1,721,065 research outputs found
Heart rate variability and target organ damage in hypertensive patients
Full text linkBackground:
We evaluated the association between linear standard Heart Rate Variability (HRV) measures and vascular, renal and cardiac target organ damage (TOD).
Methods:
A retrospective analysis was performed including 200 patients registered in the Regione Campania network (aged 62.4 ± 12, male 64%). HRV analysis was performed by 24-h holter ECG. Renal damage was assessed by estimated glomerular filtration rate (eGFR), vascular damage by carotid intima-media thickness (IMT), and cardiac damage by left ventricular mass index.
Results:
Significantly lower values of the ratio of low to high frequency power (LF/HF) were found in the patients with moderate or severe eGFR (p-value < 0.001). Similarly, depressed values of indexes of the overall autonomic modulation on heart were found in patients with plaque compared to those with a normal IMT (p-value <0.05). These associations remained significant after adjustment for other factors known to contribute to the development of target organ damage, such as age. Moreover, depressed LF/HF was found also in patients with left ventricular hypertrophy but this association was not significant after adjustment for other factors.
Conclusions:
Depressed HRV appeared to be associated with vascular and renal TOD, suggesting the involvement of autonomic imbalance in the TOD. However, as the mechanisms by which abnormal autonomic balance may lead to TOD, and, particularly, to renal organ damage are not clearly known, further prospective studies with longitudinal design are needed to determine the association between HRV and the development of TOD
Pupillometric analysis for assessment of gene therapy in Leber Congenital Amaurosis patients
Full text linkBackground:
Objective techniques to assess the amelioration of vision in patients with impaired visual function are needed to standardize efficacy assessment in gene therapy trials for ocular diseases. Pupillometry has been investigated in several diseases in order to provide objective information about the visual reflex pathway and has been adopted to quantify visual impairment in patients with Leber Congenital Amaurosis (LCA). In this paper, we describe detailed methods of pupillometric analysis and a case study on three Italian patients affected by Leber Congenital Amaurosis (LCA) involved in a gene therapy clinical trial at two follow-up time-points: 1 year and 3 years after therapy administration.
Methods:
Pupillary light reflexes (PLR) were measured in patients who had received a unilateral subretinal injection in a clinical gene therapy trial. Pupil images were recorded simultaneously in both eyes with a commercial pupillometer and related software. A program was generated with MATLAB software in order to enable enhanced pupil detection with revision of the acquired images (correcting aberrations due to the inability of these severely visually impaired patients to fixate), and computation of the pupillometric parameters for each stimulus. Pupil detection was performed through Hough Transform and a non-parametric paired statistical test was adopted for comparison.
Results:
The developed program provided correct pupil detection also for frames in which the pupil is not totally visible. Moreover, it provided an automatic computation of the pupillometric parameters for each stimulus and enabled semi-automatic revision of computerized detection, eliminating the need for the user to manually check frame by frame. With reference to the case study, the amplitude of pupillary constriction and the constriction velocity were increased in the right (treated eye) compared to the left (untreated) eye at both follow-up time-points, showing stability of the improved PLR in the treated eye.
Conclusions:
Our method streamlined the pupillometric analyses and allowed rapid statistical analysis of a range of parameters associated with PLR. The results confirm that pupillometry is a useful objective measure for the assessment of therapeutic effect of gene therapy in patients with LCA
Bioelectronic technologies and artificial intelligence for medical diagnosis and healthcare
Full text linkThe application of electronic findings to biology and medicine has significantly impacted health and wellbeing. Recent technology advances have allowed the development of new systems that can provide diagnostic information on portable point-of-devices or smartphones. The decreasing size of electronics technologies down to the atomic scale and the advances in system, cell, and molecular biology have the potential to increase the quality and reduce the costs of healthcare.
Clinicians have pervasive access to new data from complex sensors; imaging tools; and a multitude of other sources, including personal health e-records and smart environments. Humans are from being able to process this unprecedented volume of available data without advanced tools. Artificial intelligence (AI) can help clinicians to identify patterns from this huge amount of data to inform better choices for patients.
