Assessment of the Physiological Network in Sleep Apnea

Objective: Machine Learning models, in particular Artificial Neural Networks, have shown to be applicable in clinical research for tumor detection and sleep phase classification. Applications in systems medicine and biology, for example in Physiological Networks, could benefit from the ability of these methods to recognize patterns in high-dimensional data, but decisions of an Artificial Neural Network cannot be interpreted based on the model itself. In a medical context this is an undesirable characteristic, because hidden age, gender or other data biases negatively impact the model quality. If insights are based on a biased model, the ability of an independent study to come to similar conclusions is limited and therefore an essential property of scientific experiments, known as results reproducibility, is violated. Besides results reproducibility, methods reproducibility allows others to reproduce exact outputs of computational experiments, but requires data, code and runtime environments to be available. These challenges in interpretability and reproducibility are addressed as part of an assessment of the Physiological Network in Obstructive Sleep Apnea. Approach: A research platform is developed, that connects medical data, code and environ-ments to enable methods reproducibility. The platform employs a compute cluster or cloud to accelerate the demanding model training. Artificial Neural Networks are trained on the Physiological Network data of a healthy control group for age and gender prediction to verify the influence of these biases. In a subsequent study, an Artificial Neural Network is trained to classify the Physiological Networks in Obstructive Sleep Apnea and a healthy control group. The state-of-the-art interpretation method DeepLift is applied to explain model predictions. Results: An existing collaboration platform has been extended for sleep research data and modern container technologies are used to distribute training environments in compute clusters. Artificial Neural Network models predict the age of healthy subjects in a resolution of one decade and correctly classify the gender with 91% accuracy. Due to the verified biases, a matched dataset is created for the classification of Obstructive Sleep Apnea. The classification accuracy reaches 87% and DeepLift provides biomarkers as significant indicators towards or against the disorder. Analysis of misclassified samples shows potential Obstructive Sleep Apnea phenotypes. Significance: The presented platform is extensible for future use cases and focuses on the reproducibility of computational experiments, a concern across many disciplines. Machine learning approaches solve analysis tasks on high-dimensional data and novel interpretation techniques provide the required transparency for medical applications.Ziel: Methoden des maschinellen Lernens, insbesondere künstliche neuronale Netze, finden Anwendung in der klinischen Forschung, um beispielsweise Tumorzellen oder Schlafphasen zu klassifizieren. Anwendungen in der Systemmedizin und -biologie, wie physiologische Netzwerke, könnten von der Fähigkeit dieser Methoden, Muster in großen Merkmalsräumen zu finden, profitieren. Allerdings sind Entscheidungen eines künstlichen neuronalen Netzes nicht allein anhand des Modells interpretierbar. In einem medizinischen Kontext ist dies eine unerwünschte Charakteristik, weil die Daten, mit denen ein Modell trainiert wird, versteckte Einflüsse wie Alters- und Geschlechtsabhängigkeiten beinhalten können. Erkenntnisse, die auf einem beeinflussten Modell basieren, sind nur bedingt durch unabhängige Studien nach-vollziehbar, sodass keine Ergebnisreproduzierbarkeit gegeben ist. Neben der Ergebnisreproduzier-barkeit bezeichnet Methodenreproduzierbarkeit die Möglichkeit exakte Programmausgaben zu reproduzieren, was die Verfügbarkeit von Daten, Programmcode und Ausführungsumgebungen voraussetzt. Diese Promotion untersucht Veränderungen im physiologischen Netzwerk bei obstruktivem Schlafapnoesyndrom mit Methoden des maschinellen Lernens und adressiert dabei die genannten Herausforderungen der Interpretierbarkeit und Reproduzierbarkeit. Ansatz: Es wird eine Forschungsplattform entwickelt, die medizinische Daten, Programmcode und Ausführungsumgebungen verknüpft und damit Methodenreproduzierbarkeit ermöglicht. Die Plattform bindet zur Beschleunigung des ressourcenintensiven Modelltrainings verteilte Rechenressourcen in Form eines Clusters oder einer Cloud an. Künstliche neuronale Netze werden zur Bestimmung des Alters und des Geschlechts anhand der physiologischen Daten einer gesunden Kontrollgruppe trainiert, um den Einfluss der Alters- und Geschlechtsabhängigkeiten zu untersuchen. In einer Folgestudie werden die Unterschiede im physiologischen Netzwerk einer Gruppe mit obstruktivem Schlafapnoesyndrom und einer gesunden Kontrollgruppe klassifiziert. DeepLift, eine Interpretationsmethode nach aktuellem Stand der Technik, wird zur Erklärung der Modellvorhersagen angewendet. Ergebnisse: Eine existierende Forschungsplattform wurde für die Verarbeitung schlafbezogener Forschungsdaten erweitert und Containertechnologien ermöglichen die Bereitstellung der Ausführungsumgebung eines Experiments in einem Cluster. Künstliche neuronale Netze können anhand der physiologischen Daten das Alter einer Person bis auf eine Dekade genau bestimmen und eine Geschlechtsklassifikation erreicht eine Genauigkeit von 91%. Die Ergebnisse bestätigen den Einfluss der Alters- und Geschlechtsabhängigkeiten, sodass für Schlafapnoeklassifikationen zunächst eine Datenbasis geschaffen wird, in der die Geschlechts- und Altersverteilung zwischen gesunden und kranken Gruppen ausgeglichen ist. Die resultierenden Modelle erreichen eine Klassifikationsgenauigkeit von 87%. DeepLift weist auf Biomarker und mögliche physiologische Schlafapnoe-Phänotypen im Tiefschlaf hin. Signifikanz: Die vorgestellte Plattform ist für zukünftige Anwendungsfälle erweiterbar und ermöglicht Methodenreproduzierbarkeit, was über den Einsatz in der Medizin hinaus auch in anderen Disziplinen von Bedeutung ist. Maschinelles Lernen bietet sinnvolle Ansätze für die Analyse hochdimensionaler Daten und neue Interpretationstechniken schaffen die notwendige Transparenz für medizinische Anwendungszwecke

