A Decision Support System for the Treatment of Patients with Ventricular Assist Device Support

Abstract

SummaryBackground: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance.Objectives: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an inter-operable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients’ treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist’s Decision Support System (SDSS), an innovative component of the SensorART platform.Methods: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients’ data, extracting new knowledge, and making informative decisions.Results: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection.Conclusions: SDSS and its modules have been tested in a number of patients and the results are encouraging.

Big Data in cardiac surgery: real world and perspectives

“…However, the classic way for risk-prediction estimation is based on statistical methods that implies a proportional and statistically significant participation of several variables in a context where hazard is not proportional over time [ 57 ]. On a different approach, AI and Big Data may simulate the effects of decisions, and their interactions with an uncertain environment [ 58 ] and facilitate not only the prediction of specific pre-defined events.…”

Sequential Pattern Mining of Longitudinal Adverse Events After Left Ventricular Assist Device Implant

“…Some studies underlined the difference between the AEs of LVAD as short-term versus long-term therapy [14,16]. There have been also few studies investigated data-driven techniques for AEs prediction including applying Bayesian model to predict the right heart failure or survival after LVAD [21,22] and developing decision support system for patients management after LVAD [23].…”