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TRUVACE RECORD VERSION
record: TRV-2026-2147
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-22T06:53:43.769858Z
status: published
lens: g_space
sector: health
headline: A randomized controlled trial of a novel AI-based prognostic decision support tool integrated into the electronic health record for ischemic heart disease: Rationale and design of the PM Heart trial
dek: Background Ischemic heart disease (IHD) is the leading global cause of years of life lost. Although the clinical presentation and progression of IHD differ widely, clinical management is relatively uniform. To improve risk stratification and clinical management, we developed a decision support tool - the PMHeart IHD Algorithm (also named PMHNet) - based on extensive clinical, registry and biochemical data. This algorithm automatically calculates one-year survival following an IHD event. We hypothesize that prese…
gain_title: An AI algorithm integrated into the hospital record calculates one-year survival after an ischemic heart disease event, and a randomized trial is testing whether showing it to physicians improves patient outcomes.
problem_title: (none)
trace_subject: (none)
gain_reading: An AI algorithm integrated into the hospital record calculates one-year survival after an ischemic heart disease event, and a randomized trial is testing whether showing it to physicians improves patient outcomes.
gain_evidence: This algorithm automatically calculates one-year survival following an IHD event. | presenting the prognoses calculated by the PMHeart IHD Algorithm to the treating physicians will improve both the clinical management and outcome of the individual patient
problem_reading: (none)
problem_evidence: (none)
quick_read: The article describes the rationale and design of the PM Heart trial in Denmark. Researchers built the PMHeart IHD Algorithm from clinical, registry and biochemical data and connected it to the Epic-based electronic health record used in two Danish regions.

The trial matters because it will give controlled evidence on whether an AI prognosis changes care for coronary artery disease patients, rather than relying on assumptions. It is a protocol, so no results exist yet, and any benefit remains a hypothesis until follow-up ends.
limitation: The article presents only the trial rationale and design, with follow-up still expected to finish in 2026, so it provides no outcome results on whether the tool improves care.
tag: Evidence-backed gain
key_points: The algorithm automatically estimates one-year survival after an ischemic heart disease event. | The trial is multicenter, prospective, randomized and open-label, with 1:1 randomization immediately after coronary angiography. | Patients are randomized to have the calculated prognosis shown or hidden in the electronic health record. | The primary outcome combines re-admission within 30 days and one-year survival after randomization. | A secondary aim is to learn how cardiology decision support tools are implemented, received and used in practice.
rundown: Ischemic heart disease is described as the leading global cause of years of life lost. Presentation and progression vary widely, yet clinical management is relatively uniform, which motivated a tool to improve risk stratification.

The algorithm is integrated into the Sundhedsplatformen EPIC system. The trial automatically enrolls and randomizes hospitalized coronary artery disease patients at cardiology departments in the Capital Region and Region Zealand.

Inclusion began in September 2023 and follow-up is expected to finish in 2026. The article presents the design, so it reports no measured effect on patient outcomes.
sources:
- peer_reviewed | American Heart Journal | https://doi.org/10.1016/j.ahj.2026.107631 | 2026-10-18
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