Optimal schedule for home blood pressure measurements and clinical significance of the variability in home-measured blood pressure and heart rate

Siirretty Doriast

Medical Devices Information Systems in Primary Care

People who suffer from chronic diseases are becoming more involved in remote monitoring processes each year. The market acceptance of remote care programmes, connecting patients through medical devices as part of the treatment regime, is spreading worldwide. Healthcare providers use medical devices to monitor, in various ways, the chronically ill population, namely people with diabetes and hypertension. However, most hospital and service provider information systems do not conform to the same important data standards, making interoperability and information sharing difficult. In this sense, the Multimorbidity Health Information System (METHIS) project is a multidisciplinary, goal-oriented, design-science-based intervention aiming to improve physician-patient communication and patient engagement. It focuses on multimorbidity and ageing, encompassing patients with more than one chronic disease and over 65 years old. The proposed solution is a Clinical Medical Devices Information (CMDI) system and data model which contains standardised information about chronic patients, medical devices and other data sets to be included in the METHIS System. With this framework, the system can perform consistently and reliably while meeting all relevant regulatory requirements or standards. Based on this dissertation and the METHIS project’s complementary work, implementing the CMDI in various Family Health Unit (FHU) in Portugal will make it possible to combat the diversity and loss of telemonitoring information.A cada ano, os doentes crónicos estão mais envolvidos em processos de telemonitorização. A aceitação pelo mercado de programas de cuidados à distância, ligando doentes através de dispositivos médicos como parte do regime de tratamento, está a espalhar-se por todo o mundo. Os prestadores de cuidados de saúde utilizam dispositivos médicos para monitorizar, de várias formas, a população cronicamente doente, nomeadamente as pessoas com diabetes e hipertensão arterial. No entanto, a maioria dos sistemas de informação dos hospitais e prestadores de serviços não estão em conformidade com as mesmas normas, o que dificulta a interoperabilidade e a partilha de informação. Neste sentido, o projeto METHIS é uma intervenção multidisciplinar, baseada em Design Science, que visa melhorar a comunicação entre médico e doente e o envolvimento do mesmo. Tem como foco a multimorbidade e o envelhecimento, englobando doentes com várias doenças crónicas e com idade superior a 65 anos. A solução proposta é um sistema e o correspondente modelo de dados CMDI que contém informação padronizada sobre doentes crónicos, dispositivos médicos e outros conjuntos de dados a serem incluídos no Sistema METHIS. Com este modelo de dados, o sistema possui a informação para poder funcionar de forma consistente e fiável, cumprindo todos os requisitos ou normas regulamentares relevantes. Com base nesta dissertação e no trabalho complementar do projeto METHIS, a implementação da base de dados CMDI em vários Unidades de Saúde em Portugal tornará possível combater a diversidade e a perda de informação na telemonitorização

Effectiveness of bedside investigations to diagnose peripheral artery disease among people with diabetes mellitus: A systematic review.

As a progressive disease process, early diagnosis and ongoing monitoring and treatment of lower limb peripheral artery disease (PAD) is critical to reduce the risk of diabetes-related foot ulcer (DFU) development, non-healing of wounds, infection and amputation, in addition to cardiovascular complications. There are a variety of non-invasive tests available to diagnose PAD at the bedside, but there is no consensus as to the most diagnostically accurate of these bedside investigations or their reliability for use as a method of ongoing monitoring. Therefore, the aim of this systematic review was to first determine the diagnostic accuracy of non-invasive bedside tests for identifying PAD compared to an imaging reference test and second to determine the intra- and inter-rater reliability of non-invasive bedside tests in adults with diabetes. A database search of Medline and Embase was conducted from 1980 to 30 November 2022. Prospective and retrospective investigations of the diagnostic accuracy of bedside testing in people with diabetes using an imaging reference standard and reliability studies of bedside testing techniques conducted in people with diabetes were eligible. Included studies of diagnostic accuracy were required to report adequate data to calculate the positive likelihood ratio (PLR) and negative likelihood ratio (NLR) which were the primary endpoints. The quality appraisal was conducted using the Quality Assessment of Diagnostic Accuracy Studies and Quality Appraisal of Reliability quality appraisal tools. From a total of 8517 abstracts retrieved, 40 studies met the inclusion criteria for the diagnostic accuracy component of the review and seven studies met the inclusion criteria for the reliability component of the review. Most studies investigated the diagnostic accuracy of ankle -brachial index (ABI) (N = 38). In people with and without DFU, PLRs ranged from 1.69 to 19.9 and NLRs from 0.29 to 0.84 indicating an ABI 1.3, TBI of <0.70, and absent or monophasic pedal Doppler waveforms are useful to identify the presence of disease. The ability of the tests to exclude disease is variable and although reliability may be acceptable, evidence of error in the measurements means test results that are within normal limits should be considered with caution and in the context of other vascular assessment findings (e.g., pedal pulse palpation and clinical signs) and progress of DFU healing

