Cardiotocography pattern: not always a true friend

: Fetal well-being in labor could be assessed trough cardiotocography (CTG). Some doubts have been raised about its unequivocal applicability. Pathological CTG is in most cases connected to fetal acidosis at birth, but other potential causes must be considered in the differential diagnosis. A 31-years-old G2P1 patient referred to our Department of Obstetrics and Gynecology for her scheduled post-term CTG at 40 weeks and 3 days of gestation. The pregnancy was uneventful. CTG was classified as suspicious, and after pharmacological induction, it switched as pathological: an emergency cesarean section was performed. Venous and arterial blood sample taken from the umbilical cord were normal. The next assessments revealed that Atrial Flutter (AFL) occurred at birth. Suspicious CTG is not always associated to neonatal asphyxia. Cardiotocography can help not only in the evaluation of fetal distress, but also in the assessment of global fetal cardiac activity. The presence of a fetal heart defect should be considered when CTG is suspicious

Addressing Interprofessional Competence in Interpretation of Electronic Fetal Monitor Tracings

Interpretation of electronic fetal monitor (EFM) tracings is a critical clinical practice skill nurses and physicians perform during the intrapartum stage of pregnancy. However, if performed inaccurately can potentially jeopardize the well-being of the neonate. This risk is present because if concerning EFM tracings are not interpreted accurately, preventative care interventions to promote the well-being of the unborn child do not occur. The project was initiated by completing a scoping literature review on the methods for training and evaluating EFM interpretation competence, which revealed current EFM interpretation training and evaluation methods are lacking. A concept analysis defined nurse competence in diagnostic healthcare technologies. The analysis included examining surrogate terms, related concepts, attributes, antecedents, and consequences. This dissertation evaluated the feasibility and effectiveness of a Simulation-Based Mastery Learning intervention on clinical interprofessional team members’ EFM interpretation competence and self-efficacy compared to clinical experience alone. In addition, it determined how participants’ characteristics affect baseline EFM interpretation scores. The study was a randomized longitudinal design with participants recruited from a convenience sample of interprofessional healthcare team members from a large research hospital in the southeastern United States. Randomization procedures placed recruited participants into either an intervention or clinical experience alone group, with competence evaluations for both groups occurring at baseline, immediately post-intervention, and three months post-intervention. Once completed, add results and conclusion here

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

"Building back better": seeking an equitable return to sport-for-development in the wake of COVID-19

The COVID-19 pandemic affected sport programming by restricting in-person activities. Concurrently, global outcry for racial justice for Black and racialized communities promoted calls-to-action to assess equitable practices in sport, including Sport for Development (SfD). This study critically examined SfD ‘return to play’ programming to include perspectives from racialized persons’ lived experiences. We present findings based on data collected from MLSE Foundation’s Change the Game (CtG) research, which explored questions of sport inequity to ‘build back better’. Outcomes further SfD discourses challenging (potentially) harmful structures affecting participants, including under reported effects of racialization. The study used a mixed-method methodology with quantitative analysis of survey data, and thematic analysis of personal experience within an anti-racist, anti-oppressive, and decolonial conceptual framework

Measuring academic performance of students in Higher Education using data mining techniques

Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning. These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain events is not always reliable, since the counting process sometimes does not take into consideration other factors and parameters that could affect the extracted knowledge. Student attendance in Higher Education has always been dealt with in a classical way, i.e. educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student s performance. On other hand, the choice of an effective student assessment method is an issue of interest in Higher Education. Various studies (Romero, et al., 2010) have shown that students tend to get higher marks when assessed through coursework-based assessment methods - which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of Educational Data Mining (EDM) studies that pre-processed data through the conventional Data Mining processes including the data preparation process, but they are using transcript data as it stands without looking at examination and coursework results weighting which could affect prediction accuracy. This thesis explores the above problems and tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance s Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. In term of transcripts data, this thesis proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled module s assessment methods; rather they must be investigated thoroughly and considered during EDM s data pre-processing phases. More generally, it is concluded that Educational Data should not be prepared in the same way as exist data due to the differences such as sources of data, applications, and types of errors in them. Therefore, an attribute, Coursework Assessment Ratio (CAR), is proposed to use in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR and SAC on prediction process using data mining classification techniques such as Random Forest, Artificial Neural Networks and k-Nears Neighbors have been investigated. The results were generated by applying the DM techniques on our data set and evaluated by measuring the statistical differences between Classification Accuracy (CA) and Root Mean Square Error (RMSE) of all models. Comprehensive evaluation has been carried out for all results in the experiments to compare all DM techniques results, and it has been found that Random forest (RF) has the highest CA and lowest RMSE. The importance of SAC and CAR in increasing the prediction accuracy has been proved in Chapter 5. Finally, the results have been compared with previous studies that predicted students final marks, based on students marks at earlier stages of their study. The comparisons have taken into consideration similar data and attributes, whilst first excluding average CAR and SAC and secondly by including them, and then measuring the prediction accuracy between both. The aim of this comparison is to ensure that the new preparation process stage will positively affect the final results

When is remission remission? Elucidating the remission state in Ulcerative Colitis: a multimodal exploration

