This project aims to produce and beta-test a clinical decision support application for end- stage heart failure, titled CHRiSS: Cardiac Health Risk Stratification System. It will be designed for patients with progressive heart failure who may eventually become candidates for ventricular assist device (VAD) therapy. The software will be build upon existing machine learning and data mining technology, developed by the PI and colleagues, designed to predict 90-day mortality following VAD implantation. This Phase- 1 effort will entail development of graphic user interfaces for both patients and clinicians to incorporate personalized prognostic information into a functional decision-support utility. The inference algorithm will also be expanded to encompass adverse events, and risk of readmission. In this Phase-1 feasibility project, CHRiSS will be programmed and beta-tested at West Penn Allegheny Health System (WPAHS). This will entail semi- structured interviews to collect relevant expert knowledge, and mining the electronic medical records (EMR) to calibrate the prognostic algorithm to the institution-specific data. Successful completion of this Phase I project will lead to a multi-center trial in which the software is fully validated. Ultimate succes of this project will result in a software application that will optimize the benefit of VAD therapy to public health, while reducing cost by reducing the unacceptably high rate of adverse events and unnecessary readmissions.
Public Health Relevance Statement: Public Health Relevance: Narrative Heart-assist devices are becoming more commonly used to treat people with severe cardiac failure - who otherwise have few options to return to a normal life. It is estimated that tens of thousands of Americans could benefit from this therapy annually; however, only a few thousand are performed per year. This project aims to develop a software application to better inform patients and doctors about the risks and benefits of this therapy. It will assist patients and doctors to work together when weighing options - with respect to both survival and quality of life. The long-term benefits would be to improve the efficiency of delivering this therapy, thereby reducing the cost, and expanding its distribution to the many people that need it.
NIH Spending Category: Assistive Technology; Bioengineering; Cardiovascular; Clinical Research; Cost Effectiveness Research; Health Services; Heart Disease; Networking and Information Technology R&D; Patient Safety
Project Terms: Address; Adoption; Adverse event; Algorithms; American; Benefits and Risks; Businesses; Cardiac; Caring; Clinical; Communication; computer human interaction; Computer software; Computerized Medical Record; cost; Cost Effectiveness Analysis; Cost Savings; Data; data mining; design; Development; Diagnosis; Educational aspects; Evaluation; Event; Feasibility Studies; Foundations; graphical user interface; Guidelines; Health; Health Care Costs; Health system; Heart failure; Heart-Assist Devices; Hospitals; implantation; improved; Incidence; Institution; Intensive Care; Interview; Knowledge; Lead; Length of Stay; Life; Life Style; Machine Learning; Marketing; Medical; Medicare; Methods; Metric; Mining; Modeling; Mortality Vital Statistics; Needs Assessment; Outcome; outcome forecast; Outpatients; patient population; Patients; Phase; Postoperative Period; preference; prognostic; programs; prototype; public health medicine (field); public health relevance; Quality of life; Risk; satisfaction; Savings; Software Tools; Staging; Stratification; Structure; success; System; Technology; Testing; tool; trend; Update; usability; ventricular assist device; Work
