Experimentation of 2.0 telemedicine in elderly patients with chronic heart failure: A study prospective in 175 patients

Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients

Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65 years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study. We applied MICE (Multivariate Imputation via Chained Equations) method to obtain a balanced, fully valued dataset and LASSO (Least Absolute Shrinkage and Selection Operator) algorithm to get the most significant features. Training (80% of records) and test (20%) cohorts were randomly selected. Study population: 3079 patients, 394 (12.8%) presented at least one readmission within 30 days, and 2685 (87.2%) did not. In the test cohort AUCs (IC95%) of XGBoost, Ada Boost Classifier, Random forest, and Gradient Boosting, and LACE Index were: 0.803 (0.734-0.872), 0.782 (0.711-0.854), 0.776 (0.703-0.848), 0.786 (0.715-0.857), and 0.504 (0.414-0.594), respectively, for predicting readmissions. A SHAP analysis was performed to offer a breakdown of the ML variables associated with readmission. Positive and negative predicting values estimates of the different ML models and LACE index were also provided, for several values of readmission rate prevalence. Among elderly patients, the rate of all-cause unplanned 30-day readmissions after hospitalization due to an acute HF was high. ML models performed better than the conventional LACE index for predicting readmissions. ML models can be proposed as promising tools for the identification of subjects at high risk of hospitalization in this clinical setting, enabling care teams to target interventions for improving overall clinical outcomes.

Analysis of Therapeutic Effect of Elderly Patients with Severe Heart Failure Based on LSTM Neural Model

In recent years, cardiovascular-related diseases have become the "number one killer" threatening human life and health and have received much attention. The timely and accurate detection and diagnosis of arrhythmias and heart failure are relatively common heart diseases, which are of great social value and research significance in improving people's quality of life by providing early treatment or intervention for those who are at risk. Based on this, this paper proposes a deep learning network architecture based on the combination of long- and short-term memory networks and deep residual neural networks for the automatic detection of heart failure. A total of 60 elderly patients with severe heart failure treated in the emergency department of our hospital from August 2019 to August 2021 were selected as the sample subjects of this study. The treatment outcomes and prognostic quality of life of the two groups of patients were compared and analyzed. Based on the unbiased test method, the accuracy of the proposed method on the authoritative open continuous heart rate database PhysioNet was 99.67% (data length 500), 98.84% (data length 1000), and 96.63% (data length 2000). This indicates that the network model can well extract the high-dimensional features of continuous heart rate and improve the accuracy of the classification model. The LSTM neural model proposed in this paper may be able to provide richer information on heart health status for portable ECG detection systems, which have very important clinical value and social significance.

Adherence and Persistence to Insulin Therapy in People with Diabetes: Impact of Connected Insulin Pen Delivery Ecosystem

Diabetes is an increasing public health problem, and insulin is the mainstay for treatment of type 1 diabetes. In type 2 diabetes treatment, insulin therapy is used after oral or other injectable agents become inadequate to achieve glycemic control. Despite the advances in insulin therapy, management of diabetes remains challenging. Numerous studies have reported low adherence and persistence to insulin therapy, which acts as a barrier to successful glycemic control and diabetes management. The aim of this targeted review article is to provide an overview of adherence and persistence to insulin therapy in people with diabetes and to discuss the impact of the emergence of a new connected ecosystem of increasingly sophisticated insulin pens, glucose monitoring systems, telemedicine, and mHealth on diabetes management. With the emergence of a connected diabetes ecosystem, we have entered an era of advanced personalized insulin delivery, which will have the potential to enhance diabetes self-management and clinical management. Early systems promise to unlock the potential to address missed or late bolus insulin delivery, which should help to address non-adherence and non-persistence. Over time, improvements in this ecosystem have the potential to combine insulin data with previously missing contextualized patient data, including meal, glucose, and activity data to support personalized clinical decisions and ultimately revolutionize insulin therapy.

