Diagnostic Accuracy of GPT-4o and Claude for HEART Score Calculation in Chest Pain

This prospective observational diagnostic accuracy study evaluates whether large language models (LLMs) - GPT-4o (OpenAI, gpt-4o-2024-11-20) and Claude (Anthropic, claude-sonnet-4-6) - can accurately calculate HEART scores from unstructured Turkish clinical notes and predict 30-day major adverse cardiac events (MACE) in emergency department patients presenting with non-traumatic chest pain. The study will enroll 600 consecutive adult patients. For each patient, the same anonymized data (free-text anamnesis, ECG report text, troponin value, and age) will be independently processed by both LLMs via separate API calls with deterministic settings (temperature=0, JSON format). A three-expert consensus HEART score - derived through blinded independent scoring by three emergency medicine physicians with majority-vote adjudication - serves as the reference standard for agreement analysis. Actual 30-day MACE (all-cause death, AMI Type 1/2/4b, unplanned revascularization) determined via national health database and telephone follow-up serves as the outcome for diagnostic accuracy analysis. A secondary documentation-quality sub-study will quantify how spontaneously Turkish emergency anamnesis notes capture HEART score parameters.

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