Ulcerative Colitis (UC) is a chronic, inflammatory disease of the colon that has a relapsing-remitting characteristic. The disease management consists of prolonging periods of remission and reducing relapse frequency. There is currently no universally accepted definition of remission in UC. There are different methods of establishing if a patient is in remission, but the lack of definition and knowledge make it difficult to know which method to use. The majority of these methods are poorly described for remission patients representing a substantial knowledge gap. This thesis explored the remission term by investigating the mucosal transcriptional profile in UC remission patients and the utility of histology and transcripts as evaluation modalities. We found that several of the gene transcripts investigated were differently expressed in UC remission patients in comparison to healthy controls. These genes were largely related to pro-inflammatory mechanisms and barrier dysfunction, indicating that despite an apparent normal mucosa in endoscopy, the mucosa differed on a transcriptional level. We then investigated if histology could detect inflammation and consistently classify a patient as in remission using the different scoring indices. The results showed that histology could detect inflammation not apparent on endoscopy, but the histologic scores varied too much to be accurate in a categorical classification. The main source of variance was the histologic raters. Lastly, we investigated if any of the clinical, endoscopic, histologic, or transcriptional variables could predict impending relapse in a survival analysis. The results showed that patients with a low ratio between two transcripts had 5.3 times higher risk of relapse. Histologic factors did not turn out to be predictive of relapse. The conclusion was that several gene transcripts were differently regulated in UC remission patients compared to healthy controls. Some of these may be able to predict relapse and have potential use as biomarkers to improve the current treatment regimes

Circulatory Failure and Outcome in Out-of-Hospital Cardiac Arrest

Circulatory failure is considered one of the entities of the post cardiac arrest syndrome contributing to poor outcome. It is reported at 15-70% of all patients successfully resuscitated from out-of-hospital cardiac arrest (OHCA). The pathophysiologic mechanism is attributed to limitation of cell metabolism due to inadequate supply of oxygen, caused by pump or conduction failure within the cardiovascular system. The term, however, remains poorly defined and no general consensus on definition exists. Due to the heterogeneity in definition and mechanism, the association with outcome for circulatory failure in cardiac arrest varies, and is partly conflicting. In this thesis we investigate four different surrogate measures of circulatory failure and their association with outcome after out-of-hospital cardiac arrest.Paper I: We conducted a post hoc analysis of adult, unconscious survivors of out-of-hospital included in the TTM- 1 trial, to investigate lactate, a marker of anaerobic metabolism, as a predictor of short-term survival. 877 patients had admission lactate sampled and were included in analyses. Lactate at admission and 12 hours were independently associated with 30-day survival in a model adjusted for known predictors of survival after out-of- hospital cardiac arrest. Estimations of area under the receiver operator curve indicate a poor precision for predicting short time survival, limiting the clinical utility for lactate metrics as a sole predictor of outcome.Paper II: Copeptin, physiologically associated with vasoregulatory status, was analyzed as a marker of severity of circulatory failure, in this post hoc analysis of 690 patients included in the TTM-1 biobank sub study. Copeptin measured at 24 hours was found to be independently associated with 30-day survival, circulatory etiology of death and cardiovascular deterioration.Paper III: In this retrospective registry study of 4004 adult, unconscious patients resuscitated from OHCA, a composite definition of circulatory shock (systolic blood pressure < 90 mmHg, or use of inotropes/vasoactive agents, or clinical signs of hypoperfusion), compared to no circulatory shock on admission was associated with worse odds of good neurological outcome at hospital discharge in an analysis adjusted for baseline comorbidity and predictors of outcome.Paper IV: Patients with moderate vasopressor support (defined as mean arterial pressure < 70 mmHg and/or adrenalin/noradrenaline dose ≤ 0.25 μg/kg/min) treated with target temperature management at 33oC had higher incidence of 6-month mortality compared to patients treated with normothermia, in a post hoc analysis of 1861 OHCA patients included in the TTM-2 trial. No difference in mortality was detected with temperature intervention in patients with no- or high vasopressor support. The increase in mortality seems to be driven by an increase in 30- day incidence of non-neurological death in patients treated at 33oC, compared to normothermia, in the moderate vasopressor support group, while no difference in etiology of death was detected for intervention in the no-, and high vasopressor support group.Conclusion: Circulatory failure after OHCA is associated with outcome, however, the mechanism is complex and probably contains multiple pathways

Quality-of-Life Indicators for African American and European American Long-term Survivors of Early-stage Breast Cancer

This meta-analysis investigated the difference in perceptions of health-related quality of life (HRQOL) among long-term early-stage breast cancer survivors (BCS). The comparison was between African American and European American women. Initial pilot searches suggested that enough studies existed for a meaningful meta-analysis of a BCS population at least 5 years post diagnosis. Only studies using the outcome measure HRQOL were included in the study; this yielded an initial sample of 212 study reports, with 56 reports entering the coding phase of the process. African American women were grossly underrepresented in this set of studies in comparison to the overall breast cancer population. Separate analyses of Medical Outcomes Study 36- Item Short- Form Health Survey, Quality of Life-Cancer Survivor and Quality of Life Index - Cancer Version III instruments were executed. However, no stringent comparison across instruments of the difference between the HRQOL of African American and European American women was possible. When African American women were included in the populations, researchers often did not report their data separately but rather included their data in an overall population and thus differences were masked. The data that were available, including qualitative studies for African American women, suggested that there was a lower perception of the quality of survival in some areas for African American women. These differences suggest the need for greater attention to the physical components of African American BCS. The results point to a need to improve African American participant recruitment in research and to use online databases as a results repository to improve data availability for analysis