Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert

Sport treiben ein Leben lang?: Einfluss der Sportkarriere der 1.Lebenshälfte auf das Sportengagement im mittleren und späten Erwachsenenleben

Zusammenfassung: Aktuelle demographische Entwicklungen rücken Fragen nach den Einflussfaktoren der Sportbeteiligung von Menschen in der 2.Lebenshälfte in den Mittelpunkt sportwissenschaftlichen Interesses. Aufgrund der vielfältigen Lebenserfahrungen dieser Altersgruppe stellt sich die Frage, inwieweit die sportliche Vorgeschichte das aktuelle Sportengagement beeinflusst. Ausgehend vom Ansatz der Lebensverlaufsforschung wurden hierzu Personen ab dem 50.Lebensjahr zu ihrem aktuellen und früheren Sportengagement im retrospektiven Längsschnitt befragt. Die Ergebnisse zeigen, dass insbesondere ein langjähriges Sportengagement in der 1.Lebenshälfte sowie sportliche Aktivitäten im frühen Erwachsenenalter den Verlauf des Sportengagements in der 2.Lebenshälfte positiv beeinflussen. Darüber hinaus weisen Perioden- und Kohorteneffekte darauf hin, dass die lebenszeitlichen Abhängigkeiten des Sportengagements unter dem moderierenden Einfluss des sozialen Faktors Geschlecht sowie gesellschaftlicher Rahmenbedingungen stehe

Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG

Features extracted from P waves of the 12-lead electrocardiogram (ECG) have proven valuable for non-invasively estimating the left atrial fibrotic volume fraction associated with the arrhythmogenesis of atrial fibrillation. However, feature extraction in the clinical context is prone to errors and oftentimes yields unreliable results in the presence of noise. This leads to inaccurate input values provided to machine learning algorithms tailored at estimating the amount of atrial fibrosis with clinical ECGs.Another important aspect for clinical translation is the network’s generalization ability regarding newECGs.To quantify a network’s sensitivity to inaccurately extracted P wave features, we added Gaussian noise to the features extracted from 540,000 simulated ECGs consisting of P wave duration, dispersion, terminal force in lead V1, peak-to-peak amplitudes, and additionallythoracic and atrial volumes. For assessing generalization, we evaluated the network performance for train-validation-test splits divided such that ECGs simulated with the same atria or torso geometry only belongedto either the trainingand validationor the test set. The root mean squared error (RMSE) of the network increased the most in case of noisy torso volumes and P wave durations. Large generalization errors witha RMSEdifference between training and test set of more than 2% fibrotic volume fraction only occurred ifveryhigh or low atria and torso volumes were left out during training.Our results suggest that P wave duration and thoracic volume are features that have to be measured accurately if employed for estimating atrial fibrosis with a neural network. Furthermore, our method is capable of generalizing wellto ECGs simulated with anatomical models excluded during training and thus meets an important requirement for clinical translation

A bi-atrial statistical shape model for large-scale in silico studies of human atria: model development and application to ECG simulations

Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 23 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 104ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. The novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches

Sport and leisure-time physical activity over the life course

It is desirable to get as many people as possible to engage in long-term leisure-time physical activity (LTPA) due to the health-enhancing effects. Although the proportion of individuals who are physically active in their leisure time appears to have increased in Switzerland in the past years (e.g., Lamprecht et al., 2020), little is known so far about the dynamic of change in LTPA trajectories over the life course. LTPA trajectories of 1,456 Swiss resi- dents aged 35 to 76 years (random sampling) were reconstructed with the help of a retrospective telephone interview (CATI method). To address the dif- ficulties of retrospective data collection, the article presents the careful development of the questionnaire on the basis of current evidence. The majority of the respondents (approx. 73%) show a long-term LTPA without dropout (dropout = LTPA less than once a week over one year and longer), only a minority of whom (approx. 18%) took up their LTPA after the age of 20. In addition, there is also a group with a somewhat unstable LTPA trajectory (approx. 24%) that includes at least one dropout. For members of the latter group, the longer the inactive episode lasted, the lower were their chances of entering an LTPA. While the different LTPA trajectory groups differed only slightly with regard to socioeconomic characteristics, analyses of their sport- and physical activity-related history reveal that self-organized LTPA in child- hood and youth may be seen as a success factor for lifelong LTPA. The pro- portion of people practicing (long-term) LTPA is presumably overrepresented in the sample. This limitation should be taken into account, but analyses of possible advantageous conditions of long-term or lifelong LTPA are neverthe- less possible. The results indicate a demand for more specific theories related to the causality behind the observable LTPA behavio

