This project aims to introduce a clinical decision support system, CORA (Cardiac Outcomes Risk Assessment) for advanced heart failure (HF) a devastating disease affecting over 5 million Americans, and a leading cause of Medicare hospital admissions. The project specifically aims to moderate the deployment ventricular assist devices (VADs) which have been recently approved as destination (DT) such that patients most likely to benefit will be prioritized, while those better served with palliative care will be spare from unnecessary risks of debilitating side effects. The proposed system will leverage proven prognostic models based on advanced data mining and machine learning algorithms, and be packaged as a user-friendly computer application that would be used at key decision points in the course of treatment. It would provide both physician and patient with prognostic assessment based on the fusion of: patient-specific data, expert knowledge, and retrospective meta-analysis. This four-year project will develop a multi-platform software application that integrates modules for: forecasting, decision support, communication, decision making and visualization of data. This will be achieved through completion of four specific aims: (SA1) Expand existing inference (prognostic) algorithms and develop expert decision models to compute personalized estimate of life expectancy and risk of adverse events; (SA2) Create a computer application to assist patients to navigate their treatment options as they reflect their personal preferences and beliefs; (SA3) Incorporate these prognostic and decision models into user-friendly user interfaces for physicians, patients and their family caregivers; and (SA4) Conduct a multi- center study with respect to both qualitative and quantitative metrics of efficacy related to mortality, morbidity and quality of life. Training data capitalizing upon the NIH-funded Interagency Registry for Mechanical Circulatory Support (INTERMACS) as well as recently published guidance documents by the American Heart Association (AHA) and International Society of Heart & Lung Transplantation (ISHLT.) This project will involve a multi-disciplinary group of specialties including: bioengineering, advanced heart failure cardiology, cardiac surgery, computer science (machine learning/data mining), medical ethics, and palliative care. Carnegie Mellon University, one of the Nation's leading institutions for Computer Science will coordinate this project to assure a productive collaboration with four clinical sites: Allegheny Health Network (Pittsburgh), U. Colorado Denver, Duke University, and Harvard/Brigham & Women's hospital. By achieve consensus amongst stakeholders, we hope to assure the acceptance of CORA into practice and ultimately improve the efficiency, efficacy, and cost- effectiveness of the treatment of end-stage heart failure.
Congestive heart failure (CHF) is a debilitating disease affecting over 5 million Americans, and a leading cause of Medicare hospital admissions. Treatment can be very complicated, and for some, may involve advanced technologies like heart-assist devices and stents, and for others palliative care. This project will develop a software application for patients and their doctors to work together in choosing the best treatment strategy.
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| Lohmueller, Lisa C; Kanwar, Manreet K; Bailey, Stephen et al. (2018) Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center. Front Med (Lausanne) 5:277 |
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| Loghmanpour, Natasha A; Kormos, Robert L; Kanwar, Manreet K et al. (2016) A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy. JACC Heart Fail 4:711-21 |
| Loghmanpour, Natasha A; Kanwar, Manreet K; Druzdzel, Marek J et al. (2015) A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality. ASAIO J 61:313-23 |
