Artifical Intelligence, Coronary Heart Disease (CHD)
Conditions
Keywords
Artificial Intelligence, Large Language Models, Coronary Heart Disease (CHD)
Brief summary
Coronary artery disease (CAD) affects an estimated 11.39 million people in China, and mortality has continued to rise since 2012. Quality-of-care data indicate persistent gaps in diagnostic and therapeutic guideline adherence, incomplete implementation of secondary prevention, and suboptimal risk-factor control. Large language models (LLMs) can rapidly retrieve and integrate literature, medical records, and multimodal clinical data, and may support clinical decision-making. However, most existing medical LLMs are general-purpose, are trained predominantly on published literature and web text rather than authentic clinical records, and have not been validated with real patient data. No disease-specific LLM for CAD currently exists, and no evaluation framework tailored to CAD has been established; existing benchmarks are general-purpose, lack specialty-specific risk grading, are not rigorously anchored to current guidelines, and emphasize literal accuracy over clinically critical reasoning steps and prescription concordance. This prospective, multicenter, observational study will enroll approximately 640 patients with confirmed or suspected CAD at Fuwai Hospital and collaborating centers. No study-specific intervention, examination, biospecimen collection, or additional follow-up visit is performed; all clinical decisions are made independently by the treating physician according to routine standards of care. For consenting participants, routinely generated clinical documentation from the index outpatient visit or hospitalization - including medical history, laboratory results, imaging and coronary angiography/coronary CT angiography reports, physician notes, online consultation records, and follow-up records - will be de-identified and used to construct a high-quality multimodal CAD dataset. This dataset will support the development of a CAD-specific large language model (CorAI), the construction of an evaluation framework spanning eight predefined clinical scenarios and multiple assessment dimensions, and external validation of the model at participating centers. The primary objectives are to characterize the completeness and quality of the constructed prospective cohort dataset, and to quantify the guideline concordance of CorAI-generated clinical recommendations as adjudicated by a blinded panel of cardiologists.
Interventions
OTHERNo Intervention: Observational Cohort
No Intervention: Observational Cohort
Sponsors
China National Center for Cardiovascular Diseases
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years; * Patients with confirmed or suspected coronary heart disease; * Complete case information (including medical history, test results, imaging, follow-up records, etc.); * Consent for the use of case data for model evaluation and research analysis.
Exclusion criteria
* Cases with severe data gaps that prevent the formation of a complete clinical scenario; * Cases where the primary diagnosis is not coronary heart disease; * Cases with serious data errors or where data cannot be anonymized.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Completeness of the constructed multimodal CAD dataset | From enrollment of the first participant through completion of dataset construction, up to 12 months | Proportion of enrolled participants whose de-identified record satisfies all pre-specified data-completeness criteria, defined as the presence of medical history, comorbidity and medication documentation, laboratory results, electrocardiography, coronary CT angiography and/or invasive coronary angiography findings, and at least one structured follow-up or discharge record sufficient to reconstruct a complete clinical scenario. Reported as a percentage with 95% confidence interval. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Guideline concordance of CorAI-generated clinical recommendations across eight predefined coronary artery disease scenarios | At completion of model development and evaluation, up to 18 months | Proportion of CorAI responses judged fully concordant with the coronary artery disease clinical practice guidelines in force at the time of adjudication, assessed on the held-out evaluation set across eight predefined clinical scenarios: (1) initial evaluation of chest pain; (2) coronary artery assessment; (3) antithrombotic therapy; (4) revascularization decision-making; (5) lipid-lowering therapy; (6) post-procedural management; (7) post-procedural complications; and (8) patient education and physician-patient communication. Each item is scored as concordant, partially concordant, or discordant by cardiologists blinded to response source using a pre-specified rubric, with disagreements resolved by an independent third adjudicator. The primary metric is the pooled percentage of items scored fully concordant across all eight scenarios, reported with a 95% confidence interval. |
Countries
China
Contacts
CONTACTProf. Gao
Outcome results
None listed