A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care.

Critical care physicians are commonly faced with patients exhibiting atrial fibrillation (AF), a cardiac arrhythmia with multifaceted origins. Recent investigations shed light on the heterogeneity among AF patients by uncovering unique AF phenotypes, characterized by differing treatment strategies and clinical outcomes. In this retrospective study encompassing 9401 AF patients in an intensive care cohort, we sought to identify differences in average treatment effects (ATEs) across different patient groups. We extract data from the MIMIC-III database, use hierarchical agglomerative clustering to identify patients' phenotypes, and assign them to treatment groups based on their initial drug administration during AF episodes. The treatment options examined included beta blockers (BBs), potassium channel blockers (PCBs), calcium channel blockers (CCBs), and magnesium sulfate (MgS). Utilizing multiple imputation and inverse probability of treatment weighting, we estimate ATEs related to rhythm control, rate control, and mortality, approximated as hourly and daily rates (%/h, %/d). Our analysis unveiled four distinctive AF phenotypes: (1) postoperative hypertensive, (2) non-cardiovascular mutlimorbid, (3) cardiovascular multimorbid, and (4) valvulopathy atrial dilation. PCBs showed the highest cardioversion rates across phenotypes, ranging from 11.6%/h (9.35-13.3) to 7.69%/h (5.80-9.22). While CCBs demonstrated the highest effectiveness in controlling ventricular rates within the overall patient cohort, PCBs and MgS outperformed them in specific phenotypes. PCBs exhibited the most favorable mortality outcomes overall, except for the non-cardiovascular multimorbid cluster, where BBs displayed a lower mortality rate of 1.33%/d [1.04-1.93] compared to PCBs' 1.68%/d [1.10-2.24]. The results of this study underscore the significant diversity in ATEs among individuals with AF and suggest that phenotype-based classification could be a valuable tool for physicians, providing personalized insights to inform clinical decision making.

[Digital literacy as a key element in the digital transformation of health organizations]. / La alfabetización digital como elemento clave en la transformación digital de las organizaciones en salud.

In the last years, the digital transformation, has become a reality influencing organizational processes and advancing services for users. This transformation must align with WHO guidelines, addressing the needs of individuals globally and acknowledging Social Determinants of Health and emerging Digital Determinants of Health and the digital divide thas has been created. To accomplish this, the appropriate legislation and infrastructures are required. Correspondingly technology enables enhanced self-care and increased participation in decision-making across various levels, consequently, addressing the digital divide must not be an exception, and needs to include citizens, communities, entities, and professionals to work on how to diminish it and solve it. As a result of this national and supranational campaigns should formulate unified plans and strategies, that include training requirements and establishing programs for both professionals and users, highlighting the significance of incorporating digital knowledge on both groups.

Use of tele-nursing in primary care: A qualitative study on its negative and positive aspects.

OBJECTIVE: To analyze the opinions of nursing professionals on the current limitations and future potential of digital tools in healthcare. DESIGN: Qualitative and descriptive study. LOCATION: The study took place during an asynchronous MOODLE course on the use of ICT in healthcare, specifically aimed at nursing professionals. PARTICIPANTS: The number of nurses enrolled in the course was 150. METHODS: A qualitative study was conducted focusing on the positive and negative aspects that telenursing can offer in the context of a Moodle training in new technologies for nurses. A thematic analysis was carried out following the method proposed by Braun and Clarke. RESULTS: In the end 68 nurses participated in the forum. Their statements, opinions and perceptions were analyzed and 28 descriptive codes were obtained and subsequently categorized into positive and negative aspects. CONCLUSIONS: Nurses positively value the usefulness of digital tools and identify a wide range of benefits of telenursing in daily practice. At the same time, they point out crucial limitations that may slow down the adoption of telenursing, pointing to areas for improvement such as training and digital literacy of both patients and professionals. They consider that telenursing can humanise care, but insist on the need to prevent its use from increasing health inequalities.

The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities.

Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.

Short-Term Effects of Manual Therapy on Balance: A Multicenter, Randomized, Double-Blind Controlled Trial.

