Association of beat-to-beat P-wave analysis index to the extent of left atrial low-voltage areas in patients with paroxysmal atrial fibrillation

BACKGROUND

Left atrial (LA) fibrosis has been shown to be associated with atrial fibrillation (AF) recurrence. Beat-to-beat (B2B) index is a non-invasive classifier, based on B2B P-wave morphological and wavelet analysis, shown to be associated with AF incidence and recurrence. In this study, we tested the hypothesis that the B2B index is associated with the extent of LA low-voltage areas (LVAs) on electroanatomical mapping.

METHODS

Patients with paroxysmal AF scheduled for pulmonary vein isolation, without evident structural remodeling, were included. Pre-ablation electroanatomical voltage maps were used to calculate the surface of LVAs (<0.5 mV). B2B index was compared between patients with small versus large LVAs.

RESULTS

35 patients were included (87% male, median age 62). The median surface area of LVAs was 7.7 (4.4-15.8) cm2 corresponding to 5.6 (3.3-12.1) % of LA endocardial surface. B2B index was 0.57 (0.52-0.59) in patients with small LVAs (below the median) compared to 0.65 (0.56-0.77) in those with large LVAs (above the median) (p=0.009). In the receiver operator characteristic curve analysis for predicting large LVAs, the c-statistic was 0.75 (p=0.006) for B2B index and 0.81 for the multivariable model including B2B index (multivariable p=0.04) and P-wave duration.

CONCLUSION

In patients with paroxysmal AF without overt atrial myopathy, B2B P-wave analysis appears to be a useful non-invasive correlate of low-voltage areas-and thus fibrosis-in the LA. This finding establishes a pathophysiological basis for B2B index and its potential usefulness in the selection process of patients who are likely to benefit most from further invasive treatment.

Association between antihypertensive treatment, blood pressure variability, and stroke severity and outcomes in acute ischemic stroke

OBJECTIVES

The management of blood pressure (BP) and the role of antihypertensive medications (AHT) in acute ischemic stroke (AIS) remain uncertain. This study aimed to investigate the impact of pre- and intra-stroke AHT use on systolic (SBP), diastolic (DBP), and blood pressure variability (BPV).

MATERIALS AND METHODS

A post-hoc analysis was conducted on 228 AIS patients from the PREVISE study. All patients underwent 24-hour ambulatory blood pressure monitoring within 48 h of symptom onset. Clinical and laboratory data, as well as AHT details, were recorded. Mean BP parameters and BPV for SBP and DBP were computed. The study endpoint was 3-month mortality.

RESULTS

The majority of stroke patients (84.2%) were already taking AHTs. Beta blockers and ACE inhibitors use before and after stroke were linked to higher DBP variability. Prior angiotensin receptor blockers (ARBs) and vasodilators use correlated with increased SBP variability and lower daytime SBP/DBP levels, respectively. The continuation, discontinuation, or change of AHTs after stroke onset did not significantly affect outcomes. Patients under AHTs during AIS exhibited reduced mortality, with those previously using calcium channel blockers experiencing less severe strokes, and those previously using ARBs showing better outcomes at three months.

CONCLUSIONS

These findings advocate for personalized BP management in AIS, based on a patient's antihypertensive history. These insights could enhance treatment efficacy, guide research, and improve care for acute ischemic stroke patients.

Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology

Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely.

P-wave Indices as Predictors of Atrial Fibrillation: The Lion from a Claw

The P wave, representing the electrical fingerprint of atrial depolarization, contains information regarding spatial and temporal aspects of atrial electrical-and potentially structural-properties. However, technical and biological reasons, including-but not limited to-the low amplitude of the P wave and large interindividual variations in normal or pathologic atrial electrical activity, make gathering and utilizing this information for clinical purposes a rather cumbersome task. However, even crude ECG descriptors, such as P-wave dispersion, have been shown to be of predictive value for assessing the probability that a patient already has or will shortly present with AF. More sophisticated methods of analyzing the ECG signal, on a single- or multi- beat basis, along with novel approaches to data handling, namely machine learning, seem to be leading up to more accurate and robust ways to obtain clinically useful information from the humble P wave.

Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?

DNA methylation of cytosine-guanine sites (CpGs) is associated with type 1 diabetes (T1D). The sequence of methylated and non-methylated sites in a specific genetic region constitutes its methyl-haplotype. The aim of the present study was to identify insulin gene promoter (IGP) methyl-haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl-haplotypes. A total of 40 individuals (20 T1D) participated. The IGP region from peripheral whole blood DNA of 40 participants (20 T1D) was sequenced using next-generation sequencing, sequences were read using FASTQ files and methylation status was calculated by python-based pipeline for targeted deep bisulfite sequenced amplicons (ampliMethProfiler). Methylation profile at 10 CpG sites proximal to transcription start site of the IGP was recorded and coded as 0 for unmethylation or 1 for methylation. A single read could result in '1111111111' methyl-haplotype (all methylated), '000000000' methyl-haplotype (all unmethylated) or any other combination. Principal component analysis was applied to the generated methyl-haplotypes for dimensionality reduction, and the first three principal components were employed as features with five different classifiers (random forest, decision tree, logistic regression, Naive Bayes, support vector machine). Naive Bayes was the best-performing classifier, with 0.9 accuracy. Predictive models were evaluated using receiver operating characteristics (AUC 0.96). Methyl-haplotypes '1111111111', '1111111011', '1110111111', '1111101111' and '1110101111' were revealed to be the most significantly associated with T1D according to the dimensionality reduction method. Methylation-based biomarkers such as IGP methyl-haplotypes could serve to identify individuals at high risk for T1D.

Midday Dipping and Circadian Blood Pressure Patterns in Acute Ischemic Stroke

The purpose of this study was to investigate the alterations in blood pressure (BP) during midday and the changes in circadian BP patterns in the acute phase of ischemic stroke (AIS) with the severity of stroke and their predictive role outcomes within 3 months. A total of 228 AIS patients (a prospective multicenter follow-up study) underwent 24 h ambulatory blood pressure monitoring (ABPM). Mean BP parameters during the day (7:00-22:59), the midday (13:00-16:59), and the night (23:00-6:59), and midday and nocturnal dipping were calculated. Midday SBP dippers had less severe stroke, lower incidence of hypertension and SBP/DBP on admission, lower levels of serum glucose and WBCs, and delayed initiation of ABPM compared to risers. There was a reverse relation between midday SBP dipping and both nocturnal dipping and stroke severity. The "double dippers" (midday and nocturnal dipping) had the least severe stroke, the lowest SBP/DBP on admission, the lowest heart rate from ABPM, and a lower risk of an unfavorable outcome, while the "double risers" had the opposite results, by an approximately five-fold risk of death/disability at 3 months. These findings indicate different circadian BP patterns during the acute phase of AIS, which could be considered a marker of stroke severity and prognosis.

Prognostic significance of 24-h blood pressure and variability indices in the outcome of acute ischaemic stroke

BACKGROUND

The association between blood pressure (BP) levels and BP variability (BPV) following acute ischaemic stroke (AIS) and outcome remains controversial.

AIMS

To investigate the predictive value of systolic BP (SBP) and diastolic BP (DBP) and BPV measured using 24-h ambulatory blood pressure monitoring (ABPM) methods during AIS regarding outcome.

METHODS

A total of 228 AIS patients (175 without prior disability) underwent ABPM every 20 min within 48 h from onset using an automated oscillometric device (TM 2430, A&D Company Ltd) during day time (7:00-22:59) and night time (23:00-6:59). Risk factors, stroke subtypes, clinical and laboratory findings were recorded. Mean BP parameters and several BPV indices were calculated. End-points were death and unfavourable functional outcome (disability/death) at 3 months.

