Are the variations in ECG morphology associated to different blood glucose levels? implications for non-invasive glucose monitoring for T1D paediatric patients.

AIMS: Recent clinical trials and real-world studies highlighted those variations in ECG waveforms and HRV recurrently occurred during hypoglycemic and hyperglycemic events in patients with diabetes. However, while several studies have been carried out for adult age, there is lack of evidence for paediatric patients. The main aim of the study is to identify the correlations of variations in ECG Morphology waveforms with blood glucose levels in a paediatric population. METHODS: T1D paediatric patients who use CGM were enrolled. They wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glucose metrics, ECG parameters and HRV features were collected, and Wilcoxon rank-sum test and Spearman's correlation analysis were used to explore if different levels of blood glucose were associated to ECG morphological changes. RESULTS: Results showed that hypoglycaemic events in paediatric patients with T1D are strongly associated with variations in ECG morphology and HRV. CONCLUSIONS: Results showed the opportunity of using the ECG as a non-invasive adding instrument to monitor the hypoglycaemic events through the integration of the ECG continuous information with CGM data. This innovative approach represents a promising step forward in diabetes management, offering a more comprehensive and effective means of detecting and responding to critical changes in glucose levels.

Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure.

Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.

The use of smart environments and robots for infection prevention control: A systematic literature review.

BACKGROUND: Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings METHODS: The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. RESULTS: The search strategy returned 1520 citations and 17 papers were included. This review identified 3 main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (ie, air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. CONCLUSIONS: Increasing the awareness of healthcare workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries.

Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol.

Purpose: Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device. Methods: This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices. Results: Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm. Conclusion: This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions. Trial registration: ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1. Supplementary information: The online version contains supplementary material available at 10.1007/s12553-022-00719-x.

A simulation model estimates lifetime health and economic outcomes of screening prediabetes using the 1-h plasma glucose.

AIMS: The current method to diagnose impaired glucose tolerance (IGT) is based on the 2-h plasma glucose (2-hPG) value during a 75-g oral glucose tolerance test (OGTT). Robust evidence demonstrates that the 1-h post-load plasma glucose (1-hPG) ≥ 8.6 mmol/L in those with normal glucose tolerance is highly predictive of type 2 diabetes (T2D), micro and macrovascular complications and mortality. The aim of this study was to conduct a health economic analysis to estimate long-term cost-effectiveness of using the 1-hPG compared to the 2-hPG for screening and assessing the risk of diabetes over 35 years. The main outcome was cost per quality-adjusted life year (QALY) gained. METHODS: A Monte Carlo-based Markov simulation model was developed to forecast long-term effects of two screening strategies with regards to clinical and cost-effectiveness outcomes. The base case model included 20,000 simulated patients over 35-years follow-up. Transition probabilities on disease progression, mortality, effects on preventive treatments and complications were retrieved from landmark diabetes studies. Direct medical costs were sourced from published literature and inflated to 2019 Euros. RESULTS: In the lifetime analysis, the 1-hPG was projected to increase the number of years free from disease (2 years per patient); to delay the onset of T2D (1 year per patient); to reduce the incidence of T2D complications (0·6 RR-Relative Risk per patient) and to increase the QALY gained (0·58 per patient). Even if the 1-hPG diagnostic method resulted in higher initial costs associated with preventive treatment, long-term diabetes-related costs as well as complications costs were reduced leading to a lifetime saving of - 31225719.82€. The incremental cost-effectiveness ratio was - 8214.7€ per each QALY gained for the overall population. CONCLUSIONS: Screening prediabetes with the 1-hPG is feasible and cost-effective resulting in reduced costs per QALY. Notwithstanding, the higher initial costs of testing with the 1-hPG compared to the 2-hPG due to incremental preventive intervention, long-term diabetes and complications costs were reduced projecting an overall cost saving of - 8214.7€ per each QALY gained.

3D-printed activated charcoal inlet filters for oxygen concentrators: A circular economy approach.