OBJECTIVE: The purpose of this study was to analyze short-term changes in dynamic and static balance after a manual therapy protocol in healthy participants and analyze any repercussions on mood and perception of change after applying articulatory techniques. METHODS: A single-blind, randomized, multicenter clinical trial was conducted. Participants were allocated to either a manual therapy group (MTG) (n = 101) or a control group (CG) without intervention (n = 99), and measures were taken before treatment, after the intervention, and 1 week after treatment. Assessments included the Star Excursion Balance Test, Unipedal Stance Test (UPST), Profile of Mood States (POMS), and Patient Global Impression of Change (PGIC) scale. RESULTS: Two hundred healthy participants completed the study (mean age, 22 [SD = 2.67]). There was a statistically significant interaction between groups and time measurements in the right leg for anterior (P = .003), posteromedial (P < .001), and posterolateral (P = .001) directions in favor of the MTG, as well as in the left leg for anterior (P < .001), posteromedial (P < .001), and posterolateral (P = .012) directions. The analysis failed to show statistically significant interactions between any of the factors for the UPST and POMS (P > .05). The MTG showed a significant improvement compared to the CG after treatment (P = .003) and at 1-week follow-up (P < .001) on the PGIC scale. CONCLUSION: The results suggest the MT intervention was effective on dynamic balance in post-intervention in healthy participants, and some of the directions maintained the results at 1-week follow-up. Perception of change in post-treatment and 1-week follow-up also significantly improved. The protocol did not seem to produce changes in static balance and mood states. Positive changes after manual therapy were maintained in the short term.

How to Design Successful Participatory Design Workshops for Digital Health Solutions?

Participatory design (PD) is increasingly used to support design and development of digital health solutions. The involves representatives of future user groups and experts to collect their needs and preferences and ensure easy to use and useful solutions. However, reflections and experiences with PD in designing digital health solutions are rarely reported. The objective of this paper is to collect those experiences including lessons learnt and moderator experiences, and to identify challenges. For this purpose, we conducted a multiple case study to explore the skill development process required to successfully design a solution in the three cases. From the results, we derived good practice guidelines to support designing successful PD workshops. They include adapting the workshop activities and material to the vulnerable participant group and considering their environment and previous experiences, planning sufficient time for preparation and supporting the activities with appropriate material. We conclude that PD workshop results are perceived as useful for designing digital health solutions, but careful design is very relevant.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.

BACKGROUND: Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE: This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS: A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS: A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS: These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

mHealth intervention to improve quality of life in patients with chronic diseases during the COVID-19 crisis in Paraguay: A study protocol for a randomized controlled trial.

BACKGROUND: Patients with chronic disease represent an at-risk group in the face of the COVID-19 crisis as they need to regularly monitor their lifestyle and emotional management. Coping with the illness becomes a challenge due to supply problems and lack of access to health care facilities. It is expected these limitations, along with lockdown and social distancing measures, have affected the routine disease management of these patients, being more pronounced in low- and middle-income countries with a flawed health care system. OBJECTIVES: The purpose of this study is to describe a protocol for a randomized controlled trial to test the efficacy of the Adhera® MejoraCare Digital Program, an mHealth intervention aimed at improving the quality of life of patients with chronic diseases during the COVID-19 outbreak in Paraguay. METHOD: A two-arm randomized controlled trial will be carried out, with repeated measures (baseline, 1-month, 3-month, 6-month, and 12-month) under two conditions: Adhera® MejoraCare Digital Program or waiting list. The primary outcome is a change in the quality of life on the EuroQol 5-Dimensions 3-Levels Questionnaire (EQ-5D-3L). Other secondary outcomes, as the effect on anxiety and health empowerment, will be considered. All participants must be 18 years of age or older and meet the criteria for chronic disease. A total of 96 participants will be recruited (48 per arm). CONCLUSIONS: It is expected that the Adhera® MejoraCare Digital Program will show significant improvements in quality of life and emotional distress compared to the waiting list condition. Additionally, it is hypothesized that this intervention will be positively evaluated by the participants in terms of usability and satisfaction. The findings will provide new insights into the viability and efficacy of mHealth solutions for chronic disease management in developing countries and in times of pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT04659746.