RESULTS

A total of 61 (26.7%) patients eventually died. Multivariate logistic regression analysis revealed that only mean night-time DBP (hazard ratio (HR): 1.04; 95% confidence interval (CI): 1.00-1.07) was an independent prognostic factor of death. Of the 175 patients without prior disability, 79 (45.1%) finally met the end-point of unfavourable functional outcome. Mean 24-h SBP (HR: 1.03; 95% CI: 1.00-1.05), day-time SBP (HR: 1.02; 95% CI: 1.00-1.05) and night-time SBP (HR: 1.03; 95% CI: 1.01-1.05), SBP nocturnal decline (HR: 0.93; 95% CI: 0.88-0.99), mean 24-h DBP (HR: 1.08; 95% CI: 1.03-1.13), day-time DBP (HR: 1.07; 95% CI: 1.03-1.12) and night-time DBP (HR: 1.06; 95% CI: 1.02-1.10) were independent prognostic factors of an unfavourable functional outcome.

CONCLUSIONS

In contrast with BPV indices, ABPM-derived BP levels and lower or absence of BP nocturnal decline in the acute phase are prognostic factors of outcome in AIS patients.

Gamifying rehabilitation: MILORD platform as an upper limb motion rehabilitation service

Motor learning is based on the correct repetition of specific movements for their permanent storage in the central nervous system (CNS). Rehabilitation relies heavily on the repetition of specific movements, and game scenarios are ideal environments to build routines of repetitive exercises that have entertaining characteristics. In this respect, the gamification of the rehabilitation program, through the introduction of game-specific techniques and design concepts, has gained attention as a complementary or alternative to routine rehabilitation programs. A gamified rehabilitation program promises to gain the patient's attention, to reduce the monotony of the process and preserve motivation to attend, and to create virtual incentives through the game, toward maintaining compliance to the “prescribed” program. This is often achieved through goal-oriented tasks and real-time feedback in the form of points and other in-game rewards. This paper describes MILORD rehabilitation platform, an affordable technological solution, which aims to support health professionals and enable remote rehabilitation, while maintaining health service characteristics and monitoring. MILORD is an end-to-end platform that consists of an interactive computer game, utilizing a leap motion sensor, a centralized user management system, an analysis platform that processes the data generated by the game, and an analysis dashboard presenting a set of meaningful features that describe upper limb movement. Our solution facilitates the monitoring of the patients' progress and provides an alternative way to analyze hand movement. The system was tested with normal subjects and patients and experts to record user's experience, receive feedback, identify any problems, and understand the system's value in monitoring and support motion defect and progress. This small-scale study indicated the capacity of the analysis to quantify the movement in a meaningful way and express the differences between normal and pathological movement, and the user experience was positive with both patients and normal subjects.

P-wave beat-to-beat analysis to predict atrial fibrillation recurrence after catheter ablation

Introduction

Identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification.

Purpose

The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF, as a predictor of AF recurrence within a year after successful catheter ablation.

Methods

12-lead ECG and 10-minute vectorcardiogram (VCG) recordings were obtained from 138 consecutive patients scheduled for AF ablation. Pre-ablation B2B P-wave index, along with standard P-wave indices, clinical scores and patients history and physical examination parameters were evaluated as AF recurrence predictors.

Results

Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010). Prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA2DS2-VASc score ≥2 were also found to be related to higher recurrence rate. Multivariate analysis of predictors that can be assessed before ablation revealed that B2B P-wave index, along with heart failure history and history of previous stroke or transient ischemic attack are independent predicting factors of AF relapse.

Conclusion

B2B P-wave morphology and wavelet analysis, is a promising, non-invasive technique, able to identify patients prone to AF recurrence after pulmonary veins ablation. Further studies are needed to assess the predictive value of B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): Hellenic Society of Cardiology

Assessment of Singularities in the EEG During A-Phases of Sleep Based on Wavelet Decomposition

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.

The Effects of a Visual Stimuli Training Program on Reaction Time, Cognitive Function, and Fitness in Young Soccer Players

The purpose of the present study was to examine whether a visual stimuli program during soccer training can affect reaction time (RT), cognitive function, and physical fitness in adolescent soccer players. Thirty-eight male soccer players aged 10−15 were randomly assigned to either the intervention (Group A) or the control group (Group B). At baseline and at the end of the 6-month study FITLIGHT Trainer, the Cognitive Function Scanner Mobile Test Suite, a Virtual Reality (VR) game, and the ALPHA—Fitness and the Eurofit test batteries were used to measure participants’ abilities. After the baseline assessment, Group A followed their regular soccer training combined with a visual stimuli program, while Group B continued their regular soccer training program alone for 6 months. At the end of the 6-month study, Group A showed statistically significant improvements in simple RT by 11.8% (p = 0.002), repeated sprints by 13.4% (p ≤ 0.001), and Pen-to-Point Cognitive Function by 71.62% (p < 0.001) and 72.51% for dominant and non-dominant hands, respectively. However, a between-groups analysis showed that there was no statistically significant difference between the two groups in most of the measurements studied. In conclusion, a visual stimuli training program does not seem to add any value to the traditional soccer training program for adolescents. Nevertheless, this study helps to underline the potential of newly emerging technology as a tool for the assessment of RT.

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool's aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance- Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development.

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.

Diagnosis of Alzheimer's disease and Mild Cognitive Impairment using EEG and Recurrent Neural Networks

Alzheimer's disease (AD) is the main cause of dementia and Mild cognitive impairment (MCI) is a prodromal stage of AD whose early detection is considered crucial as it can contribute in slowing the progression of AD. In our study we attempted to classify a subject into AD, MCI, or Healthy Control (HC) groups with the use of electroencephalogram (EEG) data. Due to the time-series nature of EEG we exper-imented with the powerful recurrent neural network (RNN) classifiers and more specifically with models including basic or bidirectional Long Short-Term Memory (LSTM) modules. The EEG signals from 17 channels were preprocessed using a 0.1-32 Hz band-pass filter and then segmented into 2-second epochs during which, the subject had closed eyes. Finally, on each segment Fast Fourier Transform (FFT) was applied. To evaluate our models we studied four different classification problems: problem 1: separating subject into three classes (HC, MCI, AD) and problems 2-4: pairwise classifications AD vs. MCI, AD vs. HC and MCI vs. HC. For each problem we employed two different cross-validation approaches ( a) by segment and (b) by patient. In the first one, segments from a subject EEG may exist in both training and validations set, while in the second one, all the EEG segments of a subject can only exist in either the training or the validation set. In the AD-MCI-HC classification we achieved an accuracy of 99% by segment cross-validation, which was an improvement to earlier studies that utilized recurrent neural network models. In the pairwise classification problems we achieved over 90% accuracy by segment and over 80% by subject.

P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained. Univariate analysis revealed that patients with higher B2B P-wave index had a two-fold risk for AF recurrence (HR: 2.35, 95% CI: 1.24–4.44, p: 0.010), along with prolonged P-wave, interatrial block, early AF recurrence, female gender, heart failure history, previous stroke, and CHA₂DS₂-VASc score. Multivariate analysis of assessable predictors before ablation revealed that B2B P-wave index, along with heart failure history and a history of previous stroke or transient ischemic attack, are independent predicting factors of atrial fibrillation recurrence. Further studies are needed to assess the predictive value of the B2B index with greater accuracy and evaluate a possible relationship with atrial substrate analysis.

