305 research outputs found

    Severe asphyxia due to delivery-related malpractice in Sweden 1990–2005

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    Aim The objective of the thesis was to describe the most common causes of substandard care during labour contributing to severe asphyxia or neonatal death, to study risk factors related to asphyxia associated with substandard care and to explore the occurrence of substandard care during labour. Background There are about 100 000 infants born every year in Sweden. Most infants are born healthy after uncomplicated deliveries. However, 20-50 claims for financial compensation are made annually to the Patients Advisory Committee (PA C) on suspicion that substandard care during labour has contributed to severe asphyxia causing cerebral palsy or death. Even if this group of patients is notably small, asphyxia causes life-long impairment and immeasurable suffering to the patients and their families. In addition, the insurance costs are substantial and amount to 25% of all costs related to substandard care in Sweden. With the exception of this group of patients, and claims to the Health Services Disciplinary Board, the frequency of substandard care in relation to childbirth is fairly unknown. Material and methods Inclusion criteria were pregnancies with a gestational length ≥ 33 weeks, a spontaneous or induced start of labour, a normal CTG at admission for labour, and Apgar score < 7 at 5 minutes of age (Papers I-IV). 472 case records of deliveries from 1990-2005, filed at the PAC were scrutinised. In Paper I and II the deliveries and acts of neonatal resuscitation procedures are described. In Paper III, maternal characteristics, factors related to care and infant characteristics for patients receiving lifelong financial compensation from PAC are compared with all infants with full Apgar score at 5 minutes of age born after a vaginal start during the same time period in Sweden (n=1.141 059). In Paper IV deliveries and risk factors from 313 infants with Apgar score < 7at 5 minutes of age, born in the Stockholm County are compared with 313 infants with full Apgar score at five minutes of age, matched for year of birth. Results One-hundred and seventy-seven infants were considered to have been severely asphyxiated due to substandard care during labour (Paper I-III). The most common occurrences of malpractice in conjunction with labour were neglecting to supervise fetal well-being (98%), neglecting signs of fetal asphyxia (71%), including incautious use of oxytocin (71%) and choosing a non-optimal mode of delivery (52%) (Paper I). Resuscitation of the 177 severely asphyxiated infants was unsatisfactory in 47%. The most important flaw was the defective compliance with the guidelines concerning ventilation and prompt paging for skilled personnel in cases of imminent asphyxia (Paper II). Risk factors associated with asphyxia included maternal age ≥ 30 years, short maternal stature (< 159 cm), previous caesarean delivery, insulin-dependent diabetes, induced deliveries and night deliveries, where the increases in risk were doubled to a four-fold. In addition, dystocia of labour was associated with a five-fold increase in risk, which was further increased if epidural anaesthesia or opioids were used. Small- and large-for-gestational age infants, post-term (> 42 weeks) births, twins and breech deliveries had a three to eight-fold increase in risk of asphyxia when there was substandard care during labour (Paper III). Two thirds of infants born in the Stockholm region 2004-2006, with Apgar score < 7 at 5 minutes but also one third of the healthy controls were subjected to some kind of substandard care during labour (Paper IV). The main causes of substandard care during labour were related to misinterpretation of CTG, not acting timely on abnormal CTG, and incautious use of oxytocin. The risk of asphyxia increased with duration of abnormal CTG and was increased fifteen-fold when this was abnormal for ≥ 90 minutes. Oxytocin was provided without sign of inertia in 20% of cases and controls and the risk of asphyxia was increased more than fivefold in cases of tachysystole. Infants born after a spontaneous vaginal delivery with abnormal CTG for more than 45 minutes had a more than sevenfold risk of low Apgar score. In instrumental deliveries that were considered complex, there was a more than seventeen-fold risk of an Apgar score < 7 at 5 minutes of age. Assuming that substandard care is causative for low Apgar score, we estimate that 42% of the cases could be prevented by avoiding substandard care (Paper IV). Conclusion It is possible to improve patient safety during labour by applying educational efforts on fetal surveillance and increasing awareness of risk factors associated with asphyxia. The main causes of substandard care during labour are related to misinterpretation of CTG, not acting timely on abnormal CTG, misinterpretation of guidelines and misuse of oxytocin. Low Apgar score at 5 minutes of age can substantially, be prevented by avoiding substandard care