Sleep apnea cardiovascular clinical trials - current status and steps forward: the International Collaboration of Sleep Apnea Cardiovascular Trialists

Sleep apnea is a common chronic disease that is associated with coronary heart disease, stroke, heart failure and mortality, although the ability of sleep apnea treatment to reduce cardiovascular morbidity and mortality has not been demonstrated. In contrast to patients seeking treatment in sleep disorders centers, as many as half of individuals with moderate to severe sleep apnea in the general population do not report excessive sleepiness; however, if treatment of sleep apnea were shown to reduce cardiovascular disease risk, this would provide a strong rationale for treatment of sleep apnea even in the absence of daytime sleepiness. This article summarizes the status of clinical trials evaluating the potential cardiovascular benefits of sleep apnea treatment and discusses the challenges of conducting such trials, and introduces the International Collaboration of Sleep Apnea Cardiovascular Trialists (INCOSACT), a clinical research collaboration formed to foster cardiovascular sleep research.Australian National Health and Medical Research Counci

Dementia with Lewy bodies research consortia: A global perspective from the ISTAART Lewy Body Dementias Professional Interest Area working group

Dementia with Lewy bodies (DLB) research has seen a significant growth in international collaboration over the last three decades. However, researchers face a challenge in identifying large and diverse samples capable of powering longitudinal studies and clinical trials. The DLB research community has begun to focus efforts on supporting the development and harmonization of consortia, while also continuing to forge networks within which data and findings can be shared. This article describes the current state of DLB research collaborations on each continent. We discuss several established DLB cohorts, many of whom have adopted a common framework, and identify emerging collaborative initiatives that hold the potential to expand DLB networks and diversify research cohorts. Our findings identify geographical areas into which the global DLB networks should seek to expand, and we propose strategies, such as the creation of data-sharing platforms and the harmonization of protocols, which may further potentiate international collaboration.publishedVersio

Agreement in the scoring of respiratory events and sleep among international sleep centers.

To access publisher's full text version of this article. Please click on the hyperlink in Additional Links field.Abstract STUDY OBJECTIVES: The American Academy of Sleep Medicine (AASM) guidelines for polysomnography (PSG) scoring are increasingly being adopted worldwide, but the agreement among international centers in scoring respiratory events and sleep stages using these guidelines is unknown. We sought to determine the interrater agreement of PSG scoring among international sleep centers. DESIGN: Prospective study of interrater agreement of PSG scoring. SETTING: Nine center-members of the Sleep Apnea Genetics International Consortium (SAGIC). MEASUREMENTS AND RESULTS: Fifteen previously recorded deidentified PSGs, in European Data Format, were scored by an experienced technologist at each site after they were imported into the locally used analysis software. Each 30-sec epoch was manually scored for sleep stage, arousals, apneas, and hypopneas using the AASM recommended criteria. The computer-derived oxygen desaturation index (ODI) was also recorded. The primary outcome for analysis was the intraclass correlation coefficient (ICC) of the apnea-hypopnea index (AHI). The ICCs of the respiratory variables were: AHI = 0.95 (95% confidence interval: 0.91-0.98), total apneas = 0.77 (0.56-0.87), total hypopneas = 0.80 (0.66-0.91), and ODI = 0.97 (0.93-0.99). The kappa statistics for sleep stages were: wake = 0.78 (0.77-0.79), nonrapid eye movement = 0.77 (0.76-0.78), N1 = 0.31 (0.30-0.32), N2 = 0.60 (0.59-0.61), N3 = 0.67 (0.65-0.69), and rapid eye movement = 0.78 (0.77-0.79). The ICC of the arousal index was 0.68 (0.50-0.85). CONCLUSION: There is strong agreement in the scoring of respiratory events among the SAGIC centers. There is also substantial epoch-by-epoch agreement in scoring sleep variables. Our results suggest that centralized scoring of PSGs may not be necessary in future research collaboration among international sites where experienced, well-trained scorers are involved.NHLBI P01 HL094307 HL093463 Tzagournis Medical Research Endowment Funds of The Ohio State Universit