Combining Machine Learning and Network Analysis Pipelines: The Case of Microbiome and Metabolomics Data in Colorectal Cancer

This study analyzes samples of intestinal microbiome and metabolites, from healthy individuals (HE) and patients with adenomas (AD) or colorectal carcinomas (CRC). A network analysis (NetAn) method was applied to the data, to identify the metabolites and microbial genera associated with the 3 classes and then 7 classification models were used. The models were initially trained with classic feature selection vs features resulting from NetAn. The distinction of HE and AD is successful, while CRC distinction presented lower success.

Towards Data Integration for AI in Cancer Research. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:2054-2057. [PMID: 34891692 DOI: 10.1109/embc46164.2021.9629675]

Cancer research is increasing relying on data-driven methods and Artificial Intelligence (AI), to increase accuracy and efficiency in decision making. Such methods can solve a variety of clinically relevant problems in cancer diagnosis and treatment, provided that an adequate data availability is ensured. The generation of multicentric data repositories poses a series of integration and harmonization challenges. This work discusses the strategy, solutions and further issues identified along this procedure within the EU project INCISIVE that aims to generate an interoperable pan-European federated repository of medical images and an AI-based toolbox for medical imaging in cancer diagnosis and treatment.Clinical Relevance- Supporting the integration of medical imaging data and related clinical data into large interoperable repositories will enable the development, and validation, and wider adoption of AI-based methods in cancer diagnosis, prediction, treatment and follow-up.

Beat-to-beat P-wave analysis outperforms conventional P-wave indices in identifying patients with a history of paroxysmal atrial fibrillation, during sinus rhythm

Introduction

Atrial fibrillation (AF) is the most common arrhythmia and is associated with high risk of morbidity and mortality. In many patients, AF is of episodic character (paroxysmal AF – PAF), which makes the identification of these patients during sinus rhythm (SR) challenging.

Purpose

The aim of the present study is to compare the performance of beat-to-beat P-wave analysis with P-wave indices used as predictors of PAF, such as P-wave duration, area, voltage, axis, terminal force in V1, inter-atrial block or orthogonal type, in identifying patients with history of PAF during sinus rhythm.

Methods

Standard 12-lead ECG and 10-minute orthogonal ECG recordings were obtained from 40 consecutive patients with short history of PAF under no antiarrhythmic medication and 60 age- and sex- matched healthy controls. The P-waves on the 10-minute recordings were analyzed on a beat-to-beat basis and classified as belonging to a primary or secondary morphology according to previous study. Wavelet transform used to further analyze P-wave orthogonal signals of main morphology on a beat-to-beat basis.

Results

38 out of 327 studied features were found to differ significantly among the two groups. These features were tested for their diagnostic ability and receiver operating characteristic curves were ploted. Only 3 of them performed adequetly, with an area under curve (AUC) above 0.65; Two of them came from morphology analysis (percentage of beats following main morphology in axis X and Y) and one from wavelet analysis (max energy in high frequency zone -Y axis). Among standard P-wave indices, P-wave area in lead II was the one with the highest AUC (0.64).

Conclusion

Novel indices derived from beat-to-beat analysis outperform stadard P-wave markers in identifying patients with PAF history during sinus rhythm.

Funding Acknowledgement

Type of funding sources: None. ROC curves of most significant featuresAUC characteristics of P-wave indices

Network analysis with biological data of cancer patients: A scoping review

BACKGROUND & OBJECTIVE

Network Analysis (NA) is a mathematical method that allows exploring relations between units and representing them as a graph. Although NA was initially related to social sciences, the past two decades was introduced in Bioinformatics. The recent growth of the networks' use in biological data analysis reveals the need to further investigate this area. In this work, we attempt to identify the use of NA with biological data, and specifically: (a) what types of data are used and whether they are integrated or not, (b) what is the purpose of this analysis, predictive or descriptive, and (c) the outcome of such analyses, specifically in cancer diseases.

METHODS & MATERIALS

The literature review was conducted on two databases, PubMed & IEEE, and was restricted to journal articles of the last decade (January 2010 - December 2019). At a first level, all articles were screened by title and abstract, and at a second level the screening was conducted by reading the full text article, following the predefined inclusion & exclusion criteria leading to 131 articles of interest. A table was created with the information of interest and was used for the classification of the articles. The articles were initially classified to analysis studies and studies that propose a new algorithm or methodology. Each one of these categories was further screened by the following clustering criteria: (a) data used, (b) study purpose, (c) study outcome. Specifically for the studies proposing a new algorithm, the novelty presented in each one was detected.

RESULTS

& Conclusions: In the past five years researchers are focusing on creating new algorithms and methodologies to enhance this field. The articles' classification revealed that only 25% of the analyses are integrating multi-omics data, although 50% of the new algorithms developed follow this integrative direction. Moreover, only 20% of the analyses and 10% of the newly developed methodologies have a predictive purpose. Regarding the result of the works reviewed, 75% of the studies focus on identifying, prognostic or not, gene signatures. Concluding, this review revealed the need for deploying predictive and multi-omics integrative algorithms and methodologies that can be used to enhance cancer diagnosis, prognosis and treatment.

P-wave beat-to-beat morphology analysis outperforms conventional P-wave indices in detecting patients with paroxysmal atrial fibrillation

Funding Acknowledgements

Type of funding sources: None.

Background

Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders.

Purpose

The aim of this study is to compare the performance of standard P-wave indices with beat-to-beat P-wave morphological variability parameters in identifying patients with history of Paroxysmal Atrial Fibrillation (PAF).

Methods

Three-dimensional 1000Hz ECG digital recordings of 10 minutes duration were obtained from a total of 39 PAF patients and 60 healthy individuals. Following artifacts and ectopic beats removal, P‑wave morphology analysis was performed based on the dynamic application of the k‑means clustering process and main and secondary P-wave morphologies were identified. The percentage of P-waves following the main or the secondary morphology in each lead was calculated, as well as established indices such as Advanced Interatrial Block, P-wave duration, axis and area, P-wave Terminal Force in lead V1 and Orthogonal Leads Type 1, 2 or 3.

Results

9 out of 24 parameters studied, were found to be significantly different between the two groups. 7 of these indices were derived from morphology analysis and 2 from P-wave area. Logistic regression revealed that the percentage of P-waves allocated to main morphology in X axis performed better than all other indices in identifying patients with PAF history from healthy volunteers in terms of total accuracy and F1 measure.

Conclusion

P-wave beat-to-beat morphology analysis can identify PAF patients during normal sinus rhythm more efficiently than standard P-wave indices. Abstract Figure.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Serious Gaming Technology in Upper Extremity Rehabilitation: Scoping Review

Background

Serious gaming has increasingly gained attention as a potential new component in clinical practice. Specifically, its use in the rehabilitation of motor dysfunctions has been intensively researched during the past three decades.

Objective

The aim of this scoping review was to evaluate the current role of serious games in upper extremity rehabilitation, and to identify common methods and practice as well as technology patterns. This objective was approached via the exploration of published research efforts over time.

Methods

The literature search, using the PubMed and Scopus databases, included articles published from 1999 to 2019. The eligibility criteria were (i) any form of game-based arm rehabilitation; (ii) published in a peer-reviewed journal or conference; (iii) introduce a game in an electronic format; (iv) published in English; and (v) not a review, meta-analysis, or conference abstract. The search strategy identified 169 relevant articles.

