Validation of an early vascular aging construct model for comprehensive cardiovascular risk assessment using external risk indicators for improved clinical utility: data from the EVasCu study.

BACKGROUND: Cardiovascular diseases (CVDs) remain a major global health concern, necessitating advanced risk assessment beyond traditional factors. Early vascular aging (EVA), characterized by accelerated vascular changes, has gained importance in cardiovascular risk assessment. METHODS: The EVasCu study in Spain examined 390 healthy participants using noninvasive measurements. A construct of four variables (Pulse Pressure, Pulse Wave Velocity, Glycated Hemoglobin, Advanced Glycation End Products) was used for clustering. K-means clustering with principal component analysis revealed two clusters, healthy vascular aging (HVA) and early vascular aging (EVA). External validation variables included sociodemographic, adiposity, glycemic, inflammatory, lipid profile, vascular, and blood pressure factors. RESULTS: EVA cluster participants were older and exhibited higher adiposity, poorer glycemic control, dyslipidemia, altered vascular properties, and higher blood pressure. Significant differences were observed for age, smoking status, body mass index, waist circumference, fat percentage, glucose, insulin, C-reactive protein, diabetes prevalence, lipid profiles, arterial stiffness, and blood pressure levels. These findings demonstrate the association between traditional cardiovascular risk factors and EVA. CONCLUSIONS: This study validates a clustering model for EVA and highlights its association with established risk factors. EVA assessment can be integrated into clinical practice, allowing early intervention and personalized cardiovascular risk management.

Comparative effect of oral drugs in improving spasticity of different etiology: a network meta-analysis.

INTRODUCTION: Spasticity, a prevalent manifestation of various neurological conditions, significantly impacts the quality of life of patients. Research on the effects of oral drugs on spasticity has produced controversial results. Thus, the aim of this network meta-analysis was to compare the efficacy of oral drugs for improving spasticity in patients with different etiologies. METHODS: We searched four different databases from their inception to 30 November 2023. A network meta-analysis using a frequentist perspective was conducted to assess the effects of different oral drugs on spasticity, evaluated by the modified Ashworth scale. RESULTS: Our findings showed that, in a frequentist network meta-analysis, eperisone, diazepam, and baclofen had significantly greater spasticity, as measured by the modified Ashworth scale, than did the placebo (MD: -0.80; 95% CIs: -1.42, -0.18; MD: -0.68; 95% CIs: -1.28, -0.09; MD: -0.58; 95% CIs: -1.11, -0.06, respectively). CONCLUSION: In summary, our study confirmed that eperisone, diazepam, and baclofen could be effective approaches for reducing spasticity of different etiologies and could be useful approaches for improving patient quality of life. Key messages What is already known on this topic: The impact of oral drugs, such as baclofen, gabapentin, tizanidine, and dantrolene, in the treatment of spasticity has been documented. What this study adds: This study determines which of the oral drugs aimed at treating spasticity is the most effective across different etiologies. How this study might affect research, practice, or policy: This study suggests tailored treatment strategies for spasticity based on its etiology.

Early vascular aging as an index of cardiovascular risk in healthy adults: confirmatory factor analysis from the EVasCu study.

BACKGROUND: The concept of early vascular aging (EVA) represents a potentially beneficial model for future research into the pathophysiological mechanisms underlying the early manifestations of cardiovascular disease. For this reason, the aims of this study were to verify by confirmatory factor analysis the concept of EVA on a single factor based on vascular, clinical and biochemical parameters in a healthy adult population and to develop a statistical model to estimate the EVA index from variables collected in a dataset to classify patients into different cardiovascular risk groups: healthy vascular aging (HVA) and EVA. METHODS: The EVasCu study, a cross-sectional study, was based on data obtained from 390 healthy adults. To examine the construct validity of a single-factor model to measure accelerated vascular aging, different models including vascular, clinical and biochemical parameters were examined. In addition, unsupervised clustering techniques (using both K-means and hierarchical methods) were used to identify groups of patients sharing similar characteristics in terms of the analysed variables to classify patients into different cardiovascular risk groups: HVA and EVA. RESULTS: Our data show that a single-factor model including pulse pressure, glycated hemoglobin A1c, pulse wave velocity and advanced glycation end products shows the best construct validity for the EVA index. The optimal value of the risk groups to separate patients is K = 2 (HVA and EVA). CONCLUSIONS: The EVA index proved to be an adequate model to classify patients into different cardiovascular risk groups, which could be valuable in guiding future preventive and therapeutic interventions.

