Mazhude Felistas, Kramer Robert S, Hicks Anne, Jin Qingchu, Tory Melanie, Rabb Jaime B, Nourani Mahsan, Sawyer Douglas B, Winslow Raimond L
Department of Cardiovascular Services, MaineHealth Maine Medical Center, Portland, Maine.
Department of Anesthesiology and Perioperative Medicine, MaineHealth Maine Medical Center, Portland, Maine.
J Maine Med Cent. 2024 Summer;6(2). doi: 10.46804/2641-2225.1195. Epub 2024 Sep 30.
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.
心脏手术后的术后并发症对患者的短期和长期生存均有显著影响。心血管疾病是主要的健康问题，在美国占医疗支出的12%。大量心血管疾病患者接受侵入性手术，包括心脏手术，术后并发症的发生率值得关注。这些信息凸显了有效预防术后不良事件以改善治疗效果、降低发病率、缩短住院时间和降低医疗成本的必要性。
实时人工智能助力医疗（HEART）项目是一项合作努力，涉及缅因医疗集团的临床医生、日本光电的行业专家以及鲁克斯研究所的数据科学家。该项目旨在开发一种实时预测分析模型，作为心脏外科重症监护病房中照顾心脏手术后患者的临床医生的决策支持工具。该团队正在使用监督式、闭环机器学习设计来训练模型。该计划包括从缅因医疗中心接受心脏手术的一组患者中收集术前、术中和术后的静态和动态变量。这些变量，包括血液制品输注数据以及使用的强心和血管活性药物数据，正从电子健康记录传输到数据仓库。该模型将预测以下不良后果：急性肾损伤、肾衰竭、术后新发房颤、通气时间延长、再次手术、手术死亡率、谵妄、中风、胸骨深部伤口感染以及住院时间延长。
HEART团队成功建立了数据收集基础设施。数据收集和验证工作正在进行，重点是准确性和完整性。
该项目将通过开发一个用户友好的实时界面推进，纳入手术室和心脏外科重症监护病房临床医生的反馈，以确保该技术的实用性和可接受性。这个界面将实时提供不良后果预测，支持临床决策，并成为患者护理的常规组成部分。
