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NCT Number: NCT07626060

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Marmara University Pendik Training and Research Hospital

Istanbul, 34870, Turkey (Türkiye)

Location status: Recruiting

Location contact

Emir ünal

CONTACT

[email protected]

05327766010

Emre Kudu

SUB_INVESTIGATOR

Erhan Altunbas

SUB_INVESTIGATOR

Sinan Karacabey

SUB_INVESTIGATOR

About this study

AI SYSTEM SPECIFICATIONS AND PROMPT PROTOCOL Two distinct large language models (LLMs) will be evaluated as index tests: OpenAI GPT-4o (model string: gpt-4o-2024-11-20) and Anthropic Claude (model string: claude-sonnet-4-6). To ensure reproducibility and eliminate stochastic variation, both models will be accessed via standardized API calls using deterministic parameters (temperature = 0, max_tokens = 500, and strict JSON response format). The exact system prompt layout will be locked prior to initialization, and its integrity will be verified using a SHA-256 cryptographic hash. The models will evaluate each patient record independently in zero-shot isolation, with no cross-contamination or conversational history retention between runs.

REFERENCE STANDARD CONSENSUS PROTOCOL The reference standard consists of a structured consensus HEART score established by three independent emergency medicine physicians (each possessing >=3 years of clinical experience and specific training on HEART score criteria). The physicians will review the anonymized clinical charts while remaining strictly blinded to the LLM outputs and the final 30-day MACE outcomes. For each of the 5 HEART components (scored 0, 1, or 2), a majority vote (2/3 agreement) will determine the final component score. In the event of complete disagreement across all three reviewers on a specific component, a fourth independent adjudicator will resolve the tie.

INDETERMINATE RESULTS MANAGEMENT

In strict compliance with STARD-AI 2025 guidelines, cases with missing or uninterpretable parameters within the free-text clinical notes will be classified into predefined indeterminate tiers:

  • Complete Cases: 0 indeterminate components (eligible for primary diagnostic accuracy analysis).
  • Partial Indeterminate: Exactly 1 missing component preventing definitive automatic calculation.
  • Full Indeterminate: >=2 missing components. The proportion of indeterminate classifications will be quantified for both LLMs and evaluated alongside the routine documentation quality of the charts.

STATISTICAL ANALYSIS AND AGREEMENT WEIGHTING Statistical power and sample size calculation are based on the Hanley-McNeil methodology for the Area Under the ROC Curve (AUC). To achieve an expected AUC of 0.85 with a non-inferiority margin of 0.05, a power of 80%, and a two-sided alpha of 0.05, the primary complete-case analysis requires 600 evaluable patients. Accounting for an anticipated 15% indeterminate rate, a total enrollment target of 690 patients is set. Inter-rater agreement between each LLM and the expert consensus will be computed using quadratic weighted Cohen's Kappa for the ordinal total HEART score (0-10) and linear weighted Kappa for individual components (0-2). Diagnostic performance metrics (sensitivity, specificity, PPV, NPV) will be calculated at prespecified binary (>=4) and trimodal thresholds with 95% Wilson confidence intervals. Pairwise comparison of AUC values between GPT-4o and Claude will be executed using the DeLong test.

DATA ANONYMIZATION AND PRIVACY To ensure full compliance with local personal data protection legislation (KVKK), all free-text emergency department notes will undergo strict de-identification. Patient names, institutional ID numbers, precise dates, and specific demographic identifiers will be stripped entirely before formatting the data payload for API transmission.

PATIENT AND PUBLIC INVOLVEMENT BEYANI Patient and public involvement was not applicable to this study as it involves the analysis of routinely collected clinical data.

Who can participate

Healthy volunteers accepted: No

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

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

Treatment and study plan

GPT-4o HEART Score Calculator

Other

OpenAI GPT-4o (model: gpt-4o-2024-11-20, temperature=0, max_tokens=500, response_format=JSON). Each patient's anonymized anamnesis text, ECG report text, troponin value, and age are submitted via a separate API call with no conversation history. Output: HEART score components (0-2 each), total score (0-10), risk group, and indeterminate status.