    Labour ward incidents and potential claims - Lessons learned from research

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    This paper provides an insight into the underlying factors involved in potential cerebral palsy and/or shoulder dystocia claims. The research was undertaken to identify the root causes of 37 cases of birth asphyxia in term infants severe enough to warrant admission to neonatal care units in the north-west of England between 2001 and 2002. All available staff (n ¼ 93) providing care during critical periods were interviewed by the author using the cognitive interviewing technique. These included 81 midwives, two consultant obstetricians, eight registrars and two senior house officers. An expert panel consisting of consultant obstetricians, midwives, a consultant neonatologist and the researcher applied the Bolam test to identify instances where care had been substandard and injury caused as a result. Although the cases were often complex, covering more than one shift and over more than one stage of labour, the most dangerous time appeared to be during the night shift (19 cases, 51%), followed by the evening shift (13 cases, 35%) and then the day shift (five cases, 14%). The main problems include: failure to respond appropriately to signs of fetal hypoxia (26 cases, 70%); undiagnosed obstruction (22 cases, 59%), which was broken down into failure to identify cephalopelvic disproportion (13 cases, 35%); and shoulder dystocia (nine cases, 24%). Delayed resuscitation of the infant occurred in 26 cases (80%), and in 18 cases (49%) there was excessive and inappropriate use of Syntocinon. All cases involved human error, either through a delay or failure to take action, or taking inappropriate action. However, these were all underpinned and perpetuated by system and cultural errors present in the labour wards, such as allowing unsupported and inexperienced personnel to work in a position for which they lacked the necessary skill and experience. This was perpetuated by the customary practice of using unsupervised junior medical staff in a first on-call position for complications, and also of failing to sustain safe midwifery staffing levels. This in turn prevented support for more inexperienced staff. Consequently, when inexperienced midwives and obstetricians were left unsupervised in charge of complicated cases, it created accidents waiting to happen. When unsupervised and inexperienced paediatricians attended the birth of an asphyxiated infant, the child’s condition deteriorated further when they were unable to resuscitate it. If such system and cultural errors as these are not rectified, the current high rate of damaged babies is likely to continue

    Computational intelligence methods for predicting fetal outcomes from heart rate patterns

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    In this thesis, methods for evaluating the fetal state are compared to make predictions based on Cardiotocography (CTG) data. The first part of this research is the development of an algorithm to extract features from the CTG data. A feature extraction algorithm is presented that is capable of extracting most of the features in the SISPORTO software package as well as late and variable decelerations. The resulting features are used for classification based on both U.S. National Institutes of Health (NIH) categories and umbilical cord pH data. The first experiment uses the features to classify the results into three different categories suggested by the NIH and commonly being used in practice in hospitals across the United States. In addition, the algorithms developed here were used to predict cord pH levels, the actual condition that the three NIH categories are used to attempt to measure. This thesis demonstrates the importance of machine learning in Maternal and Fetal Medicine. It provides assistance for the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the Pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology to achieve a more accurate prediction of fetal outcomes using Fetal Heartrate and Uterine Activity with accuracies of greater than 99.5% for predicting categories and greater than 70% for fetal acidosis based on pH values --Abstract, page iii

    Predicting complex system behavior using hybrid modeling and computational intelligence

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    “Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors”--Abstract, page iv

    An evaluation of electronic fetal monitoring with clinical validation of ST waveform analysis during labour