Type 1 diabetes and the challenges of emotional support in crisis situations: results from a feasibility study of a multidisciplinary teleintervention

The association between type 1 diabetes and mental health disorders could be exacerbated in a stressful environment. This study aimed to evaluate the feasibility of a teleguided intervention on emotional disorders in patients with type 1 diabetes during the COVID-19 outbreak. This study was performed during the social distancing period in the COVID-19 outbreak in Brazil. Individuals with type 1 diabetes aged ≥ 18 years were selected to receive a teleguided multidisciplinary intervention or the usual care plus an educational website access. The proposed intervention aimed addressing aspects of mental health, diabetes care and lifestyle habits during the pandemic. The feasibility outcome included the assessment of recruitment capability and adherence to the proposed intervention. Moreover, we evaluated the presence of positive screening for emotional disorders (Self Report Questionnaire 20) after a 16-week intervention, patients' perceptions of pandemic-related changes, diabetes-related emotional distress, eating disorders, and sleep disorders. Data were analyzed with the intent-to-treat principle. Fifty-eight individuals (mean age, 43.8 ± 13.6 years) were included (intervention group, n = 29; control group, n = 29). At the end of the study, a total of 5 participants withdrew from the study in the intervention group compared to only 1 in the control group. Participants who dropout from the study had similar mean age, sex and income to those who remained in the study. The analysis of mental health disorders was not different between the groups at the follow up: a positive screening result was found in 48.3% and 34.5% of participants in the intervention and control groups, respectively (P = 0.29). The intervention group felt more supported in their diabetes care during the social distancing period (82.8% vs. 48.3% in the control group, P < 0.01). Our study identified a disproportionate higher number of withdrawals in the intervention group when compared to the control group. This difference may have compromised the power of the study for the proposed assessments and should be reevaluated in future studies.Trial registration: ClinicalTrials.gov (NCT04344210). Date of registration: 14/04/2020.

Type 1 diabetes and the challenges of emotional support in crisis situations: results from a randomized clinical trial of a multidisciplinary teleintervention

The association between type 1 diabetes and mental health disorders could be exacerbated in a stressful environment. This study aimed to evaluate the effectiveness of a teleguided intervention on emotional disorders in patients with type 1 diabetes during the COVID-19 outbreak. An open-label clinical trial was performed during the social distancing period in the COVID-19 outbreak in Brazil. Individuals with type 1 diabetes aged ≥ 18 years were randomized to receive a teleguided multidisciplinary intervention or the usual care plus an educational website access. The primary outcome was a positive screening for emotional disorders (Self Report Questionnaire 20) after a 16-week intervention. Secondary outcomes included evaluation of patients’ perceptions of pandemic-related changes, diabetes-related emotional distress, eating disorders, and sleep disorders. Data were analyzed with the intent‐to‐treat principle. Fifty-eight individuals (mean age, 43.8 ± 13.6 years) were included (intervention group, n = 29; control group, n = 29). The primary outcome was not different between the groups. The intervention group felt more supported in their diabetes care during the social distancing period (82.8% vs. 48.3% in the control group, P < 0.01). Both groups reported a similar self-perceived worsening of physical activity habits and mental health during the outbreak. There was no benefit to using the telehealth strategy proposed for emotional disorders in patients with type 1 diabetes during the COVID-19 outbreak. Further studies are needed to determine the impact on metabolic parameters and to understand why it is so difficult to emotionally support these patients.

Trail Registration: ClinicalTrials.gov (NCT04344210), 14/04/2020.

Glycemic Disorder Risk Remote Monitoring Program in the COVID-19 Very Elderly Patients: Preliminary Results

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. This was the basis for the “GER-e-TEC COVID study,” an experiment involving the use of the smart MyPredi^TM e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients.

Methods: The MyPredi^TM platform is connected to a medical analysis system that receives physiological data from medical sensors in real time and analyzes this data to generate (when necessary) alerts. An experiment was conducted between December 14th, 2020 and February 25th, 2021 to test this alert system. During this time, the platform was used on COVID-19 patients being monitored in an internal medicine COVID-19 unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, positive and negative predictive values with respect to clinical data.

Results: 10 older diabetic COVID-19 patients in total were monitored remotely, six of whom were male. The mean age of the patients was 84.1 years. The patients used the telemedicine solution for an average of 14.5 days. 142 alerts were emitted for the glycemic disorder risk indicating hyperglycemia, with an average of 20.3 alerts per patient and a standard deviation of 26.6. In our study, we did not note any hypoglycemia, so the system emitted any alerts. For the sensitivity of alerts emitted, the results were extremely satisfactory, and also in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts and gender played no role in the length of the hospital stay, regardless of the reason for the hospitalization (COVID-19 management).

Conclusion: This work is a pilot study with preliminary results. To date, relatively few projects and trials in diabetic patients have been run within the “telemedicine 2.0” setting, particularly using AI, ICT and the Web 2.0 in the era of COVID-19 disease.

