 27,611). First-hour CTG and clinical characteristics were compared between the groups, sub-divided to those with and without severe compromise: a composite outcome of stillbirth, neonatal death, neonatal seizures, encephalopathy, resuscitation followed by ≥48 h in neonatal intensive care unit. Two-sample t-test, X2 test, and Fisher's exact test were used for analysis. Results: Compared to babies without severe compromise, those with compromise had significantly higher proportion of cases with baseline fetal heart rate ≥150 bpm; non-reactive trace; reduced long-term and short-term variability; decelerative capacity; and no accelerations in the first-hour CTG across all groups. Prolonged decelerations(≥3 min) were also more common. Thick meconium and small for gestational age were consistently more common in compromised infants across all groups. There was more often thick meconium, maternal fever ≥38 C, sentinel events, and other clinical risk factors in the Emergency CS and Urgent CS compared to the Others group. Conclusion: A proportion of infants born with severe compromise had significantly different first-hour CTG features and clinical risk factors.

The association between placenta-associated circulating biomarkers and composite adverse delivery outcome of a likely placental cause in healthy post-date pregnancies.

INTRODUCTION: Post-date pregnancies have an increased risk of adverse delivery outcome. Our aim was to explore the association between placenta-associated circulating biomarkers and composite adverse delivery outcome of a likely placental cause in clinically healthy post-date pregnancies. MATERIAL AND METHODS: Women with healthy singleton post-date pregnancies between 40+2 and 42+2  weeks of gestation were recruited to this prospective, observational study conducted at Oslo University Hospital, Norway (NCT03100084). Placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured in the maternal serum samples closest to delivery. The composite adverse delivery outcome included fetal acidemia, low Apgar score (<4 at 1 min or <7 at 5 min), asphyxia, fetal death, assisted ventilation for more than 6 h, meconium aspiration, hypoxic-ischemic encephalopathy, therapeutic hypothermia, operative delivery due to fetal distress, or pathological placental histology findings. Two study-independent senior consultant obstetricians blinded to biomarker results concluded, based on clinical expert opinion, whether the adverse delivery outcomes were most likely associated with placental dysfunction ("likely placental cause") or not. Means were compared using one-way analysis of variance and Bonferroni corrected pairwise comparisons between groups. Receiver operating characteristic (ROC) curves assessed the predictive ability of PlGF, sFlt-1/PlGF ratio, and PlGF <10th centile after adjustment for gestational age at blood sampling. RESULTS: Of 501 pregnancies reviewed for predefined adverse delivery outcomes and for a likely placental cause, 468 were healthy pregnancies and subsequently assigned to either the "uncomplicated" (no adverse outcome, n = 359), "intermediate" (non-placental cause/undetermined, n = 90), or "complicated" (likely placental cause, n = 19) group. There was a significant difference in mean PlGF and sFlt-1/PlGF ratio between the "complicated", "intermediate", and "uncomplicated" groups (108, 185, and 179 pg/mL, p = 0.001; and 48.3, 23.4, and 24.6, p = 0.002, respectively). There was a higher proportion of PlGF concentration <10th centile in the "complicated" group compared with the "intermediate" and "uncomplicated" groups (42.1% vs. 11.1% and 9.5%, p = 0.001). The largest area under the ROC curve for predicting "complicated" outcome was achieved by PlGF concentration and gestational age at blood sampling (0.76; 95% CI 0.65-0.86). CONCLUSIONS: In clinically healthy post-date pregnancies, an antiangiogenic pre-delivery profile (lower PlGF level and higher sFlt-1/PlGF ratio) was associated with composite adverse delivery outcome of a likely placental cause.

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK).

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research.

Understanding Fetal Heart Rate Patterns That May Predict Antenatal and Intrapartum Neural Injury.