As of May 2021, the current COVID-19 pandemic is still plaguing the world, challenging all the countries and their health systems, globally. In this context, conditions typical of low-resource settings surfaced also in high-resource ones (e.g., the lack of essential medical equipment, of resources etc.), while exacerbating in the already resource-scarce settings, because of COVID-19. This is the case of oxygen concentrators that are one of the first-line medical devices for treating COVID-19 patients. Since the beginning of 2020, their demand has been rapidly growing worldwide, aggravating the situation for low-resource settings, where the availability of devices providing oxygen-enriched air was already scarce. In fact, due to their delicacy, the lack of spare parts and of an appropriate health technology management system, oxygen concentrators can often be found broken or not working properly in these settings. The underlying problems have deep roots. The current regulatory frameworks and standards, which are set by high-income countries, are too stringent, and do not take into account the limited resources of poorer settings. Thus, they are often inapplicable in such settings. One of the main issues affecting the oxygen concentrators, is that related to the filters, which are designed to filter out dust, particles, bacteria, and to be used in medical locations complying with international standards (e.g., the air filtration level in a surgical theatre in Italy is at 99.97%). When used in low-resource settings, which do not comply with these standards and face several challenges (e.g., dust), these filters have a much-reduced lifespan. For these reasons, this paper aims to present the redesign of the inlet filter of an oxygen concentrator, which is used to prevent gross particles to enter the device. The redesign is based on a reverse engineering approach, and on the use of 3D-printing along with activated charcoal. After testing the filtration efficiency with a particle counter, the filter design has been refined through several iterations. The final prototype performs particularly well when filtering particles above 1 µm (with a filtration efficiency of 64.2%), and still has a satisfactory performance with any particle size over 0.3 µm (with a filtration efficiency of 38.8%). Following the United Nations Sustainable Development Goals, this project aims to empower local communities, and start a positive trend of self-sustained supply chain of simple spare parts for medical devices, leveraging on frugal engineering, 3D-printing, locally produced activated charcoal, and circular economy.

Health Technology Assessment of Intensive Care Ventilators for Pediatric Patients.

This paper is aimed at addressing all the critical aspects linked to the implementation of intensive care ventilators in a pediatric setting, highlighting the most relevant technical features and describing the methodology to conduct health technology assessment (HTA) for supporting the decision-making process. Four ventilator models were included in the assessment process. A decision-making support tool (DoHTA method) was applied. Twenty-eight Key Performance Indicators (KPIs) were identified, defining the safety, clinical effectiveness, organizational, technical, and economic aspects. The Performance scores of each ventilator have been measured with respect to KPIs integrated with the total cost of ownership analysis, leading to a final rank of the four possible technological solutions. The final technologies' performance scores reflected a deliver valued, contextualized, and shared outputs, detecting the most performant technological solution for the specific hospital context. HTA results had informed and supported the pediatric hospital decision-making process. This study, critically identifying the pros and cons of innovative features of ventilators and the evaluation criteria and aspects to be taken into account during HTA, can be considered as a valuable proof of evidence as well as a reliable and transferable method for conducting decision-making processes in a hospital context.

Correction to: Clinical needs and technical requirements for ventilators for COVID-19 treatment critical patients: an evidence-based comparison for adult and pediatric age.

[This corrects the article DOI: 10.1007/s12553-020-00467-w.].

Clinical needs and technical requirements for ventilators for COVID-19 treatment critical patients: an evidence-based comparison for adult and pediatric age.

The spread of severe acute respiratory syndrome coronavirus 2, taking on pandemic proportions, is placing extraordinary and unprecedented demands on healthcare systems worldwide. The increasing number of critical patients who, experiencing respiratory failure from acute respiratory distress syndrome, need respiratory support, has been leading countries to race against time in arranging new Intensive Care Units (ICUs) and in finding affordable and practical solutions to manage patients in each stage of the disease. The simultaneous worldwide emergency caused serious problems for mechanical ventilators supply. This chaotic scenario generated, indeed, a frenetic race to buy life-saving ventilators. However, the variety of mechanical ventilators designs, together with the limitations in time and resources, make the decision-making processes on ventilators procurement crucial and not counterbalanced by the evaluation of devices quality. This paper aimed at offering an overview of how evidence-based approach for health technologies evaluation, might provide support during Corona Virus Disease 2019 (COVID-19) pandemic in ICUs management and critical equipment supply. We compared and combined all the publicly available indications on the essential requirements that ICU ventilators might meet to be considered acceptable for treating COVID-19 patients in severe to critical illnesses. We hope that the critical analysis of these data might help readers to understand how structured decision-making processes based on evidence, evaluating the safety and effectiveness of a given medical device and the effects of its introduction in a healthcare setting, are able to optimize time and resources allocation that should be considered essential, especially during pandemic period.

