Coronary Artery DiseaseOPAL-CADNCT07499830
Performance of an OCR-Prompt-LLM Integrated Workflow for Extracting Multi-dimensional Clinical Data in Ischemic Heart Disease
An observational study of Coronary Artery Disease and Artificial Intelligence (AI), run by China National Center for Cardiovascular Diseases at 1 site in China.
Trial summary
This research aims to evaluate a comprehensive AI-driven workflow for both clinical data extraction and diagnostic classification in coronary artery disease (CAD). Leveraging OCR and Large Language Models (LLMs), the system is designed to extract ten key clinical parameters (such as LVEF and lab results) and provide… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Coronary Artery Disease, Artificial Intelligence (AI), Data Collection
- What is being tested?
- OCR-Prompt-LLM Information Extraction Workflow and Manual Clinical Data Review
- Type of treatment
- Medical device
- How many people
- 308 participants
- Where
- 1 siteChina
- Study dates
- 23 Feb 2026to 2 Mar 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: he study population consists of 308 patients diagnosed with various subtypes of coronary artery disease (CAD). The cohort is derived from two major clinical studies: the AIM-CHD study (for pilot testing and prompt optimization) and the SMART-CHD study (for internal validation), both conducted at Fuwai Hospital. Additionally, an external validation cohort is included, comprising patients from 8 independent clinical sub-centers across China to ensure geographical and institutional diversity. The population covers a spectrum of CAD presentations, including Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS), providing a robust dataset for evaluating AI-driven diagnostic and data extraction performance.
Who may be able to join
- Patients aged 18 years and older.
- Clinical records of patients who were previously enrolled in the AIM-CHD (for the pilot/prompt optimization set) or SMART-CHD (for the internal validation cohort) studies.
- Patients diagnosed with, or suspected of having, coronary artery disease (CAD), including subtypes: Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS).
Who cannot join
- Clinical records with severe data fragmentation or missing more than 50% of the key clinical indicators.
- Handwritten medical records or low-quality scans that are illegible for Optical Character Recognition (OCR) processing.
- Duplicate records or records with conflicting "Gold Standard" labels that cannot be reconciled by the expert committee.
Trial locations and contacts
1 site in 1 country.
About this study
This research aims to evaluate a comprehensive AI-driven workflow for both clinical data extraction and diagnostic classification in coronary artery disease (CAD). Leveraging OCR and Large Language Models (LLMs), the system is designed to extract ten key clinical parameters (such as LVEF and lab results) and provide diagnostic subtypes (UA, STEMI, NSTEMI, CCS) directly from unstructured inpatient records. A man-machine comparative trial will be conducted using a test set of 308 patients, where the performance of the LLM-based workflow will be benchmarked against the average diagnostic accuracy and processing time of seven clinical physicians. The findings will provide evidence for the feasibility of using LLMs to enhance clinical data structuring and diagnostic efficiency in cardiology.
- Being tested
- OCR-Prompt-LLM Information Extraction Workflow, Manual Clinical Data Review
- Medical subject headings
- Coronary Artery Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
OCR-Prompt-LLM Information Extraction WorkflowDevice
The intervention is an automated clinical data management system integrating Optical Character Recognition (OCR), optimized Prompt Engineering, and Large Language Models (LLMs). The workflow processes unstructured inpatient records to extract 10 key clinical indicators (e.g., LVEF, CAD subtypes, medications) and classifies the patient into specific coronary artery disease categories (UA, STEMI, NSTEMI, CCS)
Manual Clinical Data ReviewDevice
Standard manual process where experienced clinical physicians collect and interpret patient information from medical records. This serves as the human benchmark for comparing diagnostic accuracy and operational efficiency.
| Study group | Role | What they receive |
|---|---|---|
| Test Cohort | This group consists of 50 patient records from the AIM-CHD Study at Fuwai Hospital. These data are specifically utilized for refining OCR processing and optimizing Prompt Engineering for the LLM-based workflow. Receives: Device: OCR-Prompt-LLM Information Extraction Workflow; Device: Manual Clinical Data Review | |
| Internal Validation Cohort | This cohort includes 188 clinical cases sourced from the SMART-CHD Study at Fuwai Hospital. These records serve as the primary internal benchmark to evaluate the diagnostic and extraction accuracy of the LLM workflow against the established ground truth. Receives: Device: OCR-Prompt-LLM Information Extraction Workflow; Device: Manual Clinical Data Review | |
| External Validation Cohort | This cohort comprises 70 patient records collected from 8 independent sub-centers (excluding Fuwai Hospital) to assess the generalizability and robustness of the model across diverse clinical environments and different medical record formats. Receives: Device: OCR-Prompt-LLM Information Extraction Workflow; Device: Manual Clinical Data Review |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall Diagnostic and Extraction Accuracy Rate
To calculate the overall accuracy rate of the LLM-based workflow across 308 cases (including the pilot set, internal validation cohort, and external validation cohort) for 10 clinical indicators (e.g., LVEF, blood glucose, etc.) and 4 diagnostic subtypes of coronary artery disease. Accuracy is defined as the proportion of cases where the LLM's extraction or diagnostic results are perfectly consistent with the 'Gold Standard' established by human clinical experts.
Measured: Through study completion, an average of 3 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Mar 2026
- Study start23 Feb 2026
- Primary completion1 Mar 2026
- Study completion2 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Coronary Artery DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 308 participants (actual)
Sponsor and investigators
- Lead sponsor
- China National Center for Cardiovascular Diseases
- Sponsor type
- Other government
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07499830
- Sponsor's protocol ID
- CAD-LLM-2025-01
- Acronym
- OPAL-CAD
- Status last verified
- Feb 2026
- First submitted
- 24 Mar 2026
- Last updated
- 30 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: To protect patient privacy and comply with the data management policies of the participating institutions (Fuwai Hospital and sub-centers), individual participant data will not be made publicly available. However, aggregated study results and statistical analyses will be included in the final publication.