Electronic fetal heart rate (FHR) monitoring is widely used to assess fetal well-being throughout pregnancy and labor. Both antenatal and intrapartum FHR monitoring are associated with a high negative predictive value and a very poor positive predictive value. This in part reflects the physiological resilience of the healthy fetus and the remarkable effectiveness of fetal adaptations to even severe challenges. In this way, the majority of "abnormal" FHR patterns in fact reflect a fetus' appropriate adaptive responses to adverse in utero conditions. Understanding the physiology of these adaptations, how they are reflected in the FHR trace and in what conditions they can fail is therefore critical to appreciating both the potential uses and limitations of electronic FHR monitoring.

Deep Learning for Continuous Electronic Fetal Monitoring in Labor.

Continuous electronic fetal monitoring (EFM) is used worldwide to visually assess whether a fetus is exhibiting signs of distress during labor, and may benefit from an emergency operative delivery (e.g. Cesarean section). Previously, computerized EFM assessment that mimics clinical experts showed no benefit in randomized clinical trials. However, as an example of routinely collected `big' data, EFM interpretation should benefit from data-driven computational approaches, such as deep learning, which allow automated evaluation based on large clinical datasets.Here we report our investigation of long short term memory (LSTM) and convolutional neural networks (CNN) in analyzing EFM traces from over 35,000 labors for the prediction of fetal compromise. Of these, 85% are used for training with crossvalidation and the remainder are set aside for testing. The results are compared with Clinical practice (reason for operative deliveryrecorded as fetal distress) and an earlier prototype system for computerized analysis of EFM (OxSys 1.5), developed on the same data. We demonstrate that CNN outperforms LSTM, Clinical practice, and OxSys 1.5 in predicting fetal compromise, with a sensitivity of 42% (30%, 34%, and 36% for the others, respectively), at comparable or lower false positive rates. We also show that increasing the size of the training set improves the sensitivity and stability of CNN's performance on the testing set. When tested on a small open-access external database, CNN moderately improves on the performance of published feature extraction based methods.We conclude that CNN could play an important role in the field of automated EFM analysis, but requires further work.

Doppler-based fetal heart rate analysis markers for the detection of early intrauterine growth restriction.

INTRODUCTION: One indicator for fetal risk of mortality is intrauterine growth restriction (IUGR). Whether markers reflecting the impact of growth restriction on the cardiovascular system, computed from a Doppler-derived heart rate signal, would be suitable for its detection antenatally was studied. MATERIAL AND METHODS: We used a cardiotocography archive of 1163 IUGR cases and 1163 healthy controls, matched for gestation and gender. We assessed the discriminative power of short-term variability and long-term variability of the fetal heart rate, computed over episodes of high and low variation aiming to separate growth-restricted fetuses from controls. Metrics characterizing the sleep state distribution within a trace were also considered for inclusion into an IUGR detection model. RESULTS: Significant differences in the risk markers comparing growth-restricted with healthy fetuses were found. When used in a logistic regression classifier, their performance for identifying IUGR was considerably superior before 34 weeks of gestation. Long-term variability in active sleep was superior to short-term variability [area under the receiver operator curve (AUC) of 72% compared with 71%]. Most predictive was the number of minutes in high variation per hour (AUC of 75%). A multivariate IUGR prediction model improved the AUC to 76%. CONCLUSION: We suggest that heart rate variability markers together with surrogate information on sleep states can contribute to the detection of early-onset IUGR.

Computerized data-driven interpretation of the intrapartum cardiotocogram: a cohort study.