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    Dissatisfaction with the electronic recording of fetal heart rate and uterine contractions (the cardiotocogram or CTG) has resulted in a search for new techniques of monitoring the fetus during labour. It is important that each method has a sound physiological and pathophysiological basis, that a model for the interpretation of changes is elucidated and that each method is thoroughly evaluated before introduction into clinical practice. Analysis of the ST waveform of the fetal electrocardiogram (FECG) is the most advanced of the new techniques under investigation. Experimental studies have shown that elevation of the ST waveform occurs with a switch to myocardial anaerobic metabolism and a negative waveform occurs during direct myocardial ischaemia. Human observational studies have suggested that a combination of ST waveform and CTG analysis may improve the specificity of intrapartum monitoring and reduce unnecessary intervention. A high quality FECG signal is necessary for waveform analysis. The FECG can be recorded from a scalp electrode (FSE) during labour. The suitability of 5 commonly available FSEs for ECG waveform analysis was compared. Single spiral FSEs had the most favourable physical and electrical properties and produced the best quality signals in a randomised clinical trial of 50 fetuses in labour. Intervention rates and neonatal outcome in labours monitored with CTG alone were compared with those monitored with the combination of ST waveform analysis plus CTG (ST+CTG) in a randomised clinical trial of 2434 high risk labours in a large district general hospital over an 18 month period. There was a 46% reduction in operative intervention for fetal distress in the ST+CTG group (p<0.001, OR 1.96 [1.42-2.71]). There was a trend to less neonatal metabolic acidemia (p = 0.09, OR 2.63 [0.93-7.39]) and fewer low five minute Apgar scores (p = 0.12, OR 1.62 [0.92-2.85]) in the ST+CTG arm. All recordings were reviewed retrospectively, blind to outcome and the CTG classified as normal, intermediate or abnormal according to the trial protocol. There was no significant difference in the proportion of recordings in each category between the trial arms. Operative intervention in the ST+CTG arm was significantly reduced in recordings classified as normal and intermediate by the review (12/1043 ST+CTG arm versus 48/1066 CTG arm, p <0.001). Three patterns of ST+CTG change were identified. 1. Normal CTG, persistent stable ST waveform elevation. These fetuses had good outcome and a significantly higher mean pH (7.29) and lower base deficit (1.1 rnmol/1) at delivery. The raised ST waveform may reflect sympathoadrenal stimulation from the general arousal of labour or a response to mild but compensated hypoxaemia and is in keeping with experimental data. 2. CTG abnormal, progressive elevation in ST waveform. All cases occurred towards the end of second stage. These fetuses had a significantly lower mean pH (7.05) and higher base deficit (7.6 mmol/1) than all other groups. This combination identified fetuses who were developing a metabolic acidosis as a result of significant hypoxia. 3. Abnormal CTG and a negative ST waveform. All cases with persistently negative waveforms were depressed at birth, required resuscitation and had low arterial pHs (where available). This high risk group probably had depleted myocardial glycogen reserves and suffered direct myocardial hypoxia, as seen in animal studies. These findings indicate that ST waveform analysis can discriminate CTG change during labour, the combination can result in a reduction in unnecessary intervention and has the potential to more accurately identify fetuses at risk of neonatal morbidity. The term 'monitoring' implies a degree of automatic surveillance but this is not the case as CTG and ST+CTG records are subjectively interpreted, frequently by junior, inexperienced staff. The retrospective review of cases in the trial revealed significant errors in the use of fetal blood sampling and the interpretation of both CTG and ST+CTG recordings during the study. The feasibility of representing expert clinical knowledge in a decision support tool to provide consistent, accurate interpretation of the CTG was demonstrated in two clinical studies. The full potential of ST+CTG analysis may only be achieved with some degree of automatic data processing and interpretation. The randomised trial also demonstrated the lack of appropriate measures of neonatal outcome with which to judge the effectiveness of fetal monitoring. Analysis of cord artery and vein blood gas status at delivery can provide useful information about fetal oxygenation prior to delivery but currently the information is poorly used, if at all. Use of erroneous data, inappropriate measures of 'acidemia', failure to distinguish between respiratory and metabolic components and unphysiological expectations about relationships to other measures of neonatal outcome were some of the problems highlighted. The use of generic terminology such as 'birth asphyxia' or 'acidosis' which have varying definitions has caused much confusion and should be avoided. There is unlikely to be one 'gold standard' measure of neonatal condition at delivery

    Agreement and accuracy using the FIGO, ACOG and NICE cardiotocography interpretation guidelines.

