By recognizing the diagnostic and prognostic significance of these biomarkers, clinicians can utilize these biomarkers to more accurately evaluate and risk stratify patients. These markers can also be used to help guide the medical management of heart failure. The best approach for an accurate diagnosis, management, and prognosis of heart failure will likely involve a multimarker panel of biomarkers, which may include high-sensitivity troponins, BNP, N-terminal prohormone of BNP, and ST2.
Background Heart failure requires complex management with increased patient knowledge shown to improve outcomes. The large language model (LLM), Chat Generative Pre-Trained Transformer (ChatGPT), may be a useful supplemental resource of information for patients with heart failure. Methods Responses produced by GPT-3.5 and GPT-4 to 107 frequently asked heart failure-related questions were graded by two reviewers board-certified in cardiology, with differences resolved by a third reviewer. The reproducibility and accuracy between GPT-3.5 and GPT-4 were compared for questions involving basic knowledge, management, prognosis, procedures, and support. Results GPT-4 displayed a greater proportion of comprehensive knowledge for the categories of basic knowledge and management, while GPT-3.5 performed better in the other category (prognosis, procedures, and support) (94.1% vs 64.7%). There were 2 total responses (1.9%) graded as some correct and incorrect for GPT-3.5, while no GPT-4 responses received a grade of some correct and incorrect or completely incorrect. Both models provided highly reproducible responses, with GPT-3.5 scoring above 94% in every category and GPT-4 with 100% for all answers. Conclusions Both GPT-3.5 and GPT-4 answered the majority of heart failure-related questions accurately and reliably, with GPT-4 displaying superior performance overall. ChatGPT may lead to better outcomes in patients with heart failure by providing health education.
Left ventricular assist devices (LVADs) have dramatically improved short-term outcomes among patients with advanced heart failure. While neurohormonal blockade (NHB) is the cornerstone of treatment for patients with heart failure with reduced ejection fraction, its effect after LVAD placement has not been established. We reviewed medical records of 307 patients who underwent primary LVAD implantation from January 2006 to September 2015 at two institutions in the United States. Patients were followed for at least 2 years post-LVAD implantation or until explantation, heart transplantation, or death. Cox regression analysis stratifying on center was used to assess associations with mortality. Neurohormonal blockade use was treated as a time-dependent predictor. Stepwise selection indicated treatment with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (ACEIs/ARBs) (hazard ratio [HR] = 0.53 [0.30–0.95], p = 0.03), age at the time of implantation (HR = 1.28 [1.05–1.56] per decade, p = 0.02), length of stay postimplantation (HR = 1.16 [1.11–1.21] per week, p < 0.01) and INTERMACS profile of 1 or 2 (HR = 1.86 [1.17–2.97], p < 0.01) were independent predictors of mortality. In this large, retrospective study, treatment with ACEIs or ARBs was an independent factor associated with decreased mortality post-LVAD placement.
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