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BACKGROUND: National prevalence rates for obesity and heart failure (HF) have been steadily increasing, which predisposes patients to higher morbidity and mortality rates. OBJECTIVES: The purpose of this study was to evaluate the prevalence of HF stages in hospitalized patients according to their body mass index (BMI). SETTING: Academic institution. METHODS: National Inpatient Sample data from 2016 to 2018 were examined to identify patients with obesity, HF (presence or absence of advanced HF [AHF]), and cardiogenic shock (CS). The proportion of hospital admissions was determined for each category on the basis of the presence of AHF with/without CS. A comparative analysis was performed between patients with and without AHF, and multivariate logistic regression analysis was performed for the event of AHF. The same analyses were performed for the event of CS. RESULTS: A total of 3,354,970 hospital admissions were identified. The prevalence of hospital admissions with a diagnosis of AHF and class III obesity and a diagnosis of CS and class III obesity was 21% and .5%, respectively. The prevalence of AHF and other classes of BMI and CS and other classes of BMI was 17% and .5%, respectively. The univariate analysis showed that there were significant variations in 10 factors between hospital admissions with/without the diagnosis of both AHF and CS. Statistical analyses indicated the following findings: Hospitalized patients in higher obesity groups are more likely to have AHF, and they are less likely to have CS compared with those with a BMI of ≤29.9. CONCLUSIONS: This study revealed that the prevalence of AHF was significantly higher in hospitalized patients with class III obesity. These findings have implications for clinical management, and it can be inferred that these patients are less likely to receive advanced cardiac replacement therapies and might benefit from innovative approaches to address severe dual morbidity.
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Background: Patients are increasingly using artificial intelligence (AI) chatbots to seek answers to medical queries. Methods: Ten frequently asked questions in anaesthesia were posed to three AI chatbots: ChatGPT4 (OpenAI), Bard (Google), and Bing Chat (Microsoft). Each chatbot's answers were evaluated in a randomised, blinded order by five residency programme directors from 15 medical institutions in the USA. Three medical content quality categories (accuracy, comprehensiveness, safety) and three communication quality categories (understandability, empathy/respect, and ethics) were scored between 1 and 5 (1 representing worst, 5 representing best). Results: ChatGPT4 and Bard outperformed Bing Chat (median [inter-quartile range] scores: 4 [3-4], 4 [3-4], and 3 [2-4], respectively; P<0.001 with all metrics combined). All AI chatbots performed poorly in accuracy (score of ≥4 by 58%, 48%, and 36% of experts for ChatGPT4, Bard, and Bing Chat, respectively), comprehensiveness (score ≥4 by 42%, 30%, and 12% of experts for ChatGPT4, Bard, and Bing Chat, respectively), and safety (score ≥4 by 50%, 40%, and 28% of experts for ChatGPT4, Bard, and Bing Chat, respectively). Notably, answers from ChatGPT4, Bard, and Bing Chat differed statistically in comprehensiveness (ChatGPT4, 3 [2-4] vs Bing Chat, 2 [2-3], P<0.001; and Bard 3 [2-4] vs Bing Chat, 2 [2-3], P=0.002). All large language model chatbots performed well with no statistical difference for understandability (P=0.24), empathy (P=0.032), and ethics (P=0.465). Conclusions: In answering anaesthesia patient frequently asked questions, the chatbots perform well on communication metrics but are suboptimal for medical content metrics. Overall, ChatGPT4 and Bard were comparable to each other, both outperforming Bing Chat.