In this Special Issue, some original research papers focusing on recent advances have been collected, covering novel theories, innovative methods, and meaningful applications that could potentially lead to significant advances in the field
Frontiers in hemodialysis part II: Toward personalized and optimized therapy
No full textThis article describes new protocols for intermittent hemodialysis, based on a mathematical model for bicarbonate restoring, which ameliorate drawbacks of the standard therapy and allow the nephrologist to design any desired profile of bicarbonate concentration in the patient blood, enabling a safer and more effective treatment. The proposed protocols can be tailored to patient-specific clinical prescriptions, opening the way to highly personalized hemodialysis treatments. This article describes a simpler and more insightful solutions to the same mathematical model considered in previous works, with more straightforward system-theory interpretation. This is relevant as it leads to self-explaining evidences of the underlying physiological mechanisms, which is crucial in order to communicate the model potential to clinicians, not necessarily skilled in advanced math modeling
User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner
Full text linkBackground:
The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital.
Methods:
AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital.
Results:
Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution.
Conclusions:
AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department
A convolutional neural network approach to detect congestive heart failure
No full textCongestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG's morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection
A 3D-printed condom intrauterine balloon tamponade: Design, prototyping, and technical validation
Full text linkPost-partum haemorrhage is among the main causes of (preventable) mortality for women in low-resource settings (LRSs), where, in 2017, the mortality ratio was 462 out of every 100 000 live births, over 10 times higher than for high-resource settings. There are different treatments available for post-partum haemorrhage. The intrauterine balloon tamponade is a medical device that proved to be a simple and cost-effective approach. Currently, there are several balloon tamponades available, with different design and working principles. However, all these devices were designed for high-resource settings, presenting several aspects that could be inappropriate for many lower-income countries. This paper presents the results of a preclinical study aiming at informing the design, prototyping and validation of a 3D-printed intrauterine balloon tamponade concept, contributing towards the United Nation's Sustainable Development Goal 3: Good health and Well-being. Frugal engineering concepts and contextualised design techniques were applied throughout, to define the design requirements and specifications. The performance of the final prototype was validated against the requirements of the UK National Health System (NHS) technical guidelines and relevant literature, measuring the water leak and pressure drop over time, both open air and in a approximate uterus model. The resulting prototype is made up of six components, some of which are easy to retrieve, namely a water bottle, a silicone tube and an ordinary condom, while others can be manufactured locally using 3D printers, namely a modified bottle cap, a flow stopper and a valve for holding the condom in place. Validation testing bore promising results with no water or pressure leak open air, and minimal leaks in the approximate uterus model. This demonstrates that the 3D printed condom-based intrauterine balloon tamponade is performing well against the requirements and, when compared to the state of the art, it could be a more appropriate and more resilient solution to low-resource settings, as it bypasses the challenges in the supply of consumables and presents a greener option based on circular economy
A smartphone-based tool for screening diabetic neuropathies: A mHealth and 3D printing approach
Full text linkDiabetic neuropathy, a nerve damage associated with diabetes mellitus, can lead to severe disabilities, morbidity, and mortality, if not diagnosed in a timely manner. Diabetic neuropathies represent a huge economic burden and are a growing problem in sub-Saharan Africa, where they affect up to 61% of the diabetic population. Therefore, the United Nations (UN) has included the reduction of the diabetes-related mortality, as a priority in the Sustainable Development Agenda. A review of the current existing solutions for diabetic patients highlighted the fact that many are focused on lifestyle management and glycemia monitoring, while less are available for diabetic neuropathies screening, in particular in the digital health field. Beyond cutting-edge screening methods, which are time-consuming and equipment-heavy, traditional ones are effective, but they require specialised knowledge, which often lacks in low-resource settings. These settings, specifically those in low-income countries, are challenged by the lack of expertise, funds, spare parts, and consumables and harsh environmental conditions, which hinder the safe use of medical devices. This paper proposes a smart-tool for the screening of diabetic neuropathies based on the effective combination of three already established methods, through 3D-printed accessories and a smartphone app, aiming at contributing towards the UN’s Sustainable Development Goal 3, as well as the fourth industrial revolution in healthcare. Moreover, an on-field evaluation for this smart-tool is ongoing. So far, we recruited 11 normosubjects as a pilot study. The results demonstrate that it could be a viable solution to improve the standard of care of diabetic patients, specifically in the field of diabetic neuropathy screening, globally, as well as locally in low-resource settings
Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients
No full textThe aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls
Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life
Full text linkBackground: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. Methods: ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman's rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. Results: Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. Conclusion: This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.Background: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress. Methods: ECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman's rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features. Results: Six out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier. Conclusion: This study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation
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