Robotic Technology in Pediatric Neurorehabilitation. A Pilot Study of Human Factors in an Italian Pediatric Hospital.

The introduction of robotic neurorehabilitation among the most recent technologies in pediatrics represents a new opportunity to treat pediatric patients. This study aims at evaluating the response of physiotherapists, patients and their parents to this new technology. The study considered the outcomes of technological innovation in physiotherapists (perception of the workload, satisfaction), as well as that in patients and their parents (quality of life, expectations, satisfaction) by comparing the answers to subjective questionnaires of those who made use of the new technology with those who used the traditional therapy. A total of 12 workers, 46 patients and 47 parents were enrolled in the study. Significant differences were recorded in the total workload score of physiotherapists who use the robotic technology compared with the traditional therapy (p < 0.001). Patients reported a higher quality of life and satisfaction after the use of the robotic neurorehabilitation therapy. The parents of patients undergoing the robotic therapy have moderately higher expectations and satisfaction than those undergoing the traditional therapy. In this pilot study, the robotic neurorehabilitation technique involved a significant increase in the patients' and parents' expectations. As it frequently happens in the introduction of new technologies, physiotherapists perceived a greater workload. Further studies are needed to verify the results achieved.

Experimental application of Business Process Management technology to manage clinical pathways: a pediatric kidney transplantation follow up case.

BACKGROUND: To test the application of Business Process Management technology to manage clinical pathways, using a pediatric kidney transplantation as case study, and to identify the benefits obtained from using this technology. METHODS: Using a Business Process Management platform, we implemented a specific application to manage the clinical pathway of pediatric patients, and monitored the activities of the coordinator in charge of the case management during a 6-month period (from June 2015 to November 2015) using two methodologies: the traditional procedure and the one under study. RESULTS: The application helped physicians and nurses to optimize the amount of time and resources devoted to management purposes. In particular, time reduction was close to 60%. In addition, the reduction of data duplication, the integrated event management and the efficient data collection improved the quality of the service. CONCLUSIONS: The use of Business Process Management technology, usually related to well-defined processes with high management costs, is an established procedure in multiple environments; its use in healthcare, however, is innovative. The use of already accepted clinical pathways is known to improve outcomes. The combination of these two techniques, well established in their respective areas of application, could represent a revolution in clinical pathway management. The study has demonstrated that the use of this technology in a clinical environment, using a proper architecture and identifying a well-defined process, leads to real benefits in terms of resources optimization and quality improvement.

Evaluation of thermal comfort and carbon dioxide levels during ventricular-assist-device implant. / Valutazione del benessere termico e dei livelli di anidride carbonica durante l'impianto di in dispositivo di assistenza ventricolare.

BACKGROUND: Environmental measurements were performed in an operating theatre within a pediatric cardiac department, during a surgical operation involving the use of carbon dioxide for the implantation of a ventricular system (VAD). OBJECTIVES: After some reports from the staff, who were complaining about low temperatures in the operating room, it was decided to check carbon dioxide levels, the conditions of thermal comfort and the presence of draughts. METHODS: Microclimatic parameters and carbon dioxide concentration were performed with a microclimatic unit Delta OHM model HD 32.1. RESULTS: The carbon dioxide concentration values measured during the operation were below the levels at which the working environment was not comfortable, as expressed by both the ASHRAE (American Society of Heating, Refrigeration and Air Conditioning Engineers) and the ACGIH (American Conference of Government Industrial Hygienists) standards. PMV (Predicted Mean Vote) and PPD (Preticted Pencentage of Dissatisfied) values obtained indicate a thermal discomfort tendency to cold perception, perceived in particular by the anesthesiologist, circulating nurse and cardiovascular perfusionist. Airflow discomforts occurred at different stages of the operation. CONCLUSIONS: Acting on the air conditioning system, decreasing air velocity, while guaranteeing the minimum number of air recirculation prescribed by the regulations, appears to be the best prevention measure. Changing the mode of laminar air inlet above the cot may, however, affect the "wash" effect of the operating range. Otherwise, a "protective" measure could concern staff clothes, providing them with garments with better insulation, in order to protect the neck area, which is affected by the effects of draughts.

Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review.

The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.