Exploring the prognostic utility of large language models versus traditional clinical models in heart failure: a pilot study

Abstract

BACKGROUND: Large language models (LLMs) show promise in clinical decision support; however, their role in risk prediction for heart failure (HF) remains uncertain. OBJECTIVE: This pilot study evaluated the prognostic performance and reproducibility of two general-purpose LLMs, ChatGPT and DeepSeek, using structured clinical data and unstructured discharge summaries, compared with a conventional clinical model. METHODS: Structured data from the Zigong HF study included 473 hospitalized HF patients with 33 clinical variables predicting a 90-day composite outcome of all-cause death or rehospitalization. Discharge summaries from the MIMIC-IV cohort included 2,091 ICU HF patients predicting 1-year all-cause mortality. Standardized prompts were used to obtain predicted probabilities from each LLM. Model predictions were compared with logistic regression results, and reproducibility was assessed using intraclass correlation coefficients. RESULTS: In the Zigong HF study, both LLMs showed limited discrimination (AUC 0.59 for ChatGPT, 0.56 for DeepSeek), performing below the conventional model (AUC 0.63). In the MIMIC-IV cohort, ChatGPT achieved higher discrimination (AUC 0.72) than DeepSeek (AUC 0.67, P < 0.001) and comparable performance to the clinical model (AUC 0.74, P = 0.31). Decision curve analysis showed modest benefit for ChatGPT at low-to-moderate thresholds, while DeepSeek offered minimal benefit. Repeated predictions showed significant variability for both models. CONCLUSIONS: This pilot study provides preliminary evidence that LLMs have limited predictive value for structured data but show comparable performance in text-based risk prediction. These findings suggest potential for LLMs in processing unstructured clinical information and highlight the need for validation in larger, contemporary cohorts.

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