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Background: Sarcopenia is a comorbidity associated with heart failure, which aggravates its prognosis. Objectives: To analyze the differential characteristics of polypathological patients with acute heart failure (AHF) based on the presence of low muscle strength, as well as to study whether this condition is associated with a worse prognosis. Methods: An observational study of 377 patients with a diagnosis of acute heart failure from the prospective multicentric PROFUND-IC registry was carried out. The main variable is low muscle strength, which is assessed with dynamometry or prehensile strength. Epidemiological and anthropometric characteristics, as well as associated comorbidities, were analyzed. Likewise, the etiology of the AHF episode, the number of admissions in the previous year, and the NYHA scale were also included. Finally, scores on functionality, treatment established, and mortality and readmission rates were studied. Quantitative variables are described as mean, and standard deviation, and qualitative variables are expressed as absolute numbers and percentages. A descriptive and bivariate analysis was performed according to the presence of low muscle strength (handgrip <27 kg in men and <16 kg in women), using the Welch test for quantitative measures and Chi-square for qualitative variables. In addition, Kaplan-Meier curves of readmission and mortality and a logistic regression analysis were also performed. Results: 377 patients were included (56% female, mean age 83 years). 310 (82.23%) had low muscle strength. Those with low muscle strength were older (84 vs. 78 years, p < 0.001), with more cognitive impairment (11.9% vs. 0%, p = 0.021), worse functional class (p = 0.016), lower scores in the Barthel index and Rockwood scale (p < 0.001), and higher in the PROFUND index (p < 0.001). They had higher rates of readmission and mortality without statistically significant differences. The PROFUND index is significantly associated with low muscle strength (OR 1.19, CI (1.09-1.31), p < 0.001). Conclusions: Elderly polypathological patients with acute heart failure and low muscle strength have a higher PROFUND index and a lower probability of survival per year.
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The emergence of large language models (LLM) with remarkable performance such as ChatGPT and GPT-4, has led to an unprecedented uptake in the population. One of their most promising and studied applications concerns education due to their ability to understand and generate human-like text, creating a multitude of opportunities for enhancing educational practices and outcomes. The objective of this study is twofold: to assess the accuracy of ChatGPT/GPT-4 in answering rheumatology questions from the access exam to specialized medical training in Spain (MIR), and to evaluate the medical reasoning followed by these LLM to answer those questions. A dataset, RheumaMIR, of 145 rheumatology-related questions, extracted from the exams held between 2010 and 2023, was created for that purpose, used as a prompt for the LLM, and was publicly distributed. Six rheumatologists with clinical and teaching experience evaluated the clinical reasoning of the chatbots using a 5-point Likert scale and their degree of agreement was analyzed. The association between variables that could influence the models' accuracy (i.e., year of the exam question, disease addressed, type of question and genre) was studied. ChatGPT demonstrated a high level of performance in both accuracy, 66.43%, and clinical reasoning, median (Q1-Q3), 4.5 (2.33-4.67). However, GPT-4 showed better performance with an accuracy score of 93.71% and a median clinical reasoning value of 4.67 (4.5-4.83). These findings suggest that LLM may serve as valuable tools in rheumatology education, aiding in exam preparation and supplementing traditional teaching methods.
Assuntos
Reumatologia , Humanos , Escolaridade , Transporte Biológico , Hidrolases , IdiomaRESUMO
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