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    INTRODUCTION: One of the limitations reported with cardiotocography (CTG) is the modest interobserver agreement observed in tracing interpretation. This study compared agreement, reliability and accuracy of CTG interpretation using the FIGO, ACOG and NICE guidelines. MATERIAL AND METHODS: A total of 151 tracings was evaluated by 27 clinicians from three centers where FIGO, ACOG and NICE guidelines were routinely used. Interobserver agreement was evaluated using the proportions of agreement (PA) and reliability with the kappa (k) statistic. The accuracy of tracings classified as "pathological/category III" was assessed for prediction of newborn acidemia. For all measures, 95% confidence intervals (95%CI) were calculated RESULTS: CTG classifications were more distributed with FIGO (9%, 52%, 39%) and NICE (30%, 33%, 37%) than with ACOG (13%, 81%, 6%). The category with the highest agreement was ACOG category II (PA=0.73 95%CI 0.70-76), and the ones with the lowest agreement were ACOG categories I and III. Reliability was significantly higher with FIGO (k=0.37, 95%CI 0.31-0.43), and NICE (k=0.33, 95%CI 0.28-0.39) than with ACOG (k= 0.15, 95%CI 0.10-0.21), however all represent only slight/fair reliability. FIGO and NICE showed a trend towards higher sensitivities in prediction of newborn acidemia (89% and 97% respectively) than ACOG (32%,), but the latter achieved a significantly higher specificity (95%) CONCLUSIONS: With ACOG guidelines there is high agreement in category II, low reliability, low sensitivity and high specificity in prediction of acidemia. With FIGO and NICE guidelines there is higher reliability, a trend towards higher sensitivity, and lower specificity in prediction of acidemia. This article is protected by copyright. All rights reserved

    Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data

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    Background: A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor.Aim: The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses.Methods: CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified.Results: Determined by obstetricians\u27 interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (\u3e96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (\u3e92%).Conclusion: The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management

    Cardiotocography Signal Abnormality Detection based on Deep Unsupervised Models

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    Cardiotocography (CTG) is a key element when it comes to monitoring fetal well-being. Obstetricians use it to observe the fetal heart rate (FHR) and the uterine contraction (UC). The goal is to determine how the fetus reacts to the contraction and whether it is receiving adequate oxygen. If a problem occurs, the physician can then respond with an intervention. Unfortunately, the interpretation of CTGs is highly subjective and there is a low inter- and intra-observer agreement rate among practitioners. This can lead to unnecessary medical intervention that represents a risk for both the mother and the fetus. Recently, computer-assisted diagnosis techniques, especially based on artificial intelligence models (mostly supervised), have been proposed in the literature. But, many of these models lack generalization to unseen/test data samples due to overfitting. Moreover, the unsupervised models were applied to a very small portion of the CTG samples where the normal and abnormal classes are highly separable. In this work, deep unsupervised learning approaches, trained in a semi-supervised manner, are proposed for anomaly detection in CTG signals. The GANomaly framework, modified to capture the underlying distribution of data samples, is used as our main model and is applied to the CTU-UHB dataset. Unlike the recent studies, all the CTG data samples, without any specific preferences, are used in our work. The experimental results show that our modified GANomaly model outperforms state-of-the-arts. This study admit the superiority of the deep unsupervised models over the supervised ones in CTG abnormality detection
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