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data

The “Hypertension Approaches in the Elderly: a Lifestyle study” multicenter, randomized trial (HAEL Study): rationale and methodological protocol

Background: Hypertension is a clinical condition highly prevalent in the elderly, imposing great risks to cardiovascular diseases and loss of quality of life. Current guidelines emphasize the importance of nonpharmacological strategies as a first-line approach to lower blood pressure. Exercise is an efficient lifestyle tool that can benefit a myriad of health-related outcomes, including blood pressure control, in older adults. We herein report the protocol of the HAEL Study, which aims to evaluate the efficacy of a pragmatic combined exercise training compared with a health education program on ambulatory blood pressure and other health-related outcomes in older individuals. Methods: Randomized, single-blinded, multicenter, two-arm, parallel, superiority trial. A total of 184 subjects (92/center), ≥60 years of age, with no recent history of cardiovascular events, will be randomized on a 1:1 ratio to 12-week interventions consisting either of a combined exercise (aerobic and strength) training, three times per week, or an active-control group receiving health education intervention, once a week. Ambulatory (primary outcome) and office blood pressures, cardiorespiratory fitness and endothelial function, together with quality of life, functional fitness and autonomic control will be measured in before and after intervention. Discussion: Our conceptual hypothesis is that combined training intervention will reduce ambulatory blood pressure in comparison with health education group. Using a superiority framework, analysis plan prespecifies an intention-to-treat approach, per protocol criteria, subgroups analysis, and handling of missing data. The trial is recruiting since September 2017. Finally, this study was designed to adhere to data sharing practices. Trial registration: NCT03264443. Registered on 29 August, 2017

Age, sex, and setting in the etiology of stroke study (ASSESS): Study design and protocol

RATIONALE: Stroke etiology and risk factors vary by age, sex, setting (hospital or community-based) and by region. Identifying these differences would improve our understanding of stroke etiology, diagnosis, and treatment. AIM: The Age, Sex and Setting in the Etiology of Stroke Study (ASSESS) is a multicenter cohort study to assess differences in stroke etiology. METHODS AND DESIGN: Data from all centers will be categorized according to age, sex, setting, stroke subtypes. Centers with extensive hospital- or community-based data regarding stroke from Argentina, Australia, Canada, India, Iran, Italy, Ghana, Nigeria, Thailand, the United Kingdom and the United States have agreed to participate so far. STUDY OUTCOMES: The primary outcome includes differences in stroke etiology in study centers. The secondary outcomes include stroke incidence, risk factors, preventive strategies, and short- and long-term outcomes. CONCLUSION: ASSESS will enable comparisons of data from different regions to determine the age and sex distribution of the most common causes of stroke in each setting. This will help clinicians to tailor the assessment and treatment of stroke patients on the basis of their specific local characteristics. It will also empower stroke epidemiologists to design preventive measures by targeting the specific characteristics of each population

A new tool to screen patients with severe obstructive sleep apnea in the primary care setting : a prospective multicenter study

Altres ajuts: Sociedad Española de Neumología y Cirugía Torácica (SEPAR), Societat Catalana de Pneumologia (SOCAP).The coordination between different levels of care is essential for the management of obstructive sleep apnea (OSA). The objective of this multicenter project was to develop a screening model for OSA in the primary care setting. Anthropometric data, clinical history, and symptoms of OSA were recorded in randomly selected primary care patients, who also underwent a home sleep apnea test (HSAT). Respiratory polygraphy or polysomnography were performed at the sleep unit to establish definite indication for continuous positive airway pressure (CPAP). By means of cross-validation, a logistic regression model (CPAP yes/no) was designed, and with the clinical variables included in the model, a scoring system was established using the β coefficients (PASHOS Test). In a second stage, results of HSAT were added, and the final accuracy of the model was assessed. 194 patients completed the study. The clinical test included the body mass index, neck circumference and observed apneas during sleep (AUC 0.824, 95% CI 0.763-0.886, P < 0.001). In a second stage, the oxygen desaturation index (ODI) of 3% (ODI3% ≥ 15%) from the HSAT was added (AUC 0.911, 95% CI 0.863-0.960, P < 0.001), with a sensitivity of 85.5% (95% CI 74.7-92.1) and specificity of 67.8% (95% CI 55.1-78.3). The use of this model would prevent referral to the sleep unit for 55.1% of the patients. The two-stage PASHOS model is a useful and practical screening tool for OSA in primary care for detecting candidates for CPAP treatment. Clinical Trial Registration Registry: ClinicalTrials.gov; Name: PASHOS Project: Advanced Platform for Sleep Apnea Syndrome Assessment; URL: ; Identifier: NCT02591979. Date of registration: October 30, 2015. The online version contains supplementary material available at 10.1186/s12890-022-01827-0