Results

The results indicated an increasing research trend in the domain of serious gaming deployment in upper extremity rehabilitation. Furthermore, differences regarding the number of publications and the game approach were noted between studies that used commercial devices in their rehabilitation systems and those that proposed a custom-made robotic arm, glove, or other devices for the connection and interaction with the game platform. A particularly relevant observation concerns the evaluation of the introduced systems. Although one-third of the studies evaluated their implementations with patients, in most cases, there is the need for a larger number of participants and better testing of the rehabilitation scheme efficiency over time. Most of the studies that included some form of assessment for the introduced rehabilitation game mentioned user experience as one of the factors considered for evaluation of the system. Besides user experience assessment, the most common evaluation method involving patients was the use of standard medical tests. Finally, a few studies attempted to extract game features to introduce quantitative measurements for the evaluation of patient improvement.

Conclusions

This paper presents an overview of a significant research topic and highlights the current state of the field. Despite extensive attempts for the development of gamified rehabilitation systems, there is no definite answer as to whether a serious game is a favorable means for upper extremity functionality improvement; however, this certainly constitutes a supplementary means for motivation. The development of a unified performance quantification framework and more extensive experiments could generate richer evidence and contribute toward this direction.

Challenges and opportunities for telehealth in the management of chronic obstructive pulmonary disease: a qualitative case study in Greece

BACKGROUND

Telehealth (TH) was introduced as a promising tool to support integrated care for the management of chronic obstructive pulmonary disease (COPD). It aims at improving self-management and providing remote support for continuous disease management. However, it is often not clear how TH-supported services fit into existing pathways for COPD management. The objective of this study is to uncover where TH can successfully contribute to providing care for COPD patients exemplified in a Greek care pathway. The secondary objective is to identify what conditions need to be considered for successful implementation of TH services.

METHODS

Building on a single case study, we used a two-phase approach to identify areas in a Greek COPD care pathway where care services that are recommended in clinical guidelines are currently not implemented (challenges) and areas that are not explicitly recommended in the guidelines but that would benefit from TH services (opportunities). In phase I, we used the care delivery value chain framework to identify the divergence between the clinical guidelines and the actual practice captured by a survey with COPD healthcare professionals. In phase II, we conducted in-depth interviews with the same healthcare professionals based on the discovered divergences. The responses were analyzed with respect to identified opportunities for TH and care pathway challenges.

RESULTS

Our results reveal insights in two areas. First, several areas with challenges were identified: patient education, self-management, medication adherence, physical activity, and comorbidity management. TH opportunities were perceived as offering better bi-directional communication and a tool for reassuring patients. Second, considering the identified challenges and opportunities together with other case context details a set of conditions was extracted that should be fulfilled to implement TH successfully.

CONCLUSIONS

The results of this case study provide detailed insights into a care pathway for COPD in Greece. Addressing the identified challenges and opportunities in this pathway is crucial for adopting and implementing service innovations. Therefore, this study contributes to a better understanding of requirements for the successful implementation of integrated TH services in the field of COPD management. Consequently, it may encourage healthcare professionals to implement TH-supported services as part of routine COPD management.

Implementation of the Endocrine Society clinical practice guidelines for gestational diabetes mellitus to a knowledge tool

BACKGROUND

Despite the production of clinical practice guidelines (CPGs) in many medical areas, their use is not sufficiently adopted in clinical practice. Incorporation of CPGs in knowledge tools (KnowT) or decision support systems (DSS) for routine use can improve healthcare providers' compliance to CPGs.

MATERIALS AND METHODS

Clinical practice guidelines for gestational diabetes mellitus (GDM) were searched for, collected and compared. The CPG that met pre-specified criteria ([a] published by a European or American organization between 2010 and 2018, [b] being developed a systematic way and [c] having statements of "level of evidence" and "strength of recommendation") was chosen for implementation (Endocrine Society, 2013). Its recommendations were deconstructed, re-organized and reconstructed as an algorithm (in the form of a flowchart), which was integrated into a KnowT. Content completeness and evaluation of CPG by the Guideline Implementability Appraisal tool (GLIA) were performed as well. The primary objective was the development of a clinical algorithm in the field of GDM and its integration into a KnowT. The secondary objective was to demonstrate the completeness of the CPG content and evaluate its implementability in the KnowT.

RESULTS

Endocrine Society 2013 CPG was restructured as a flowchart, and a KnowT was constructed with the use of the "Openlabyrinth" software. The completeness of the content was confirmed, and GLIA appraisal demonstrated its implementability.

CONCLUSION

Endocrine Society 2013 CPG for GDM is a complete set of recommendations. Its structure makes possible the design of a clinical algorithm and its implementation into a KnowT.

A sustainable HL7 FHIR based ontology for PHR data

One of the most widely acknowledged standards in health informatics is HL7 (Health Level 7 International). HL7 FHIR® (Fast Healthcare Interoperability Resources) is a new HL7 standard for exchanging electronic health data. It builds upon previous HL7 data format standards, but also leverages more modern technical concepts and approaches, aiming to be more developer-friendly. We present a developed ontology that, not only represents the domain entities of a personal health record (PHR) focusing on tele-health and integrated care, but also stores the actual data as instances of the defined ontology classes. Inspired and based on HL7 FHIR we defined a methodology for representing FHIR data types and FHIR resources in OWL and we have extended or restricted the resources to match specific domain needs. Additionally, since HL7 FHIR is a developing standard, we present a methodology for maintaining backward compatibility as the ontology is updated to match the latest definition of the standard. All the effort is represented as an OWL-DL ontology that is publicly available for reuse and extension.

219A machine learning classification algorithm to detect patients with paroxysmal atrial fibrillation during sinus rhythm

Background

Atrial fibrillation (AF) - the most common sustained cardiac arrhythmia - while not a life-threatening condition itself, leads to an increased risk of stroke and high rates of mortality. Early detection and diagnosis of AF is a critical issue for all health stakeholders.

Purpose

The aim of this study is to identify P-wave morphology patterns encountered in patients with Paroxysmal AF (PAF) and to develop a classifier discriminating PAF patients from healthy volunteers.

Methods

Three-dimensional 1000Hz ECG signals of 5 minutes duration were obtained through the use of a Galix GBI-3S Holter monitor from a total of 68 PAF patients and 52 healthy individuals. Signal pre-processing, consisting of denoising, QRS auto-detection, and ectopic beats removal was performed and a signal window of 250ms prior to the Q-wave (Pseg) was considered for every single beat. P‑wave morphology analysis based on the dynamic application of the k‑means clustering process was performed. For those Pseg that were assigned in the largest cluster, the mean P-wave was computed. The correlation of every P-wave with the mean P-wave of the main cluster was calculated. In case that it exceeded a prespecified threshold, the P-wave was allocated to the main morphology. For the remaining P‑waves, the same approach was followed once again, and the secondary morphology was extracted (picture). The P-waves of the dominant morphology were further analyzed using wavelet transform, whereas time-domain characteristics were also extracted.

A Support Vector Machine (SVM) model was created using the Gaussian Radial Basis Function kernel and the forward feature selection wrapper approach was followed. ECGs were allocated to the training, internal validation, and testing datasets in a 3:1:1 ratio.

Results

The percentage of P-waves following the main morphology in all three leads was lower in PAF patients (91.2 ±7.3%) than in healthy subjects (96.1 ±3.5%, p = 0.02). Classification between the two groups highlighted 7 features, while the SVM classifier resulted in a balanced accuracy of 91.4 ± 0.2% (sensitivity 94.2 ± 0.3%, specificity 88.6 ± 0.1%)

Conclusion

An Artificial Intelligence based ECG Classifier can efficiently identify PAF patients during normal sinus rhythm.

Abstract Figure.