Clinical Changes of Respiratory Parameters in Institutionalized Older Adults after a Physiotherapy Program Combining Respiratory and Musculoskeletal Exercises.

Nowadays pulmonary diseases are an increasingly important cause of morbidity and mortality. Diaphragmatic breathing is a controlled-breathing technique that aims to optimize thoracoabdominal movements. The aim of this study was to apply a respiratory and musculoskeletal physiotherapy program in institutionalized older adults and to assess the effects on their pulmonary function tests and oxygen saturation. A randomized double-blind clinical trial was conducted with thirty institutionalized older adults, randomly assigned to a control group (CG), who conducted musculoskeletal exercises; or an experimental group (EG) who, in addition, carried out diaphragmatic breathing, administered for eight weeks, three times/week. Outcomes were pulmonary function variables (forced vital capacity, FVC; forced expired volume at 1 s, FEV1; the FEV1/FVC ratio) and oxygen saturation (SpO2) before and after treatment. Normality of the distributions was tested with Saphiro-Wilk and the pre-post improvement was assessed with a two-sample Mann-Whitney test. Significance level was corrected for multiple comparisons using Benjamini-Hochberg correction (p < 0.04). There was a clinically significant improvement of FVC and FEV1 for the EG. Moreover, the EG showed a statistically significant increase of SpO2 (p = 0.028) after treatment when compared to CG. A physiotherapy program combining breathing and musculoskeletal exercises, improved respiratory parameters in institutionalized older adults.

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.

BACKGROUND: Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE: To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS: A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS: The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION: Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.

Motivating Physical Activity for Individuals with Intellectual Disability through Indoor Bike Cycling and Exergaming.

People with intellectual disabilities have more sedentary lifestyles than the general population. Regular physical activity is of both medical and social importance, reducing the risk of cardiovascular disease and promoting functioning in everyday life. Exergames have been envisioned for promoting physical activity; however, most of them are not user-friendly for individuals with intellectual disabilities. In this paper, we report the design, development, and user acceptance of a mobile health solution connected to sensors to motivate physical activity. The system is mounted on an indoor stationary bicycle and an ergometer bike tailored for people with intellectual disabilities. The development process involved the application of user-centered design principles to customize the system for this group. The system was pilot-tested in an institutional house involving six end-users (intervention group) and demonstrated/self-tested to relatives of persons with ID and staff (supervision group). A System Usability Scale and open-ended interview in the supervision group were used to assess the user acceptance and perceived usefulness. Results indicate that the users with an intellectual disability enjoyed using the system, and that respondents believed it was a useful tool to promote physical activity for the users at the institution. The results of this study provide valuable information on beneficial technological interventions to promote regular physical activity for individuals with intellectual disabilities.

A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards.

The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Results after 13 years of kidney-pancreas transplantation in type 1 diabetic patients in Comunidad Valenciana.

INTRODUCTION: Simultaneous pancreas-kidney (SPK) transplant is a proven option of treatment for patients with type 1 diabetes mellitus and related end-stage renal disease, who are candidates for kidney transplantation. The results from the beginning of SPK transplant program in Comunidad Valenciana are presented. METHODS: Descriptive, retrospective, and single-center study of the pancreas transplant performed at the Hospital Universitari i Politècnic La Fe, from September 2002 to December 2015. Clinical variables from donors and recipients, peri-operative variables, patient survival, and pancreatic graft survival were collected. RESULTS: Eighty-one patients with type 1 diabetes mellitus (48 males and 33 females, mean age 37.4 ± 5.7 years, mean BMI 24.1 ± 3.4 kg/m2, mean duration of diabetes 25.5 ± 6.5 years) received SPK transplantation. The overall patient survival at one, 3, and 5 years were 91.3%, 91.3% and 89.5%, respectively. However, patient survival in the periods 2002-2008 and 2009-2015 were 88.2% and 93.6% at one year, 88.2% and 93.7% at 3 years, and 85.3% and 93.7% at 5 years, respectively (P = 1). The overall pancreatic graft survival at one, 3, and 5 years were 75.2%, 69.1% and 63.2%, respectively. On the other hand, pancreatic graft survival in the periods 2002-2008 and 2009-2015 were 67.5% and 80.6% at one year, 64.7% and 71.8% at 3 years, and 58.8% and 65.3% at 5 years, respectively (P = .0109). Post-transplant complications were: graft rejection 8.6%, venous graft thrombosis 7.4%, graft pancreatitis 4.9%. CONCLUSIONS: In 13 years' experience of SPK transplantation, patient and pancreatic graft survival and the rate of complications after pancreas transplantation were similar to those of other larger series. The medical-surgical team experience improves pancreatic graft survival without influencing patient survival.

