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临床试验/NCT07837765
NCT07837765尚未招募不适用

HEART-AI: Heart tEam Decision-making Assisted by Revaz AI Trial

University of Calgary2 个研究点 分布在 1 个国家目标入组 712 人开始时间: 2027年4月1日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
尚未招募
入组人数
712
试验地点
2
主要终点
5-year post-treatment 5-point MACE

研究概览

简要总结

Coronary artery disease, the narrowing of the blood vessels that supply the heart, is the leading cause of death worldwide. About 2.4 million Canadian adults live with it. There are three main ways to treat it: opening the narrowed artery with a stent, heart bypass surgery, or medications alone. For many patients the best choice is clear, but for people with complicated disease or multiple health conditions, it can be genuinely difficult to know which treatment will lead to the best outcome. For these complex cases, hospitals bring together a "heart team", a group of heart specialists including cardiologists and heart surgeons, to discuss the case and recommend the best treatment. Even so, studies show that different doctors, teams, and hospitals often make different decisions for similar patients, which suggests there is room to make these decisions better.

The investigators developed a computer program called Revaz AI. It uses artificial intelligence (AI) that has learned from the health records of more than 38,000 past patients with coronary artery disease in Alberta. When given a new patient's health information, Revaz AI estimates that person's risk of dying or having a major heart problem, such as a heart attack, stroke, heart failure, or the need for another procedure, over the next 90 days to 5 years, under each of the three treatment options. In testing on past patient records, Revaz AI's predictions were more accurate than the risk scores doctors currently use. A separate study estimated that using it could improve treatment choices and reduce health care costs. However, Revaz AI has never been tested in real time with real patients.

The central question of this trial is: Do patients do better over 5 years when the heart team can see Revaz AI's risk estimates during its discussion, compared with the usual way of deciding? The investigators will also look at whether the tool changes treatment decisions, whether it makes the team more confident in its recommendations, whether patients feel better in daily life, and whether it saves the health care system money.

The study will take place at two Canadian hospitals: Foothills Medical Centre in Calgary and the University of Ottawa Heart Institute. Together, their heart teams discuss about 900 patients each year. The investigators plan to enroll 712 adult patients whose cases have been referred to the heart team. Each patient who agrees to join will be assigned by chance, like a coin flip, to one of two groups. In one group, Revaz AI's risk estimates for that patient will be shown on screen during the heart team's virtual meeting, alongside all the usual information. In the other group, the heart team will discuss the case in the usual way, without Revaz AI. Assigning patients by chance ensures the two groups are alike, so any difference in results can be traced to Revaz AI. Importantly, Revaz AI does not make decisions: in both groups, the heart team makes the recommendation, and each patient and their doctor make the final treatment choice together, just as they do now. The investigators will then follow every patient for 5 years. Most of the follow-up information - hospital stays, procedures, and major heart problems - will come from existing health databases, so patients do not need extra hospital visits. The investigators will also contact patients four times (at about 3 months, 1 year, 3 years, and 5 years) with short questionnaires about their symptoms and quality of life.

The main measure is the proportion of patients who have a major heart problem or die within 5 years of treatment. Based on past data, about 40% of similar patients experience one of these events within 5 years. The study is designed to detect whether Revaz AI can reduce this to 30%. Independent statisticians will analyze the results, and an independent safety board of experts not involved in the study will review the data regularly and can stop the study if any safety concern appears.

The study is led by university researchers with heart specialists at both hospitals. The lead researcher who co-founded the company that makes Revaz AI will stay at arm's length from the study's conduct and analysis, which will be handled by independent team members. All patient information will be coded and stored on secure hospital and university servers.

No AI tool for choosing coronary artery disease treatment has ever been tested in a rigorous trial like this. If Revaz AI improves outcomes, patients with complex heart disease could receive treatment decisions tailored to them, and the tool could be adopted by hospitals across Canada and beyond. If it does not, the study will still teach the medical community valuable lessons about how doctors and AI tools work together - knowledge that will shape the safe use of AI in health care.

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Other
盲法
None

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者
否

入选标准

  • •Patient Eligibility:
  • •Inclusion Criteria:
  • •adult patients (≥18 years) referred for heart team discussion of coronary artery disease treatment at a participating center
  • •diagnosis of obstructive coronary artery disease, defined as any stenosis ≥50% in the left main coronary artery and/or ≥70% in any other coronary artery, as confirmed by diagnostic angiography

排除标准

  • •any subsequent heart team referral for patients who are already enrolled in the trial
  • •Heart Team Clinician Eligibility:
  • •Inclusion Criteria:
  • •any staff clinician (interventional cardiologist, cardiac surgeon, or non-invasive cardiologist) who participates in heart team discussions and contributes to coronary artery disease treatment recommendations at the participating centers
  • •Exclusion Criteria:
  • •trainees such as fellows or residents

研究组 & 干预措施

Revaz AI-supported coronary artery disease treatment decision-making

Experimental

Coronary artery disease treatment decision-making will be supported by Revaz AI insights

干预措施: Revaz AI (Behavioral)

Control

No Intervention

Usual coronary artery disease treatment decision-making without Revaz AI support

结局指标

主要结局

5-year post-treatment 5-point MACE

时间窗: 5 years post-treatment

5-point MACE includes myocardial infarction, heart failure, stroke, repeat revascularization, and all-cause mortality.

次要结局

  • 90 days, 1 year, and 3 years post-treatment 5-point MACE(90 days, 1 year, and 3 years post-treatment)
  • Patient-reported Health-related Quality of Life (SAQ-7) at 90 days, 1 year, 3 years, and 5 years post-treatment(90 days, 1 year, 3 years, and 5 years post-treatment)
  • Patient-reported Health-related Quality of Life (EQ-5D-5L) at 90 days, 1 year, 3 years, and 5 years post-treatment(90 days, 1 year, 3 years, and 5 years post-treatment)
  • The lead clinician's level of confidence in the heart team treatment recommendation(Immediately after the heart team meeting)
  • Concordance between the heart team's final treatment recommendation and the treatment predicted by Revaz AI to yield the lowest 5-year MACE risk(Immediately after the heart team meeting)
  • Heart team consensus(Immediately after the heart team meeting)
  • 90-day hospital readmission(90 days post-treatment)
  • Total health care costs incurred during the post-treatment 5-year period(5 years post-treatment)

研究者

申办方类型
Other
责任方
Sponsor

研究点 (2)

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