Study at a glance
From the registry record, last updated by the sponsor on 30 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Performance of an OCR-Prompt-LLM Integrated Workflow for Extracting Multi-dimensional Clinical Data in Ischemic Heart Disease
- ClinicalTrials.gov ID
- NCT07499830
- Acronym / sponsor ID
- OPAL-CAD / CAD-LLM-2025-01
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 308 participants (actual)
- Lead sponsor
- China National Center for Cardiovascular DiseasesOther government
- Study start
- 23 Feb 2026 (actual)
- Primary completion
- 1 Mar 2026 (actual)
- Study completion
- 2 Mar 2026 (actual)
- First posted
- 30 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This research aims to evaluate a comprehensive AI-driven workflow for both clinical data extraction and diagnostic classification in coronary artery disease (CAD). Leveraging OCR and Large Language Models (LLMs), the system is designed to extract ten key clinical parameters (such as LVEF and lab results) and provide diagnostic subtypes (UA, STEMI, NSTEMI, CCS) directly from unstructured inpatient records. A man-machine comparative trial will be conducted using a test set of 308 patients, where the performance of the LLM-based workflow will be benchmarked against the average diagnostic accuracy and processing time of seven clinical physicians. The findings will provide evidence for the feasibility of using LLMs to enhance clinical data structuring and diagnostic efficiency in cardiology.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Test Cohort | – | Device: OCR-Prompt-LLM Information Extraction Workflow, Device: Manual Clinical Data ReviewThis group consists of 50 patient records from the AIM-CHD Study at Fuwai Hospital. These data are specifically utilized for refining OCR processing and optimizing Prompt Engineering for the LLM-based workflow. |
| Internal Validation Cohort | – | Device: OCR-Prompt-LLM Information Extraction Workflow, Device: Manual Clinical Data ReviewThis cohort includes 188 clinical cases sourced from the SMART-CHD Study at Fuwai Hospital. These records serve as the primary internal benchmark to evaluate the diagnostic and extraction accuracy of the LLM workflow against the established ground truth. |
| External Validation Cohort | – | Device: OCR-Prompt-LLM Information Extraction Workflow, Device: Manual Clinical Data ReviewThis cohort comprises 70 patient records collected from 8 independent sub-centers (excluding Fuwai Hospital) to assess the generalizability and robustness of the model across diverse clinical environments and different medical record formats. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- he study population consists of 308 patients diagnosed with various subtypes of coronary artery disease (CAD). The cohort is derived from two major clinical studies: the AIM-CHD study (for pilot testing and prompt optimization) and the SMART-CHD study (for internal validation), both conducted at Fuwai Hospital. Additionally, an external validation cohort is included, comprising patients from 8 independent clinical sub-centers across China to ensure geographical and institutional diversity. The population covers a spectrum of CAD presentations, including Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS), providing a robust dataset for evaluating AI-driven diagnostic and data extraction performance.
Key inclusion criteria (3 listed)
- Patients aged 18 years and older.
- Clinical records of patients who were previously enrolled in the AIM-CHD (for the pilot/prompt optimization set) or SMART-CHD (for the internal validation cohort) studies.
- Patients diagnosed with, or suspected of having, coronary artery disease (CAD), including subtypes: Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS).
Key exclusion criteria (3 listed)
- Clinical records with severe data fragmentation or missing more than 50% of the key clinical indicators.
- Handwritten medical records or low-quality scans that are illegible for Optical Character Recognition (OCR) processing.
- Duplicate records or records with conflicting "Gold Standard" labels that cannot be reconciled by the expert committee.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| OCR-Prompt-LLM Information Extraction WorkflowThe intervention is an automated clinical data management system integrating Optical Character Recognition (OCR), optimized Prompt Engineering, and Large Language Models (LLMs). The workflow processes unstructured inpatient records to extract 10 key clinical indicators (e.g., LVEF, CAD subtypes, medications) and classifies the patient into specific coronary artery disease categories (UA, STEMI, NSTEMI, CCS) | Device | External Validation Cohort, Internal Validation Cohort, Test Cohort |
| Manual Clinical Data ReviewStandard manual process where experienced clinical physicians collect and interpret patient information from medical records. This serves as the human benchmark for comparing diagnostic accuracy and operational efficiency. | Device | External Validation Cohort, Internal Validation Cohort, Test Cohort |
Primary outcome
Overall Diagnostic and Extraction Accuracy Rate
Time frame: Through study completion, an average of 3 months.
To calculate the overall accuracy rate of the LLM-based workflow across 308 cases (including the pilot set, internal validation cohort, and external validation cohort) for 10 clinical indicators (e.g., LVEF, blood glucose, etc.) and 4 diagnostic subtypes of coronary artery disease. Accuracy is defined as the proportion of cases where the LLM's extraction or diagnostic results are perfectly consistent with the 'Gold Standard' established by human clinical experts.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Mar 2026.
- Individual participant dataSharing planned: No. To protect patient privacy and comply with the data management policies of the participating institutions (Fuwai Hospital and sub-centers), individual participant data will not be made publicly available. However, aggregated study results and statistical analyses will be included in the final publication.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Artery Disease and Artificial Intelligence (AI), run by China National Center for Cardiovascular Diseases at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall Diagnostic and Extraction Accuracy Rate (time frame: Through study completion, an average of 3 months.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Mar 2026.