Predictive Analytics in Cardiothoracic Care: Enhancing Outcomes with the Healthcare Enabled by Artificial Intelligence in Real Time (HEART) Project

TL;DR

The HEART project uses AI to predict post-surgery complications in cardiac patients, aiming to improve outcomes and reduce costs.

Contribution

A real-time predictive analytics model for cardiothoracic care using machine learning and clinician feedback.

Findings

A data-collecting infrastructure was successfully established for predictive modeling.

The model will predict multiple adverse outcomes like kidney injury and atrial fibrillation.

A user-friendly interface is being developed for real-time clinical decision support.

Abstract

Postoperative complications after cardiac surgery significantly impact both the short-term and long-term survival of patients. Cardiovascular diseases are a major health concern, accounting for 12% of health expenditures in the United States. A substantial number of patients with cardiovascular disease undergo invasive procedures, including cardiac surgery, and the incidence of postoperative complications is notable. This information underscores the need to effectively prevent postoperative adverse events to improve outcomes, reduce morbidity, shorten hospital stays, and lower health care costs.

The Healthcare Enabled by Artificial Intelligence in Real Time (HEART) project is a collaborative effort involving clinicians from MaineHealth, industry experts from Nihon Kohden, and data scientists from the Roux Institute. The project aims to develop a real-time predictive analytics model as a decision support tool for clinicians in the cardiothoracic intensive care unit who care for patients after cardiac surgery. The team is using a supervised, closed-loop, machine learning design to train the model. The initiative involves collecting static and dynamic preoperative, intraoperative, and postoperative variables from a cohort of patients undergoing cardiac surgery at Maine Medical Center. These variables, including data on blood product transfusions and inotropic and vasoactive medications administered, are being transmitted from the electronic health record to a data warehouse. The model will predict the following adverse outcomes: acute kidney injury, renal failure, new onset postoperative atrial fibrillation, prolonged ventilation, reoperation, operative mortality, delirium, stroke, deep sternal wound infection, and extended hospital length of stay.

The HEART team successfully established a data-collecting infrastructure. Data collection and validation are ongoing, with an emphasis on accuracy and completeness.

The project will advance by developing a user-friendly, real-time interface, incorporating feedback from clinicians in the operating room and cardiothoracic intensive care unit to ensure practicality and acceptance of the technology. This interface will provide adverse outcome predictions in real time, support clinical decision-making, and become a regular part of patient care.

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