244An automated beat exclusion algorithm to improve beat-to-beat P-wave morphology analysis

Background

A manually beat-to-beat P-wave analysis has previously revealed the existence of multiple P-wave morphologies in patients with paroxysmal Atrial Fibrillation (AF) while on sinus rhythm, distinguishing them from healthy, AF free patients.

Purpose

The aim of this study was to investigate the effectiveness of an Automated Beat Exclusion algorithm (ABE) that excludes noisy or ectopic beats, replacing manual beat evaluation during beat-to-beat P-wave analysis, by assessing its effect on inter-rater variability and reproducibility.

Methods

Beat-to-beat P-wave morphology analysis was performed on 34 ten-minute ECG recordings of patients with a history of AF. Each recording was analyzed independently by two clinical experts for a total of four analysis runs; once with ABE and once again with the manual exclusion of ineligible beats. The inter-rater variability and reproducibility of the analysis with and without ABE were assessed by comparing the agreement of analysis runs with respect to secondary morphology detection, primary morphology ECG template and the percentage of both, as these aspects have been previously used to discriminate PAF patients from controls.

Results

Comparing ABE to manual exclusion in detecting secondary P-wave morphologies displayed substantial (Cohen"s k = 0.69) to almost perfect (k = 0.82) agreement. Area difference among auto and manually calculated main morphology templates was in every case &lt;5% (p &lt; 0.01) and the correlation coefficient was &gt;0.99 (p &lt; 0.01). Finally, the percentages of beats classified to the primary or secondary morphology per recording by each analysis were strongly correlated, for both main and secondary P-wave morphologies, ranging from ρ=0.756 to ρ=0.940 (picture)

Conclusion

The use of the ABE algorithm does not diminish inter-rater variability and reproducibility of the analysis. The primary and secondary P-wave morphologies produced by all analyses were similar, both in terms of their template and their frequency. Based on the results of this study, the ABE algorithm incorporated in the beat-to-beat P-wave morphology analysis drastically reduces operator workload without influencing the quality of the analysis.

Abstract Figure.

P322Understanding the multiple P-wave morphologies in paroxysmal atrial fibrillation, during sinus rhythm, using computer simulation

Background

Atrial Fibrillation (AF) is the most common atrial arrhythmia. The initiation and perpetuation of AF are related to atrial remodeling affecting the electrical and structural atrial characteristics. The beat-to-beat analysis of the P-wave morphology (PWM), during sinus rhythm (SR), revealed the existence of a secondary PWM, while the proportion of the P-waves which follow the secondary morphology is higher in patients with a history of paroxysmal AF (pAF). This observation has led to the hypothesis that the multiple PWM may be the result of a transient shift in the stimulus origin, possibly within the broader anatomical region of the sinoatrial (SA) node, and it is the atrial electrical remodeling that contributes to more frequent P-waves following a secondary morphology in patients with pAF.

Purpose

To better understand the pathophysiology of AF there is a need to link different levels of analysis, in order to interpret macroscopic observations, through a surface electrocardiogram, with changes occurring at cell and tissue level. Towards this direction, computational modeling can be used as it is a non-invasive and reproducible method of analyzing the electrical activity of the heart.

Methods

The CRN atrial model was used, and a two-dimensional geometry of the atrial architecture was considered, including the major anatomical structures, like Crista Terminalis, Pectinate Muscles and Pulmonary Veins. Using existing knowledge, the CRN model was adapted to describe the ionic properties of the atrial structures as well as the electrical remodeling occurring under pAF conditions. Several scenarios were considered related to the extent of the electrical remodeled tissue and Heart Rate (HR) values. The stimulation protocol was designed as 5 stimuli originated at a specific point within the SA node area whereas the sixth stimulus originated either at the same location or 1 mm far from the previous one. The temporal variations of the atrial activation as a result of the transient shift of the sixth stimulus origin were computed.

Results

In electrically remodeled tissue, the displacement of the excitation site within the SA node resulted in a significant increase of the differences in atrial activation compared to healthy tissue, and the greater the spatial extent of the remodeling the greater the differences in the completion of the electrophysiological processes. In addition, increased HR or HR variability led to the increase of the differences especially when electrical remodeling coexists.

Conclusions

The observed differences in atrial substrate activation can explain the increased number of P-waves that match a secondary PWM in pAF patients during SR, while a future perspective is to use PWM as a marker to estimate the electrical remodeling extent in the atrial tissue. These results underline the need to link the macroscopic findings to the suspected microscopic electrical activity in order to better understand the pathophysiology of AF.

Prediction of the Adherence to a Home-Based Cardiac Rehabilitation Program

The incidence and prevalence of cardiovascular diseases (CVD) is increasing which is partly due to an increase in unhealthy lifestyles, including lack of physical activity. Therefore, following a cardiovascular event, patients are encouraged to participate in a supervised exercise-based cardiac rehabilitation (CR) program. However, uptake rates of these programs are low and compliance to adequate volumes of physical activity after the completion of such programs are even lower. An approach that has been proposed towards the increase of patient adherence to exercise, is the incorporation of technology-enabled solutions which are applied at patient's homes. However, different factors may affect patient engagement with such alternative solutions. In this work, we use diverse types of data, including baseline characteristics of the patient (i.e. physiological, behavioral, demographical data) as well as usage data of a tele-rehabilitation solution during a 4-week familiarization period, in order to predict the compliance of patients with CVD to a technology-supported physical activity intervention after completion of a supervised exercise program. Patients were clustered based on their use of a technology intervention during a previously conducted study. Following a feature selection approach, a support vector machine was trained to classify patients as adherent or non-adherent to the intervention. The performance of the classifier was assessed by means of the receiving operator curve (ROC). Bio-psycho-social baseline variables predicted adherence with a ROC of 0.86, but adding usage data of the platform during a 4-week familiarization period increased the ROC up to 0.94. Furthermore, the high sensitivity values (83.8% and 95.5% respectively) support the strength of the models to identify those patients with CVD that will be adherent to a technology-enabled, home-based CR program.

Nutrition Adherence in Critically Ill Patients; How is nutritional intake within the 1st week of hospitalization affecting the patient's Outcome?

Nutritional requirements vary during a patient's stay in the Intensive Care Unit (ICU) and their calculation can be relatively complex. During ICU stay nutrition requirements are rarely met, especially during the initial days of the hospitalization. Studies have shown that poor nutrition is associated with adverse patient outcome. This study examines for correlation between poor nutrition (calories, proteins, lipids and micronutrients) during the 1^st week of ICU stay and adverse patient outcome. Nutritional adherence effect is examined on groups of patients, such as patients with high BMI that receive low nutrition and critically ill males. Regarding the latter analysis, an accuracy rate of 76.4% was achieved when classifying the critically ill males towards their outcome. The results of this work could contribute to the development of smart alarms in the ICU.

Developing a strategic understanding of telehealth service adoption for COPD care management: A causal loop analysis of healthcare professionals

BACKGROUND

Telehealth services can improve the quality of health services for chronic obstructive pulmonary disease (COPD) management, but the clinical benefits for patients yet not clear. It is crucial to develop a strategy that supports the engagement of healthcare professionals to promote the sustainable adoption of telehealth services further. The aim of the study was to show how variables related to the perception of telehealth services for COPD by different healthcare professionals interact to influence its adoption and to generate advice for future telehealth service implementation.

METHODS

Data was thematically synthesized from published qualitative studies to create causal loop diagrams, further validated by expert interviews. These diagrams visualize dependencies and their polarity between different variables.

RESULTS

Adoption of telehealth services from the nurse's perspective is directly affected by change management and autonomous decision making. From the physician's perspective, perceived value is the most important variable. Physical activity management and positive user experience are considered affecting perceived value for physiotherapists. There is no consensus where self-management services should be positioned in the COPD care pathway.