Selection criteria for flagship species by conservation organizations

Flagship species are among key marketing tools used by conservation organizations to motivate public support, but are often selected in an ad hoc, rather than systematic, manner. Furthermore, it is unclear whether selected flagship species do motivate public support. This paper describes a multi-method exploratory study, carried out in Switzerland, which aimed to determine the selection criteria for flagship species and measure whether a species selected according to these criteria was able to motivate support. Fourteen representatives of international, regional and local conservation organizations were interviewed and the selection criteria for their flagship species were identified. A charismatic species (the great spotted woodpecker) that meets these criteria and an apparently less charismatic species (the clover stem weevil) were selected as treatments in a quantitative experiment with 900 respondents. Using conjoint analysis, it was found that both charismatic and uncharismatic species have the ability to positively influence public preferences for habitat variables that encourage biodiversity in urban landscapes. These results may be used by conservation organizations to assist in the selection of flagship species, and in particular for flagship species that are intended to perform a specific conservation functio

Changes in German sport participation

Sport has become a highly differentiated social phenomenon in recent years. Changes in society, such as individualization, the growing significance of the health and body culture, and changing values, are considered to be generative mechanisms for increasing social importance and the differentiation of modern sport. Although discussions in sport sociology attribute the changes observed in recent decades of sport participation to a socially determined differentiation of sport, this premise has hardly ever been empirically tested. The present study examines to what extent the postulated developments in sport can be observed on the micro level of those engaging in sport, by examining sport behaviour from a contemporary historical perspective. Based on a life-course approach to research, a total of 1739 over 50-year-olds in Germany were asked about their sport participation as part of a retrospective longitudinal study. Results show that the increasing differentiation of sport can be documented by more diversified forms of individual sport careers. During a 30-year observation period the popularity of competitive sport decreased and the variety of ways in which sport was organized increased. A differentiated analysis based on examining three birth cohorts showed that the reported change in sport participation can be attributed to age, cohort and period effects. In addition, the present study examines how specific events in contemporary history are reflected in individual sporting careers. Sport careers in Chemnitz (Eastern Germany) and Braunschweig (Western Germany) differed before German reunification, but these differences have evened out after the political changes and the process of transformation

Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG—A Large-Scale Computational Study Covering Anatomical Variability

The arrhythmogenesis of atrial fibrillation is associated with the presence of fibrotic atrial tissue. Not only fibrosis but also physiological anatomical variability of the atria and the thorax reflect in altered morphology of the P wave in the 12-lead electrocardiogram (ECG). Distinguishing between the effects on the P wave induced by local atrial substrate changes and those caused by healthy anatomical variations is important to gauge the potential of the 12-lead ECG as a non-invasive and cost-effective tool for the early detection of fibrotic atrial cardiomyopathy to stratify atrial fibrillation propensity. In this work, we realized 54,000 combinations of different atria and thorax geometries from statistical shape models capturing anatomical variability in the general population. For each atrial model, 10 different volume fractions (0–45%) were defined as fibrotic. Electrophysiological simulations in sinus rhythm were conducted for each model combination and the respective 12-lead ECGs were computed. P wave features (duration, amplitude, dispersion, terminal force in V1) were extracted and compared between the healthy and the diseased model cohorts. All investigated feature values systematically in- or decreased with the left atrial volume fraction covered by fibrotic tissue, however value ranges overlapped between the healthy and the diseased cohort. Using all extracted P wave features as input values, the amount of the fibrotic left atrial volume fraction was estimated by a neural network with an absolute root mean square error of 8.78%. Our simulation results suggest that although all investigated P wave features highly vary for different anatomical properties, the combination of these features can contribute to non-invasively estimate the volume fraction of atrial fibrosis using ECG-based machine learning approaches

A Large-scale Virtual Patient Cohort to Study ECG Features of Interatrial Conduction Block

Interatrial conduction block refers to a disturbance in the propagation of electrical impulses in the conduction pathways between the right and the left atrium. It is a risk factor for atrial fibrillation, stroke, and premature death. Clinical diagnostic criteria comprise an increased P wave duration and biphasic P waves in lead II, III and aVF due to retrograde activation of the left atrium. Machine learning algorithms could improve the diagnosis but require a large-scale, well-controlled and balanced dataset. In silico electrocardiogram (ECG) signals, optimally obtained from a statistical shape model to cover anatomical variability, carry the potential to produce an extensive database meeting the requirements for successful machine learning application. We generated the first in silico dataset including interatrial conduction block of 9,800 simulated ECG signals based on a bi-atrial statistical shape model. Automated feature analysis was performed to evaluate P wave morphology, duration and P wave terminal force in lead V1. Increased P wave duration and P wave terminal force in lead V1 were found for models with interatrial conduction block compared to healthy models. A wide variability of P wave morphology was detected for models with interatrial conduction block. Contrary to previous assumptions, our results suggest that a biphasic P wave morphology seems to be neither necessary nor sufficient for the diagnosis of interatrial conduction block. The presented dataset is ready for a classification with machine learning algorithms and can be easily extended