Telemonitoring in diabetes: evolution of concepts and technologies, with a focus on results of the more recent studies

This is a narrative review of telemonitoring (remote monitoring) projects and studies within the field of diabetes, with a focus on results of the more recent studies. Since the beginning of the 1990s, several telemedicine projects and studies focused on type 1 and type 2 diabetes. Over the last 5 years, numerous telemedicine projects based on connected objects and new information and communication technologies (ICT) (elements defining telemedicine 2.0) have emerged or are still under development. Two examples are the DIABETe and Telesage telemonitoring project which perfectly fits within the telemedicine 2.0 framework – the first to include artificial intelligence (AI) with MyPredi^TM and Diabeo^TM. Mainly, these projects and studies show that telemonitoring diabetic result in: improvements in control of blood glucose (BG) level and significant reduction in HbA1c (e.g., for Telescot et TELESAGE studies); positive impact on co-morbidities (arterial hypertension, weight, dyslipidemia) (e.g., for Telescot and DIABETe studies); better patient’s quality of life (e.g., for DIABETe study); positive impact on appropriation of the disease by patients and/or greater adherence to therapeutic and hygiene-dietary measures (e.g., The Utah Remote Monitoring Project); and at least, good receptiveness by patients and their empowerment. To date, the magnitude of its effects remains debatable, especially with the variation in patients’ characteristics (e.g., background, ability for self-management, medical condition), samples selection and approach for the treatment of control groups. All of the recent studies have been classified as “Moderate” to “High”.

Current Research and New Perspectives of Telemedicine in Chronic Heart Failure: Narrative Review and Points of Interest for the Clinician

Background: This is a narrative review of both the literature and Internet pertaining to telemedicine projects within the field of heart failure, with special attention placed on remote monitoring of second-generation projects and trials, particularly in France. Results: Since the beginning of the 2000’s, several telemedicine projects and trials focused on chronic heart failure have been developed. The first telemedicine projects (e.g., TEN-HMS, BEAT-HF, Tele-HF, and TIM-HF) primarily investigated telemonitoring or for the older ones, telephone follow-up. Numerous second-generation telemedicine projects have emerged in Europe over the last ten years or are still under development for computer science heart failure, especially in Europe, such as SCAD, OSICAT, E-care, PRADO-INCADO, and TIM-HF2. The E-care telemonitoring project fits within the telemedicine 2.0 framework, based on connected objects, new information and communication technologies (ICT) and Web 2.0 technologies. E-care is the first telemedicine project including artificial intelligence (AI). TIM-HF2 is the first positive prospective randomized study with regards to EBM with positive significant clinical benefit, in terms of unplanned cardiovascular hospital admissions and all-cause deaths. The potential contribution of second-generation telemedicine projects in terms of mortality, morbidity, and number of hospitalizations avoided is currently under study. Their impact in terms of health economics is likewise being investigated, taking into account that the economic and social benefits brought up by telemedicine solutions were previously validated by the original telemedicine projects.

Black Garlic Improves Heart Function in Patients With Coronary Heart Disease by Improving Circulating Antioxidant Levels

Background: Black garlic (BG) has many health-promoting properties.

Objectives: We aimed to explore the clinical effects of BG on chronic heart failure (CHF) in patients with coronary heart disease (CHD).

Design: The main components of BG were measured by gas chromatography–mass spectrometry (GC–MS) and its antioxidant properties were determined by the clearance rate of free radicals. One hundred twenty CHF patients caused by CHD were randomly and evenly assigned into BG group and placebo group (CG). The duration of treatment was 6 months. Cardiac function was measured according to the New York Heart Association (NYHA) functional classification system. The following parameters were measured, including walking distance, BNP precursor N-terminal (Nt-proBNP), left-ventricular ejection fraction (LVEF) value, and the scores of quality of life (QOL). The circulating antioxidant levels were compared between two groups.

Results: There are 27 main compounds in BG with strong antioxidant properties. BG treatment improved cardiac function when compared with controls (P < 0.05). The QOL scores and LVEF values were higher in the BG group than in the CG group while the concentration of Nt-proBNP was lower in the BG group than in the CG group (P < 0.05). Circulating antioxidant levels were higher in the BG group than in the CG group. Antioxidant levels had positive relation with QOL and LVEF values, and negative relation with Nt-proBNP values.

Conclusion: BG improves the QOL, Nt-proBNP, and LVEF in CHF patient with CHD by increasing antioxidant levels.