CONCLUSION

Our results indicate how complex interactions between multiple variables influence the adoption of telehealth services. Consequently, there is a need for multidimensional interventions to achieve adoption. Moreover, key variables were identified that require attention to ensure success of telehealth services. Furthermore, it is necessary to explore where self-management services are best positioned in the care pathway of COPD patients.

Developing a Smartphone Application to Support Smoking Behavior Change through Social Comparison

The growing field of mHealth has often dealt with the modification of harmful behaviors, such as smoking, that are associated with medical conditions. Smoking behavior has been targeted by numerous mHealth smoking cessation interventions with the help of a wide range of behavior change support (BCS) techniques. However, the exploitation of the established BCS technique of social comparison by mHealth research on smoking cessation has been limited. Based on up-to-date BCS theory and following a user-centered design, we have developed a novel smartphone application, namely QuitIT!, for smoking behavior modification with the help of social comparison. This paper presents the development of QuitIT! as well as its preliminary evaluation through a small pilot study. The latter has yield encouraging initial results concerning the feasibility and the effectiveness of QuitIT! as an mHealth tool for smoking BCS.

Leveraging Interdisciplinary Education Toward Securing the Future of Connected Health Research in Europe: Qualitative Study

BACKGROUND

Connected health (CH) technologies have resulted in a paradigm shift, moving health care steadily toward a more patient-centered delivery approach. CH requires a broad range of disciplinary expertise from across the spectrum to work in a cohesive and productive way. Building this interdisciplinary relationship at an earlier stage of career development may nurture and accelerate the CH developments and innovations required for future health care.

OBJECTIVE

This study aimed to explore the perceptions of interdisciplinary CH researchers regarding the design and delivery of an interdisciplinary education (IDE) module for disciplines currently engaged in CH research (engineers, computer scientists, health care practitioners, and policy makers). This study also investigated whether this module should be delivered as a taught component of an undergraduate, master's, or doctoral program to facilitate the development of interdisciplinary learning.

METHODS

A qualitative, cross-institutional, multistage research approach was adopted, which involved a background study of fundamental concepts, individual interviews with CH researchers in Greece (n=9), and two structured group feedback sessions with CH researchers in Ireland (n=10/16). Thematic analysis was used to identify the themes emerging from the interviews and structured group feedback sessions.

RESULTS

A total of two sets of findings emerged from the data. In the first instance, challenges to interdisciplinary work were identified, including communication challenges, divergent awareness of state-of-the-art CH technologies across disciplines, and cultural resistance to interdisciplinarity. The second set of findings were related to the design for interdisciplinarity. In this regard, the need to link research and education with real-world practice emerged as a key design concern. Positioning within the program context was also considered to be important with a need to balance early intervention to embed integration with later repeat interventions that maximize opportunities to share skills and experiences.

CONCLUSIONS

The authors raise and address challenges to interdisciplinary program design for CH based on an abductive approach combining interdisciplinary and interprofessional education literature and the collection of qualitative data. This recipe approach for interdisciplinary design offers guidelines for policy makers, educators, and innovators in the CH space. Gaining insight from CH researchers regarding the development of an IDE module has offered the designers a novel insight regarding the curriculum, timing, delivery, and potential challenges that may be encountered.

Smart alarms towards optimizing patient ventilation in intensive care: the driving pressure case

OBJECTIVE

Alarms are a substantial part of clinical practice, warning clinicians of patient complications. In this paper, we focus on alarms in the intensive care unit and especially on the use of machine learning techniques for the creation of alarms for the ventilator support of patients. The aim is to study a method to enable timely interventions for intubated patients and prevent complications induced by high driving pressure (ΔP) and lung strain during mechanical ventilation.

APPROACH

The relation between the ΔP and the total set of the ventilator parameters was examined and resulted in a predictive model with bimodal implementation for the short-term prediction of the ΔP level (high/low). The proposed method includes two sub-models for the prediction of future ΔP level based on the current level being high or low, named cH and cL, respectively. Based on this method, for both sub-models, an alarm will be triggered when the predicted ΔP level is considered to be high. In this vein, three classifiers (the random forest, linear support vector machine, and kernel support vector machine methods) were tested for each sub-model. To adjust the highly unbalanced classes, four different sampling methods were considered: downsampling, upsampling, synthetic minority over-sampling technique (SMOTE) sampling, and random over-sampling examples (ROSE) sampling.

MAIN RESULTS

For the cL sub-model the combination of linear support vector machine with SMOTE sampling showed the best performance, resulting in accuracy of 93%, while the cH sub-model reached the best performance, with accuracy of 73%, with kernel support vector machine combined with the downsampling method.

SIGNIFICANCE

The results are positive in terms of the generation of new alarms in mechanical ventilation. The technical and organizational possibility of integrating data from multiple modalities is expected to further advance this line of work.

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background

This study examines the development of the connected health (CH) research landscape with a view to providing an overview of the existing CH research. The research field of CH has experienced rapid growth coinciding with increasing pressure on health care systems to become more proactive and patient centered.

Objective

This study aimed to assess the extent and coverage of the current body of knowledge in CH. In doing so, we sought to identify specific topics that have drawn the attention of CH researchers and to identify research gaps, in particular those offering opportunities for further interdisciplinary research.

Methods

A systematic mapping study that combined scientific contributions from research in the disciplines of medicine, business, computer science, and engineering was used. Overall, seven classification criteria were used to analyze the papers, including publication source, publication year, research type, empirical type, contribution type, research topic, and the medical condition studied.

Results

The search resulted in 208 papers that were analyzed by a multidisciplinary group of researchers. The results indicated a slow start for CH research but showed a more recent steady upswing since 2013. The majority of papers proposed health care solutions (77/208, 37.0%) or evaluated CH approaches (49/208, 23.5%). Case studies (59/208, 28.3%) and experiments (55/208, 26.4%) were the most popular forms of scientific validation used. Diabetes, cancer, multiple sclerosis, and heart conditions were among the most prevalent medical conditions studied.

Conclusions

We conclude that CH research has become an established field of research that has grown over the last five years. The results of this study indicate a focus on technology-driven research with a strong contribution from medicine, whereas the business aspects of CH have received less research attention.

A systematic map and in-depth review of European telehealth interventions efficacy for chronic obstructive pulmonary disease

BACKGROUND

Evidence to support the implementation of telehealth (TH) interventions in the management of chronic obstructive pulmonary disease (COPD) varies throughout Europe. Despite more than ten years of TH research in COPD management, it is still not possible to define which TH interventions are beneficial to which patient group. Therefore, informing policymakers on TH implementation is complicated. We aimed to examine the provision and efficacy of TH for COPD management to guide future decision-making.

METHODS

A mapping study of twelve systematic reviews of TH interventions for COPD management was conducted. This was followed by an in-depth review of fourteen clinical trials performed in Europe extracted from the systematic reviews. Efficacy outcomes for COPD management were synthesized.

RESULTS

The mapping study revealed that systematic reviews with a meta-analysis often report positive clinical outcomes. Despite this, we identified a lack of pragmatic trial design affecting the synthesis of reported outcomes. The in-depth review visualized outcomes for three TH categories, which revealed a plethora of heterogeneous outcomes. Suggestions for reporting within these three outcomes are synthesized as targets for future empirical research reporting.

CONCLUSION

The present study indicates the need for more standardized and updated systematic reviews. Policymakers should advocate for improved TH trial designs, focusing on the entire intervention's adoption process evaluation. One of the policymakers' priorities should be the harmonization of the outcome sets, which would be considered suitable for deciding about subsequent reimbursement. We propose possible outcome sets in three TH categories which could be used for discussion with stakeholders.

Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers

Background

Wearable sensing and information and communication technologies are key enablers driving the transformation of health care delivery toward a new model of connected health (CH) care. The advances in wearable technologies in the last decade are evidenced in a plethora of original articles, patent documentation, and focused systematic reviews. Although technological innovations continuously respond to emerging challenges and technology availability further supports the evolution of CH solutions, the widespread adoption of wearables remains hindered.

Objective

This study aimed to scope the scientific literature in the field of pervasive wearable health monitoring in the time interval from January 2010 to February 2019 with respect to four important pillars: technology, safety and security, prescriptive insight, and user-related concerns. The purpose of this study was multifold: identification of (1) trends and milestones that have driven research in wearable technology in the last decade, (2) concerns and barriers from technology and user perspective, and (3) trends in the research literature addressing these issues.

Methods

This study followed the scoping review methodology to identify and process the available literature. As the scope surpasses the possibilities of manual search, we relied on the natural language processing tool kit to ensure an efficient and exhaustive search of the literature corpus in three large digital libraries: Institute of Electrical and Electronics Engineers, PubMed, and Springer. The search was based on the keywords and properties to be found in articles using the search engines of the digital libraries.

Results

The annual number of publications in all segments of research on wearable technology shows an increasing trend from 2010 to February 2019. The technology-related topics dominated in the number of contributions, followed by research on information delivery, safety, and security, whereas user-related concerns were the topic least addressed. The literature corpus evidences milestones in sensor technology (miniaturization and placement), communication architectures and fifth generation (5G) cellular network technology, data analytics, and evolution of cloud and edge computing architectures. The research lag in battery technology makes energy efficiency a relevant consideration in the design of both sensors and network architectures with computational offloading. The most addressed user-related concerns were (technology) acceptance and privacy, whereas research gaps indicate that more efforts should be invested into formalizing clear use cases with timely and valuable feedback and prescriptive recommendations.

Conclusions

This study confirms that applications of wearable technology in the CH domain are becoming mature and established as a scientific domain. The current research should bring progress to sustainable delivery of valuable recommendations, enforcement of privacy by design, energy-efficient pervasive sensing, seamless monitoring, and low-latency 5G communications. To complement technology achievements, future work involving all stakeholders providing research evidence on improved care pathways and cost-effectiveness of the CH model is needed.

Connected Health Services: Framework for an Impact Assessment

Background

Connected health (CH), as a new paradigm, manages individual and community health in a holistic manner by leveraging a variety of technologies and has the potential for the incorporation of telehealth and integrated care services, covering the whole spectrum of health-related services addressing healthy subjects and chronic patients. The reorganization of services around the person or citizen has been expected to bring high impact in the health care domain. There are a series of concerns (eg, contextual factors influencing the impact of care models, the cost savings associated with CH solutions, and the sustainability of the CH ecosystem) that should be better addressed for CH technologies to reach stakeholders more successfully. Overall, there is a need to effectively establish an understanding of the concepts of CH impact. As services based on CH technologies go beyond standard clinical interventions and assessments of medical devices or medical treatments, the need for standardization and for new ways of measurements and assessments emerges when studying CH impact.

Objective

This study aimed to introduce the CH impact framework (CHIF) that serves as an approach to assess the impact of CH services.

Methods

This study focused on the subset of CH comprising services that directly address patients and citizens on the management of disease or health and wellness. The CHIF was developed through a multistep procedure and various activities. These included, as initial steps, a literature review and workshop focusing on knowledge elicitation around CH concepts. Then followed the development of the initial version of the framework, refining of the framework with the experts as a result of the second workshop, and, finally, composition and deployment of a questionnaire for preliminary feedback from early-stage researchers in the relevant domains.

Results

The framework contributes to a better understanding of what is CH impact and analyzes the factors toward achieving it. CHIF elaborates on how to assess impact in CH services. These aspects can contribute to an impact-aware design of CH services. It can also contribute to a comparison of CH services and further knowledge of the domain. The CHIF is based on 4 concepts, including CH system and service outline, CH system end users, CH outcomes, and factors toward achieving CH impact. The framework is visualized as an ontological model.

Conclusions

The CHIF is an initial step toward identifying methodologies to objectively measure CH impact while recognizing its multiple dimensions and scales.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

Supporting integrated care with a flexible data management framework built upon Linked Data, HL7 FHIR and ontologies

In this paper we present the methodology and decisions behind an implementation of a telehealth data management framework, aiming to support integrated care services for chronic and multimorbid patients. The framework leverages an OWL ontology, built upon HL7 FHIR resources, to provide storage and representation of semantically enriched EHR data following Linked Data principles. This is presented along with the realization of the persistent storage solution and communication web services that allow the management of EHR data, ensuring the validity and integrity of the exchanged patient data as self-describing ontology instances. The framework concentrates on flexibility and reusability, which is addressed by regarding the aforementioned ontology as a single point of change. This solution has been implemented in the scope of the EU project WELCOME for managing data in a telemonitoring system for patients with COPD and co-morbidities and was also successfully deployed for the INLIFE EU project with minimal effort. The results of the two applications suggest it can be adopted and properly adapted in a series of integrated care scenarios with minimal effort.

"OPTImAL": an ontology for patient adherence modeling in physical activity domain

BACKGROUND

Maintaining physical fitness is a crucial component of the therapeutic process for patients with cardiovascular disease (CVD). Despite the known importance of being physically active, patient adherence to exercise, both in daily life and during cardiac rehabilitation (CR), is low. Patient adherence is frequently composed of numerous determinants associated with different patient aspects (e.g., psychological, clinical, etc.). Understanding the influence of such determinants is a central component of developing personalized interventions to improve or maintain patient adherence. Medical research produced evidence regarding factors affecting patients' adherence to physical activity regimen. However, the heterogeneity of the available data is a significant challenge for knowledge reusability. Ontologies constitute one of the methods applied for efficient knowledge sharing and reuse. In this paper, we are proposing an ontology called OPTImAL, focusing on CVD patient adherence to physical activity and exercise training.

METHODS

OPTImAL was developed following the Ontology Development 101 methodology and refined based on the NeOn framework. First, we defined the ontology specification (i.e., purpose, scope, target users, etc.). Then, we elicited domain knowledge based on the published studies. Further, the model was conceptualized, formalized and implemented, while the developed ontology was validated for its consistency. An independent cardiologist and three CR trainers evaluated the ontology for its appropriateness and usefulness.

RESULTS

We developed a formal model that includes 142 classes, ten object properties, and 371 individuals, that describes the relations of different factors of CVD patient profile to adherence and adherence quality, as well as the associated types and dimensions of physical activity and exercise. 2637 logical axioms were constructed to comprise the overall concepts that the ontology defines. The ontology was successfully validated for its consistency and preliminary evaluated for its appropriateness and usefulness in medical practice.

CONCLUSIONS

OPTImAL describes relations of 320 factors originated from 60 multidimensional aspects (e.g., social, clinical, psychological, etc.) affecting CVD patient adherence to physical activity and exercise. The formal model is evidence-based and can serve as a knowledge tool in the practice of cardiac rehabilitation experts, supporting the process of activity regimen recommendation for better patient adherence.

An open access database for the evaluation of respiratory sound classification algorithms

OBJECTIVE

Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases.

APPROACH

This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated.

MAIN RESULTS

The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations.

SIGNIFICANCE

The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.

The development and codesign of the PATHway intervention: a theory-driven eHealth platform for the self-management of cardiovascular disease

Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.