Optimal schedule for home blood pressure measurements and clinical significance of the variability in home-measured blood pressure and heart rate

Väitöskirja, liitteenä alkuperäisartikkelit (ei verkkoversiossa

Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging

Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis

Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

none3noDigital and information technologies are heavily pervading several aspects of human activities, improving our life quality. Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inferring systems. This paper reports, at first, a comprehensive overview of innovative sensing systems for monitoring biophysical and psychophysical parameters, all suitable for integration with wearable or portable accessories. Wearable devices represent a headstone on which the IoT-based healthcare platforms are based, providing capillary and real-time monitoring of patient’s conditions. Besides, a survey of modern architectures and supported services by IoT platforms for health monitoring is presented, providing useful insights for developing future healthcare systems. All considered architectures employ wearable devices to gather patient parameters and share them with a cloud platform where they are processed to provide real-time feedback. The reported discussion highlights the structural differences between the discussed frameworks, from the point of view of network configuration, data management strategy, feedback modality, etc.Article Number: 1660openRoberto De Fazio; Massimo De Vittorio; Paolo ViscontiDE FAZIO, Roberto; DE VITTORIO, Massimo; Visconti, Paol

AN ACTIVE NON-INTRUSIVE SYSTEM IDENTIFICATION APPROACH FOR CARDIOVASCULAR HEALTH MONITORING

In this study a novel active non-intrusive system identification paradigm is developed for the purpose of cardiovascular health monitoring. The proposed approach seeks to utilize a collocated actuator sensor unit devised from the common blood pressure cuff to simultaneously 1) produce rich transmural blood pressure waves that propagate through the cardiovascular system and 2) to make measurements of these rich peripheral transmural blood pressures utilizing the pressure oscillations produced within the cuffs bladder in order to reproduce the central aortic blood pressure accurately. To achieve this end a mathematical model of the cardiovascular system is developed to model the wave propagation dynamics of the external (excitation applied by the cuff) and internal (excitation produced by the heart) blood pressure waveforms through the cardiovascular system. Next a system identification protocol is developed in which rich transmural blood pressures are recorded and used to identify the parameters characterizing the model. The peripheral blood pressures are used in tandem with the characterized model to reconstruct the central aortic blood pressure waveform. The results of this study indicate the developed protocol can reliably and accurately reproduced the central aortic blood pressure and that it can outperform its intrusive passive counterpart (the Individualized Transfer Function methodology). The root-mean-square error in waveform reproduction, pulse pressure error and systolic pressure errors were evaluated to be 3.31 mmHg, 1.36 mmHg and 0.06 mmHg respectively for the active nonintrusive methodology while for the passive intrusive counterpart the same errors were evaluated to be 4.12 mmHg, 1.59 mmHg and 2.67 mmHg indicating the superiority of the proposed approach

Machine learning approaches for early prediction of hypertension.

Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery pressure to diagnose hypertension after its onset. However, this method cannot detect cerebrovascular alterations that lead to adverse events which may occur prior to the onset of hypertension. The early detection and quantification of these cerebral vascular structural changes could help in predicting patients who are at a high risk of developing hypertension as well as other cerebral adverse events. This may enable early medical intervention prior to the onset of hypertension, potentially mitigating vascular-initiated end-organ damage. The goal of this dissertation is to develop a novel efficient noninvasive computer-aided diagnosis (CAD) system for the early prediction of hypertension. The developed CAD system analyzes magnetic resonance angiography (MRA) data of human brains gathered over years to detect and track cerebral vascular alterations correlated with hypertension development. This CAD system can make decisions based on available data to help physicians on predicting potential hypertensive patients before the onset of the disease