A Privacy-Preserving OCR-LLM System for Coronary Syndrome Subtyping From Admission HPI: Multicenter Validation in China and the US

This study develops and validates a privacy-preserving OCR-LLM pipeline that converts admission history of present illness (HPI) records into structured coronary syndrome subtypes (STEMI, NSTEMI, unstable angina, and chronic coronary syndrome). The system first extracts text from de-identified HPI images using locally deployed OCR, then applies large language models with a fixed diagnostic prompt to generate subtype classification and evidence. Performance is evaluated in an internal validation cohort and multiple external datasets covering heterogeneous EHR templates, emergency department cases, and an English dataset from MIMIC-IV. A clinician usability study assesses changes in diagnostic accuracy and time with and without tool assistance.

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Key information

Conditions

Coronary Artery Disease (CAD) (E.G., Angina, Myocardial Infarction, and Atherosclerotic Heart Disease (ASHD)) Acute Coronary Syndrome Acute Coronary Syndromes Angina Pectoris Arterial Occlusive Diseases Arteriosclerosis Cardiovascular Diseases Chest Pain Coronary Artery Disease Coronary Disease Heart Diseases Infarction Ischemia Myocardial Infarction Myocardial Ischemia Necrosis Neurologic Manifestations Non-ST Elevated Myocardial Infarction Non-ST-Segment Elevation Myocardial Infarction (NSTEMI) Pain Pathologic Processes Pathological Conditions, Signs and Symptoms ST Elevation Myocardial Infarction ST-segment Elevation Myocardial Infarction (STEMI) Signs and Symptoms Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Hospital encounters with admission HPI documenting sym

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evant to coronary syndrome subtyping.

Cases with sufficient documentation to assign one of four target subtypes (STEMI, NSTEMI, UA, CCS) by adjudication.

Exclusion criteria

Unclear subtype or incomplete/uncertain time information preventing gold standard assignment.

Non-CHD primary reason for admission after screening (for MIMIC-IV cohort).

Treatment and study plan

OCR-Prompt-LLM Information Extraction and Classification Workflow (OCR-Prompt-LLM)

Device

An automated clinical data management workflow integrating Optical Character Recognition (OCR), optimized prompt engineering, and large language models (LLMs). The system processes unstructured inpatient/ED records (primarily admission history of present illness and related narrative text) to extract prespecified key clinical indicators (e.g., left ventricular ejection fraction, coronary syndrome subtype, medications) and to classify cases into prespecified coronary artery disease categories (e.g., unstable angina, STEMI, NSTEMI, chronic coronary syndrome). The workflow outputs structured fields and a classification result with supporting evidence excerpts.

Manual Clinical Data Review

Device

Standard manual process in which experienced clinicians review patient medical records and extract the same prespecified clinical indicators and coronary artery disease categories using routine clinical judgment and documentation review. This manual abstraction serves as the human benchmark for comparing diagnostic accuracy, completeness, and operational efficiency against the automated OCR-Prompt-LLM workflow.

Primary outcomes

Overall classification accuracy

Time frame: 1 month

Time Frame: Up to completion of dataset evaluation (internal + external cohorts)

Description: Proportion of cases with correct subtype (STEMI/NSTEMI/UA/CCS) compared with expert-adjudicated gold standard.

Study contacts

Contact information is provided by the study sponsor or research team.

Xiaojin Gao, Dr

CONTACT

[email protected]

+86010 88322415

Sponsors and collaborators

Lead sponsor

China National Center for Cardiovascular Diseases

Other Gov

Registry information

Official study title

Development and Multicenter Validation of a Privacy-Preserving OCR-LLM Pipeline for Four-Subtype Coronary Syndrome Classification Using Admission HPI Across Heterogeneous EHR Systems

Acronym: OCR-LLM-CHD

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2026
First posted: Mar 4, 2026
Registry last updated: Mar 4, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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