Driving pressure during proportional assist ventilation: an observational study

Background

During passive mechanical ventilation, the driving pressure of the respiratory system is an important mediator of ventilator-induced lung injury. Monitoring of driving pressure during assisted ventilation, similar to controlled ventilation, could be a tool to identify patients at risk of ventilator-induced lung injury. The aim of this study was to describe driving pressure over time and to identify whether and when high driving pressure occurs in critically ill patients during assisted ventilation.

Methods

Sixty-two patients fulfilling criteria for assisted ventilation were prospectively studied. Patients were included when the treating physician selected proportional assist ventilation (PAV+), a mode that estimates respiratory system compliance. In these patients, continuous recordings of all ventilator parameters were obtained for up to 72 h. Driving pressure was calculated as tidal volume-to-respiratory system compliance ratio. The distribution of driving pressure and tidal volume values over time was examined, and periods of sustained high driving pressure (≥ 15 cmH₂O) and of stable compliance were identified and analyzed.

Results

The analysis included 3200 h of ventilation, consisting of 8.8 million samples. For most (95%) of the time, driving pressure was < 15 cmH₂O and tidal volume < 11 mL/kg (of ideal body weight). In most patients, high driving pressure was observed for short periods of time (median 2.5 min). Prolonged periods of high driving pressure were observed in five patients (8%). During the 661 periods of stable compliance, high driving pressure combined with a tidal volume ≥ 8 mL/kg was observed only in 11 cases (1.6%) pertaining to four patients. High driving pressure occurred almost exclusively when respiratory system compliance was low, and compliance above 30 mL/cmH₂O excluded the presence of high driving pressure with 90% sensitivity and specificity.

Conclusions

In critically ill patients fulfilling criteria for assisted ventilation, and ventilated in PAV+ mode, sustained high driving pressure occurred in a small, yet not negligible number of patients. The presence of sustained high driving pressure was not associated with high tidal volume, but occurred almost exclusively when compliance was below 30 mL/cmH₂O.

Electronic supplementary material

The online version of this article (10.1186/s13613-018-0477-4) contains supplementary material, which is available to authorized users.

Exploring the impact of sleep and stress on daily physical activity of cardiac patients: a preliminary study

BACKGROUND

Current approaches to cardiac rehabilitation services tailoring are often based on patient demographics or readiness for behavior change. However, the success of interventions acceptance and improved adherence to recommendations could be much higher when considering and adapting to a patient's lifestyle, such as sleep and stress.

AIMS

We aimed to analyze the potential associations between patient sleep and stress and daily moderate-intensity activity in patients with cardiovascular disease and to gain experience on the methods to collect and analyze a combination of qualitative and quantitative data.

METHODS

Patients with cardiovascular disease enrolled for an outpatient cardiac rehabilitation program were assessed at the study baseline regarding sociodemographic, clinical profile, and perceived level of stress. To collect daily physical activity and sleep data, all participants had two-week long diaries. Collected data was analyzed through correlation analysis, linear regression, and one-way ANOVA analysis.

RESULTS

The mean age of the participants (n =11) was 67.3 ± 9.6 years old. The patients were mainly male (82 %), married (91 %), and having at least one comorbid disease (64 %). The results of the analysis revealed that the night sleep duration is associated with moderate-intensity physical activity [F(1,6) =7.417, p =0.034]. Stress was not associated with patients' moderate-intensity daily physical activity.

CONCLUSION

The outcomes of the study can support the development of e-health and home-based interventions design and strategies to promote adherence to physical activity. Tailoring an intervention to a daily behavioral pattern of a patient, such as sleep, can support the planning of the physical activity in a form to be easier accepted by the patient. This finding emphasizes the need for further investigation of the association with a larger population sample and the use of objective physical activity and sleep-related measure instruments. HIPPOKRATIA 2019, 23(1): 15-20.

Telehealth for patients with chronic obstructive pulmonary disease (COPD): a systematic review and meta-analysis protocol

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is a highly prevalent chronic disease characterised by persistent respiratory symptoms. A focus of COPD interventional studies is directed towards prevention of exacerbations leading to hospital readmissions. Telehealth as a method of remote patient monitoring and care delivery may be implemented to reduce hospital readmissions and improve self-management of disease. Prior reviews have not systematically assessed the efficacies of various telehealth functionalities in patients with COPD at different stages of disease severity. We aim to evaluate which COPD telehealth interventions, classified by their functionalities, are most effective in improving patient with COPD management measured by both clinical and resource utilisation outcomes.

METHODS AND ANALYSIS

We will conduct a systematic review which will include randomised controlled trials comparing the efficacy of telehealth interventions versus standard care in patients with COPD with confirmed disease severity based on forced expiratory volume(%) levels. An electronic search strategy will be used to identify trials published since 2000 in MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, CINHAL. Telehealth is described as remote monitoring and delivery of care where patient data/clinical information is routinely or continuously collected and/or processed, presented to the patient and transferred to a clinical care institution for feedback, triage and intervention by a clinical specialist. Two authors will independently screen articles for inclusion, assess risk of bias and extract data. We will merge studies into a meta-analysis if the interventions, technologies, participants and underlying clinical questions are homogeneous enough. We will use a random-effects model, as we expect some heterogeneity between interventions. In cases where a meta-analysis is not possible, we will synthesise findings narratively. We will assess the quality of the evidence for the main outcomes using GRADE.

ETHICS AND DISSEMINATION

Research ethics approval is not required. The findings will be disseminated through publication in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42018083671.

Computerized decision support for beneficial home-based exercise rehabilitation in patients with cardiovascular disease

BACKGROUND

Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes.

OBJECTIVES

We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs.

METHODS

The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems.

RESULTS

The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance.

CONCLUSIONS

Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.

Association of global and local low endothelial shear stress with high-risk plaque using intracoronary 3D optical coherence tomography: Introduction of 'shear stress score'

Aims

The association of low endothelial shear stress (ESS) with high-risk plaque (HRP) has not been thoroughly investigated in humans. We investigated the local ESS and lumen remodelling patterns in HRPs using optical coherence tomography (OCT), developed the shear stress score, and explored its association with the prevalence of HRPs and clinical outcomes.

Methods and results

A total of 35 coronary arteries from 30 patients with stable angina or acute coronary syndrome (ACS) were reconstructed with three dimensional (3D) OCT. ESS was calculated using computational fluid dynamics and classified into low, moderate, and high in 3-mm-long subsegments. In each subsegment, (i) fibroatheromas (FAs) were classified into HRPs and non-HRPs based on fibrous cap (FC) thickness and lipid pool size, and (ii) lumen remodelling was classified into constrictive, compensatory, and expansive. In each artery the shear stress score was calculated as metric of the extent and severity of low ESS. FAs in low ESS subsegments had thinner FC compared with high ESS (89 ± 84 vs.138 ± 83 µm, P < 0.05). Low ESS subsegments predominantly co-localized with HRPs vs. non-HRPs (29 vs. 9%, P < 0.05) and high ESS subsegments predominantly with non-HRPs (9 vs. 24%, P < 0.05). Compensatory and expansive lumen remodelling were the predominant responses within subsegments with low ESS and HRPs. In non-stenotic FAs, low ESS was associated with HRPs vs. non-HRPs (29 vs. 3%, P < 0.05). Arteries with increased shear stress score had increased frequency of HRPs and were associated with ACS vs. stable angina.

Conclusion

Local low ESS and expansive lumen remodelling are associated with HRP. Arteries with increased shear stress score have increased frequency of HRPs and propensity to present with ACS.