Improved aftercare in LVAD patients: Development and feasibility of a smartphone application as a first step for telemonitoring.

For the success of the treatment with a left ventricular assist device (LVAD), both adequate self-management by the patient and an optimum level of support from the implanting hospital are crucial. A smartphone application has recently been developed within the framework of a European research project for the close monitoring of LVAD patients in order to improve upon their current aftercare situation. Using this new tool, different relevant parameters (eg, weight, international normalized ratio [INR], medication, LVAD parameters, symptoms, and photos of the driveline exit) can be sent daily or as required to a corresponding clinical application at the hospital for evaluation. The objective of this study was to monitor the functionality, acceptance, and usability of this smartphone application in LVAD patients. Prospective single-center study: in total, 13 patients (60 ± 7 years, 92% male, 1027 ± 653 days after LVAD implantation) were requested to test the application for approx. 4 weeks. At the end of the study, all entered data were evaluated and the patients were questioned regarding the acceptance and the usability. During the study period of mean 34 ± 8 days, a total of 453 data records (mean 35 ± 7 per patient) arrived at the hospital. In addition, a total of 19 photos of the driveline exit site were also sent via smartphone. The clinical application registered a total of 160 conspicuities. These comprised 126 INR deviations (target range 2.3-2.8) and 34 symptoms (mainly nosebleeds). The smartphone application functioned reliably, was well received by the patients and was graded highly for acceptance and usability. The results show that smartphone applications can definitely be used to improve aftercare in LVAD therapy in selected patients. Long-term studies are now needed to investigate the extent to which complications can be prevented, healthcare costs reduced, and quality of life increased.

Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression.

Major Depressive Disorder (MDD) has a significant impact on the daily lives of those affected. This concept paper presents a project that aims at addressing MDD challenges through innovative therapy systems. The project consists of two use cases: a multimodal neurofeedback (NFB) therapy and an AI-based virtual therapy assistant (VTA). The multimodal NFB integrates EEG and fNIRS to comprehensively assess brain function. The goal is to develop an open-source NFB toolbox for EEG-fNIRS integration, augmented by the VTA for optimized efficacy. The VTA will be able to collect behavioral data, provide personalized feedback and support MDD patients in their daily lives. This project aims to improve depression treatment by bringing together digital therapy, AI and mobile apps to potentially improve outcomes and accessibility for people living with depression.

Telemedical Concepts for Heart Failure Patients Treated with a Wearable Cardioverter Defibrillator.

Over the last few decades, implantable defibrillators have become an established method of treating malign cardiac arrhythmias. There are some situations, however, in which it would be premature to implant a permanent defibrillator. In such cases, a wearable cardioverter defibrillator (WCD) can provide temporary relief and protect patients from life-threatening cardiac arrhythmias. Treatment with WCD is now included in national and international guidelines. Nevertheless, there are still some deficits in connection with WCD, especially regarding rescue chain optimization. For example, there is currently no telemedical link in place to emergency call centers and healthcare practitioners in the case of an event. Likewise, there are still some problems with rhythm analysis, concerning both shock delivery and cardiopulmonary resuscitation (CPR). These deficits are now to be addressed within the framework of MiniDefi, a project funded by the German Federal Ministry of Education and Research (BMBF). The concepts are described here for the first time.

Adaptive Pump Speed Algorithms to Improve Exercise Capacity in Patients Supported with a Left-Ventricular Assist Device.

For the treatment of terminal heart failure, the therapy with left-ventricular assist devices has already been established. In the systems used today, pump speed does not adjust during physical activity so that cardiac output and exercise capacity remain markedly limited. It is the aim of this study to develop an automatic pump speed control based on filling pressure values in order to improve exercise capacity and quality of life in these patients. Different approaches are planned, to be tested in an in vitro patient simulator. The algorithms aim to match the pump speed with the increased venous return. In addition, preservation of aortic valve function should be taken into account.

Telemonitoring and Medical Care of Heart Failure Patients Supported by Left Ventricular Assist Devices - The Medolution Project.

Long-term survival after left ventricular assist device (LVAD) implantation in heart failure patients is mainly determined by a sophisticated after-care. Ambulatory visits only take place every 12 weeks. In case of life-threatening complications (pump thrombosis, driveline infection) this might lead to delayed diagnosis and delayed intervention. It is the intention of the international project Medolution (Medical care evolution) to develop new approaches in order to create best structures for telemonitoring of LVAD patients. In the very early period of the project a questionnaire was sent to 180 LVAD patients to evaluate the need and acceptance of telemonitoring. Thereafter, a graphical user interface (GUI) mockup was developed as one of the first steps to improve the continuous contact between the LVAD patient and the physician. As a final goal the Medolution project aims to bundle all relevant informations from different data sources into one platform in order to provide the physician a comprehensive overview of a patient's situation. In the systems background a big data analysis should run permanently and should try to detect abnormalities and correlations as well. At crucial events, a notification system should inform the physician and should provide the causing data via a decision support system. With this new system we are expecting early detection and prevention of common and partially life-threatening complications, less readmissions to the hospital, an increase in quality of life for the patients and less costs for the health care system as well.

Heart rate prediction for coronary artery disease patients (CAD): Results of a clinical pilot study.

This paper describes the results of a pilot study with cardiac patients based on information that can be derived from a smartphone. The idea behind the study is to design a model for estimating the heart rate of a patient before an outdoor walking session for track planning, as well as using the model for guidance during an outdoor session. The model allows estimation of the heart rate several minutes in advance to guide the patient and avoid overstrain before its occurrence. This paper describes the first results of the clinical pilot study with cardiac patients taking ß-blockers. 9 patients have been tested on a treadmill and during three outdoor sessions each. The results have been derived and three levels of improvement have been tested by cross validation. The overall result is an average Median Absolute Deviation (MAD) of 4.26 BPM between measured heart rate and smartphone sensor based model estimation.