A Privacy-Preserving OCR-LLM System for Coronary Syndrome Subtyping From Admission HPI: Multicenter Validation in China and the US
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: OCR-Prompt-LLM Information Extraction and Classification Workflow (OCR-Prompt-LLM), Manual Clinical Data Review.
- Who it may be relevant to
- Registry conditions: Coronary Artery Disease (CAD) (E.G., Angina, Myocardial Infarction, and Atherosclerotic Heart Disease (ASHD)), Acute Coronary Syndromes, ST-segment Elevation Myocardial Infarction (STEMI), Non-ST-Segment Elevation Myocardial Infarction (NSTEMI). Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official 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
Overview
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.
Interventions
- Device OCR-Prompt-LLM Information Extraction and Classification Workflow (OCR-Prompt-LLM)
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. - Device Manual Clinical Data Review
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 outcome measures
- Overall classification accuracy [Time frame: 1 month]
Eligibility criteria
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.
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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).
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Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07449429 · CAD-LLM-002 · Sponsor