Heart score calculator test using GPT-4o and Claude in chest pain patients
Official title: Diagnostic Accuracy of Large Language Models (GPT-4o and Claude) in HEART Score Calculation and 30-Day MACE Prediction in Emergency Department Chest Pain Patients: A Prospective Observational Validation Study Against Three-Expert Consensus
Run by Marmara University Pendik Training and Research Hospital · NCT07626060 · Data updated 2026-11-12
This study tests whether two large language models can calculate HEART scores from patient notes and predict 30-day adverse heart events in adults with chest pain in the emergency department.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 690 (estimated) |
| Sponsor | Marmara University Pendik Training and Research Hospital Academic / other |
| Locations | 1 site (Istanbul, Istanbul) |
| Status | Recruiting |
| Started | June 2026 |
| Main results expected | March 2027 |
| Study ends | June 2027 |
| Registry record updated | June 23, 2026 |
| Trial ID | NCT07626060 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (18+) with non-traumatic chest pain at the emergency department who consent and can be followed for 30 days.
✕Probably not a fit
Patients with incomplete follow-up or presentations not meeting chest pain criteria may not benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
In this prospective observational study, researchers will apply GPT-4o and Claude to anonymized, unstructured patient notes to generate HEART scores and forecast 30-day major adverse cardiac events. Each patient’s data (free-text history, ECG report text, troponin, and age) are processed independently by both models with fixed, deterministic settings. The model-derived HEART scores are compared against a three-expert consensus HEART score as the reference standard, with majority voting used for component-level scoring and a fourth adjudicator for any complete disagreement. The study plans to enroll 600 adult patients presenting with non-traumatic chest pain at a Turkish emergency department, with strict blinding between LLM outputs and the reference and outcome assessments.
Why it matters
Potential benefit: If successful, the approach could streamline risk stratification in emergency departments by providing rapid HEART score estimates from clinical notes.
How similar studies have gone: Prior work has shown promise in using AI to support diagnostic scoring, but prospective, blinded accuracy in this specific HEART-score context is novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
INCLUSION CRITERIA: * Age \>=18 years * Chief complaint of non-traumatic chest pain at the emergency department * Written informed consent obtained from the patient or legally authorized representative * Availability for 30-day follow-up (reachable by telephone and/or actively registered in the e-Nabiz national health database) EXCLUSION CRITERIA: * Traumatic chest pain etiology * ST-elevation myocardial infarction (STEMI) at presentation requiring immediate reperfusion protocol * Refusal or subsequent withdrawal of informed consent * Inability to complete the mandatory 30-day follow-up period WITHDRAWAL CRITERIA: * Patient or representative requests data withdrawal after initial consent * Administrative identification of retrospective data entry after enrollment
Where it's running
-
Marmara University Pendik Training and Research Hospital
Istanbul, Istanbul, Turkey (Türkiye) Recruiting
Study contacts
- Study coordinator: Emir Unal, Assistant Professor
- Email: emirunal@gmail.com
- Phone: +905327766010
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.