Other names: GPT-4o İndeks Testi

Claude HEART Score Calculator

Other

Anthropic Claude (model: claude-sonnet-4-6, temperature=0, max_tokens=500, response_format=JSON). Identical system prompt and input format as GPT-4o. Processed independently with no cross-contamination between models. Output: same JSON schema as GPT-4o.

Other names: Claude İndeks Testi

Three-Expert Consensus HEART Score

Other

Three emergency medicine physicians (>=3 years experience, HEART-score trained) independently score each anonymized record. Majority vote (2/3) determines component scores; a 4th adjudicator resolves ties. Experts are blinded to LLM scores, each other's scores, and MACE outcomes.

Other names: Referans Standart

Primary outcomes

  1. Area Under the ROC Curve (AUC) of GPT-4o and Claude HEART Score for 30-Day MACE Prediction

    Time frame: 30 days after index emergency department visit

    AUC calculated separately for GPT-4o and Claude using the Hanley-McNeil method. MACE is defined as a composite of all-cause death, acute myocardial infarction (Type 1/2/4b), and unplanned revascularization within 30 days. HEART score range is 0-10; a higher score indicates a higher risk of MACE. Analysis will be performed on complete cases only (0 indeterminate components).

Secondary outcomes

  1. Sensitivity and Specificity of GPT-4o and Claude HEART Score at Prespecified Thresholds

    Time frame: 30 days after index emergency department visit

    Diagnostic sensitivity and specificity calculated at two threshold types: (a) total score >=4 (binary high-risk cutoff) and (b) trimodal cutoffs (0-3 low risk, 4-6 intermediate risk, 7-10 high risk). Metrics will be reported with 95% Wilson confidence intervals separately for each LLM.

  2. Component-Level and Total-Score Agreement (Cohen's Kappa) Between LLMs and Expert Consensus

    Time frame: Baseline (At index emergency department visit)

    Inter-rater agreement will be computed using quadratic weighted Cohen's Kappa for the ordinal total HEART score (range 0-10) and linear weighted Kappa for the individual components (range 0-2). Calculated separately for GPT-4o vs. expert consensus and Claude vs. expert consensus. Values will be interpreted using the Landis & Koch scale (<0.20 poor, 0.21-0.40 fair, 0.41-0.60 moderate, 0.61-0.80 good, >0.80 excellent).

  3. Comparative AUC Difference Between GPT-4o and Claude (DeLong Test)

    Time frame: 30 days after index emergency department visit

    Statistical comparison of paired ROC curves between GPT-4o and Claude using the DeLong et al. (1988) method. The formal hypothesis is non-inferiority with an expected delta AUC <= 0.05. The correlation coefficient between the paired LLM measurements is estimated as rho >= 0.70.

  4. Proportion of Indeterminate Results for GPT-4o and Claude

    Time frame: Baseline (At index emergency department visit)

    The proportion of cases classified into predefined missing data tiers: Complete (0 indeterminate components), Partial indeterminate (exactly 1 missing component preventing definitive score calculation), and Full indeterminate (>=2 missing components). Reported separately for each LLM and statistically compared between the two models.

  5. HEART Parameter Documentation Rate in Routine Turkish Anamnesis Notes

    Time frame: Baseline (At index emergency department visit)

    For each of the 5 individual HEART components, the proportion of emergency department free-text anamnesis notes that spontaneously contain sufficient objective clinical information for scoring. Rates will be categorized as: Present and scorable, Partiall

  6. Subgroup AUC by Age Group and Sex (Algorithmic Bias Assessment)

    Time frame: 30 days after the index emergency department visit

    AUC values for 30-day MACE prediction were calculated separately across demographic strata: age groups (<45, 45-64, >=65 years) and biological sex (male vs. female). This analysis serves as the formal algorithmic bias assessment required by the STARD-AI 2025 guidelines.

Study contacts

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

Emir Unal, Assistant Professor

CONTACT

[email protected]

+905327766010

Emre Kudu, associate professor

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

Marmara University Pendik Training and Research Hospital

Other

Registry information

Official study 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

Acronym: LLM-HEART

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jun 4, 2026
Registry last updated
Jun 23, 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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