Results after 13 years of kidney-pancreas transplantation in type 1 diabetic patients in Comunidad Valenciana. / Resultados tras 13 años del inicio del trasplante simultáneo de páncreas-riñón en pacientes con diabetes mellitus tipo 1 en la Comunidad Valenciana.

INTRODUCTION: Simultaneous pancreas-kidney (SPK) transplant is a proven option of treatment for patients with type 1 diabetes mellitus and related end-stage renal disease, who are candidates for kidney transplantation. The results from the beginning of SPK transplant program in Comunidad Valenciana are presented. METHODS: Descriptive, retrospective, and single-center study of the pancreas transplant performed at the Hospital Universitari i Politècnic La Fe, from September 2002 to December 2015. Clinical variables from donors and recipients, peri-operative variables, patient survival, and pancreatic graft survival were collected. RESULTS: Eighty-one patients with type 1 diabetes mellitus (48 males and 33 females, mean age 37.4±5.7 years, mean BMI 24.1±3.4kg/m2, mean duration of diabetes 25.5±6.5 years) received SPK transplantation. The overall patient survival at one, 3, and 5 years were 91,3, 91,3 and 89,5%, respectively. However, patient survival in the periods 2002-2008 and 2009-2015 were 88.2 and 93.6% at one year, 88.2 and 93.7% at 3 years, and 85.3 and 93.7% at 5 years, respectively (P=1). The overall pancreatic graft survival at one, 3, and 5 years were 75.2, 69.1 and 63.2%, respectively. On the other hand, pancreatic graft survival in the periods 2002-2008 and 2009-2015 were 67.5 and 80.6% at one year, 64.7 and 71.8% at 3 years, and 58.8% and 65.3% at 5 years, respectively (P=.0109). Postransplant complications were: graft rejection 8.6%, venous graft thrombosis 7.4%, graft pancreatitis 4.9%. CONCLUSIONS: In 13-year's experience of SPK transplantation, patient and pancreatic graft survival and the rate of complications after pancreas transplantation were similar to those of other larger series. The medical-surgical team experience improves pancreatic graft survival without influencing patient survival.

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper.

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

Simultaneous Pancreas Kidney Transplantation Improves Cardiovascular Autonomic Neuropathy with Improved Valsalva Ratio as the Most Precocious Test.