INTRODUCTION: Continuous intrapartum fetal monitoring remains a significant clinical challenge. We propose using cohorts of routinely collected data. We aim to combine non-classical (data-driven) and classical cardiotocography features with clinical features into a system (OxSys), which generates automated alarms for the fetus at risk of intrapartum hypoxia. We hypothesize that OxSys can outperform clinical diagnosis of "fetal distress", when optimized and tested over large retrospective data sets. MATERIAL AND METHODS: We studied a cohort of 22 790 women in labor (≥36 weeks of gestation). Paired umbilical blood analyses were available. Perinatal outcomes were defined by objective criteria (normal; severe, moderate or mild compromise). We used the data retrospectively to develop a prototype of OxSys, by relating its alarms to perinatal outcome, and comparing its performance against standards achieved by bedside diagnosis. RESULTS: OxSys1.5 triggers an alarm if the initial trace is nonreactive or the decelerative capacity (a nonclassical cardiotocography feature), exceeds a threshold, adjusted for preeclampsia and thick meconium. There were 187 newborns with severe, 613 with moderate and 3197 with mild compromise; and 18 793 with normal outcome. OxSys1.5 increased the sensitivity for compromise detection: 43.3% vs. 38.0% for severe (p = 0.3) and 36.1% vs. 31.0% for moderate (p = 0.06); and reduced the false-positive rate (14.4% vs. 16.3%, p < 0.001). CONCLUSIONS: Large historic cohorts can be used to develop and optimize computerized cardiotocography monitoring, combining clinical and cardiotocography risk factors. Our simple prototype has demonstrated the principle of using such data to trigger alarms, and compares well with clinical judgment.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function.

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

Feature selection using genetic algorithms for fetal heart rate analysis.

The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies.

Feature selection for computerized fetal heart rate analysis using genetic algorithms.

During birth, timely and accurate diagnosis is needed in order to prevent severe conditions such as birth asphyxia. The fetal heart rate (FHR) is often monitored during labor to assess the condition of fetal health. Computerized FHR analysis is needed to help clinicians identify abnormal patterns and to intervene when necessary. The objective of this study is to apply Genetic Algorithms (GA) as a feature selection method to select a best feature subset from 64 FHR features and to integrate these best features to recognize unfavorable FHR patterns. The GA was trained on 408 cases and tested on 102 cases (both balanced datasets) using a linear SVM as classifier. 100 best feature subsets were selected according to different splits of data; a committee was formed using these best classifiers to test their classification performance. Fair classification performance was shown on the testing set (Cohen's kappa 0.47, proportion of agreement 73.58%). To our knowledge, this is the first time that a feature selection method has been tested for FHR analysis on a database of this size.

Umbilical cord gases in relation to the neonatal condition: the EveREst plot.

OBJECTIVE: To validate umbilical arterial and venous pH and base deficit (pH(UA), pH(UV), BD(UA), and BD(UV), respectively), as well as venous-arterial pH differences, as measures of perinatal condition at birth (in relation to emergency intervention, resuscitation, facial mask, low Apgar, seizures, other cerebral problems or death); to investigate whether BD(UA) or pH(UA) better measures perinatal risk. STUDY DESIGN: A novel method (Event Rate Estimate (EveREst) plots) was used to analyze cord blood gases and perinatal outcomes of 34,510 term singleton deliveries: cord blood gas values were grouped into exclusive quantiles (containing equal proportions of cases); the quantiles were plotted against per cent rates (event rates) for perinatal outcomes; the event rates for the different blood gases were compared using the χ² test for difference of proportions. RESULTS: Low pH(UA) predicts poor perinatal outcome better than or comparably to high BD(UA): pH(UA) is significantly better than BD(UA) for predicting low Apgar, resuscitation and facial mask (p<0.001, p<0.05, and p<0.001, respectively). For seizures and other cerebral problems, low pH(UA) is better than high BD(UA) but the difference is not statistically significant. For death, both measures perform equally well. Interventions for the specific reason of "fetal distress" increased as pH(UA) decreased but only where electronic fetal monitoring was used. In acidemic neonates (pH(UA) ≤ 7.05, n=1752), significantly more cord prolapses and placenta abruptions were associated with large and small venous-arterial pH difference (pH(VAD)) respectively (p<0.01). CONCLUSION: EveREst plots display clearly the diagnostic value of cord gases. They allow for the easy identification of background rates and increases above background, thresholds of interest, and comparison of the blood gas measures. Overall pH(UA) is the best umbilical blood measure of perinatal outcome. BD(UA) is comparable or inferior. Extremes of pH(VAD) (large or small) identify higher proportions of specific poor outcomes in acidemic neonates.