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain's behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information.

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices.

Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.

Electrodermal Activity Sensor for Classification of Calm/Distress Condition.

This article introduces a new and unobtrusive wearable monitoring device based on electrodermal activity (EDA) to be used in health-related computing systems. This paper introduces the description of the wearable device capable of acquiring the EDA of a subject in order to detect his/her calm/distress condition from the acquired physiological signals. The lightweight wearable device is placed in the wrist of the subject to allow continuous physiological measurements. With the aim of validating the correct operation of the wearable EDA device, pictures from the International Affective Picture System are used in a control experiment involving fifty participants. The collected signals are processed, features are extracted and a statistical analysis is performed on the calm/distress condition classification. The results show that the wearable device solely based on EDA signal processing reports around 89% accuracy when distinguishing calm condition from distress condition.

Smart environment architecture for emotion detection and regulation.

This paper introduces an architecture as a proof-of-concept for emotion detection and regulation in smart health environments. The aim of the proposal is to detect the patient's emotional state by analysing his/her physiological signals, facial expression and behaviour. Then, the system provides the best-tailored actions in the environment to regulate these emotions towards a positive mood when possible. The current state-of-the-art in emotion regulation through music and colour/light is implemented with the final goal of enhancing the quality of life and care of the subject. The paper describes the three main parts of the architecture, namely "Emotion Detection", "Emotion Regulation" and "Emotion Feedback Control". "Emotion Detection" works with the data captured from the patient, whereas "Emotion Regulation" offers him/her different musical pieces and colour/light settings. "Emotion Feedback Control" performs as a feedback control loop to assess the effect of emotion regulation over emotion detection. We are currently testing the overall architecture and the intervention in real environments to achieve our final goal.

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.

Purpose: Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health. Methods: This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions. Results: The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention. Conclusion: This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.

Effectiveness of Extracorporeal Shock Wave Therapy in Treatment of Spasticity of Different Aetiologies: A Systematic Review and Meta-Analysis.

Background: Spasticity is a motor disorder characterised by exaggerated movements of the tendons and accompanied by hyperreflexia and hypertonia. Extracorporeal shock wave therapy (ESWT) is used as a treatment for spasticity, although more evidence is needed on the effectiveness of this therapy in the treatment of spasticity. Therefore, the aim of this study was to assess the effectiveness ESWT in the treatment of upper and lower limbs spasticity in both children and adults through different aetiologies. Methods: A systematic search was performed in different databases from inception to December 2023. Random-effects meta-analysis was used to estimate the efficacy of ESWT on spasticity using the Modified Ashworth Scale. Results: Sixteen studies were included in the systematic review and meta-analysis. The effect of ESWT on spasticity measured with the Modified Ashworth Scale shows a significant decrease in spasticity in the upper limbs and in the lower limbs in adults with chronic stroke and in children with cerebral palsy, is more effective immediately after application, and maintains its effect up to 12 weeks post treatment. Conclusions: These findings are important for clinical practice since they show evidence that ESWT is effective in reducing spasticity in both children and adults.

Effect of Botulinum Toxin Injections in the Treatment of Spasticity of Different Etiologies: An Umbrella Review.

BACKGROUND: Spasticity is a very common neurological sequelae that significantly impacts the quality of life of patients, affecting more than 12 million people worldwide. Botulinum toxin is considered a reversible treatment for spasticity, but due to the large amount of available evidence, synthesis seems necessary. Therefore, we conducted an overview of existing systematic reviews and meta-analyses to evaluate the effect of botulinum toxin injections in the treatment of spasticity of different etiologies. METHODS: A systematic search of different databases, including Pubmed, Scopus, the Cochrane Library, and Web of Science, was performed from inception to February 2024. Standardized mean differences (SMDs) and their respective 95% confidence intervals (CIs) were calculated to assess the effect of botulinum toxin compared to that of the control treatment using the Modified Ashworth Scale (MAS). All the statistical analyses were performed using STATA 15 software. RESULTS: 28 studies were included in the umbrella review. The effect of botulinum toxin injections on spasticity, as measured by the MAS, was significantly lower in all but three studies, although these studies also supported the intervention. The SMDs reported by the meta-analyses ranged from -0.98 to -0.01. CONCLUSION: Botulinum toxin injections were effective at treating spasticity of different etiologies, as indicated by the measurements on the MAS. This implies an improvement in muscle tone and, consequently, in the patient's mobility and quality of life.