BACKGROUND: Simultaneous pancreas-kidney (SPK) transplantation is a proven option of treatment for patients with type 1 diabetes mellitus (T1DM) and related end-stage renal disease. There is discrepancy between the results of different studies about the impact of prolonged normalization of glucose metabolism achieved by SPK on the course of diabetic complications including severe forms of diabetic neuropathy. The objective of the study was to evaluate the prevalence of cardiovascular autonomic neuropathy (CAN) in patients undergoing SPK transplantation and its evolution 10 years after transplantation. METHODS: Prospective study of 81 patients transplanted in a single center from year 2002 to 2015. Autonomic function was assessed using cardiovascular autonomic reflex tests (CARTs). CARTs were made before SPK transplantation and during the follow-up. Evolution of tests after SPK transplantation was evaluated by contrasting hypotheses (paired tests). Multiple testing was adjusted with the Benjamini-Hochberg procedure with a false discovery rate of 10%. RESULTS: 48 males and 33 females, mean age 37.4 ± 5.7 years, mean BMI 24.0 ± 3.4 kg/m2, and mean duration of diabetes 25.5 ± 6.5 years, received SPK transplantation. Ten years after SPK transplantation, 56 patients re tained the pancreatic graft (42 of them with normofunctioning pancreas and 14 with low doses of insulin therapy). These 42 patients were selected for the autonomic study. Before transplant procedure, all CART results were abnormal. After SPK transplantation, paired test analysis showed an improvement of systolic blood pressure (SBP) response to orthostasis at the 5th year after SPK (p = 0.03), as well as improvement of the Valsalva ratio at the 3rd (p < 0.001) and 5th (p = 0.001) year after SPK. After correcting for the false discovery rate, all the variables of autonomic study reached significance at different time points. CONCLUSIONS: Prevalence of CAN in patients who are candidates for SPK transplantation is high and is generally advanced. SPK transplantation improves CAN with improved Valsalva ratio as the most precocious test.

Autoimmune Diabetes Recurrence After Pancreas Transplantation: Diagnosis, Management, and Literature Review.

BACKGROUND Pancreas transplantation can be a viable treatment option for patients with type 1 diabetes mellitus (T1DM), especially for those who are candidates for kidney transplantation. T1DM may rarely recur after pancreas transplantation, causing the loss of pancreatic graft. The aim of this study was to describe the prevalence of T1DM recurrence after pancreas transplantation in our series. MATERIAL AND METHODS Eighty-one patients transplanted from 2002 to 2015 were included. Autoantibody testing (GADA and IA-2) was performed before pancreas transplantation and during the follow-up. RESULTS The series includes 48 males and 33 females, mean age 37.4±5.7 years and mean duration of diabetes 25.5±6.5 years. Patients received simultaneous pancreas kidney (SPK) transplantation. After SPK transplantation, 56 patients retained pancreatic graft, 8 patients died, and 17 patients lost their pancreatic graft. T1DM recurrence occurred in 2 of the 81 transplanted patients, yielding a prevalence of 2.5%, with an average time of appearance of 3.3 years after transplant. Pancreatic enzymes were normal in the 2 patients, ruling out pancreatic rejection. T1DM recurrence was confirmed histologically, showing selective lymphoid infiltration of the pancreatic islets. CONCLUSIONS T1DM recurrence after pancreas transplantation is infrequent; however, it is one of the causes of pancreatic graft loss that should always be ruled out. Negative autoimmunity prior to transplantation does not ensure that T1DM does not recur.

User Centered Design to Improve Information Exchange in Diabetes Care Through eHealth : Results from a Small Scale Exploratory Study.

Heterogeneity of people with diabetes makes maintaining blood glucose control and achieving therapy adherence a challenge. It is fundamental that patients get actively involved in the management of the disease in their living environments. The objective of this paper is to evaluate the use and acceptance of a self-management system for diabetes developed with User Centered Design Principles in community settings. Persons with diabetes and health professionals were involved the design, development and evaluation of the self-management system; which comprised three iterative cycles: scenario definition, user archetype definition and system development. A comprehensive system was developed integrating modules for the management of blood glucose levels, medication, food intake habits, physical activity, diabetes education and messaging. The system was adapted for two types of principal users (personas): Type 1 Diabetes user and Type 2 Diabetes user. The system was evaluated by assessing the use, the compliance, the attractiveness and perceived usefulness in a multicenter randomized pilot study involving 20 patients and 24 treating professionals for a period of four weeks. Usage and compliance of the co-designed system was compared during the first and the last two weeks of the study, showing a significantly improved behaviour of patients towards the system for each of the modules. This resulted in a successful adoption by both type of personas. Only the medication module showed a significantly different use and compliance (p= 0.01) which can be explained by the different therapeutic course of the two types of diabetes. The involvement of patients to make their own decisions and choices form design stages was key for the adoption of a self-management system for diabetes.

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project.

BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.