Taler, Sandra J.

Obesity is increasingly prevalent among living kidney donor candidates, presenting complex clinical, ethical, and logistical challenges. Although obesity is linked to increased post-donation hypertension, diabetes, proteinuria, and chronic kidney disease, the overall absolute risks are modest and may differ depending on metabolic health and patterns of fat distribution. Current reliance on body mass index alone fails to capture the full spectrum of obesity-related risks, leading to inconsistent donor selection practices and potential inequities. This review examines the pathophysiological mechanisms of obesity-related kidney injury and available evidence on comorbidities and postdonation outcomes related to obesity. We discuss tools for better risk stratification, such as imaging-based adiposity assessment and personalized weight management strategies, and note that some risks may remain after weight loss. Barriers such as rigid body mass index cutoffs, limited access to treatment, and lack of long-term follow-up exacerbate disparities in access to donation. We argue for a shift toward individualized, risk-based evaluation supported by multidisciplinary care. We underscore the importance of donor counseling and advocate for a comprehensive, rather than paternalistic approach to living kidney donor selection. Advancing safe and equitable living donation in the context of rising obesity rates will necessitate revised guidelines, improved access to treatment, and an ongoing commitment to donor well-being.

Background/Objective: Hypertension is the most prevalent chronic disease in primary care and a leading cause of cardiovascular morbidity and mortality. Despite existing guidelines, therapeutic inertia and suboptimal control persist. Large language models (LLMs) offer a potential valuable addition to augment clinical decision-making, yet their reliability for guideline-driven tasks remains unverified. This study evaluated the accuracy and safety of hypertension management recommendations generated by three LLMs compared to expert responses. Methods: Fifty-one clinical vignettes representing 17 core hypertension management concepts were constructed by hypertension experts. Each case was submitted to three LLMs (GPT-4, Gemini, MedLM) and a hypertension expert also wrote the “gold standard” answers. Three blinded expert reviewers rated each response on a 4-point accuracy scale, a binary safety (safe/unsafe) scale, and attempted to identify the source (LLM vs. expert) providing the response. Ratings were analyzed using mean scores, percentages of accurate and safe responses, and inter-rater agreement. Results: GPT-4 had the highest accuracy (83%) and safety (86%) scores among LLMs but remained inferior to expert responses (92% accuracy, 93% safety). Gemini and MedLM performed significantly worse (accuracy: 64% and 35%; safety: 73% and 39%, respectively). GPT-4 generated the most guideline-concordant responses (46%) among the three LLMs (Gemini 35%, MedLM 14%), but remains lower than experts’ responses (68%). Evaluators misidentified LLM responses as expert-written in 10 to 25% of cases, particularly with GPT-4. Inter-rater reliability for accuracy ratings was highest for expert-generated responses (ICC 0.81), with progressively lower agreement for GPT-4 (0.76), Gemini (0.70), and MedLM (0.68). A similar pattern was observed for safety and source discrimination ratings. The agreement was strongest for safety assessments and weakest for source discrimination. Conclusion: Among three tested LLMs, GPT-4 demonstrated closer agreement to expert decisions thereby showing greater potential for supporting hypertension management. However, current LLMs’ versions frequently produce inaccurate or unsafe recommendations and remain inferior to expert judgment. Human-in-the-loop supervision remains essential when deploying LLMs for clinical decision-making.

BACKGROUND: Hypertension, the leading cause of cardiovascular mortality, remains suboptimally controlled. Large language models (LLMs) could improve hypertension control by augmenting clinical decision-making, but their reliability for guideline-driven tasks is unverified. This study evaluated the accuracy and safety of hypertension management recommendations generated by 3 LLMs. METHODS: Fifty-one vignettes were constructed and submitted to the LLMs (GPT-4, Gemini, Medical Large Language Model [by Google; MedLM]) and a hypertension expert to generate the responses. Three blinded reviewers rated each response on a 4-point accuracy scale, a binary safety (safe/unsafe) scale, and attempted to identify the source (LLM versus expert) providing the response. RESULTS: GPT-4 had the highest accuracy (83%) and safety (86%) scores among LLMs but remained inferior to expert responses (92% accuracy, 93% safety). Gemini and MedLM performed significantly worse (accuracy: 64% and 35%; safety: 73% and 39%, respectively). GPT-4 generated the most guideline-concordant responses (46%) among the 3 LLMs (Gemini 35%, MedLM 14%) but was lower than expert responses (68%). Interrater reliability for accuracy ratings was higher for LLM-generated responses (GPT-4 [intraclass correlation coefficient, 0.30], Gemini [intraclass correlation coefficient, 0.61], and MedLM [intraclass correlation coefficient, 0.58]), with lower agreement for expert responses (intraclass correlation coefficient, 0.23). A similar pattern was observed for safety and source discrimination ratings. The agreement was strongest for safety assessments and weakest for source discrimination. CONCLUSIONS: Among the 3 tested LLMs, GPT-4 demonstrated closer agreement to expert decisions, thereby showing greater potential for supporting hypertension management. Despite their potential, current LLM versions are inferior to expert recommendations. Human-in-the-loop supervision remains essential when deploying LLMs for clinical decision-making.

Background: Hypertension (HTN) affects almost half of the adults in the US, and most do not have their blood pressure (BP) under control. Minorities and individuals living in rural areas experience health disparities. The goal of this study was to assess the rate of BP control among adults in a large health system that included minorities and adults living in rural areas. Methods: Clinical cohort study of adults with HTN who live in Southeast Minnesota and had at least one outpatient clinic encounter within the Mayo Clinic Health System in the last 12 months (5/2021-5/2022). Data was extracted through the HTN registry of the electronic health record and included age, race, ethnicity, last office BP and any active anti-hypertensives drug use. Results: The overall rate of HTN control among 127,644 individuals with HTN, defined as an office BP < 130/80 mmHg, was 25.7%. The rates of BP control between HTN subjects living in rural (64527, 67.3%) versus urban areas (41285, 32.3 %), were significantly lower at 24.9% vs. 27% respectively, p< 0.001. A total of 8,722 (6.8%) were non-white, including 2640 Black, 1919 Asian, and 2867 Hispanics A total of 5,734 (45%) had a SBP ≥ 140 or DBP ≥ 90 mmHg and 7178 (12.5%) did not have any anti-hypertensives in their medication list. The percentage of White and Asian persons who were not on anti-hypertensives was 12.4% and 12.5%, respectively, while a larger percentage of Black (13.8%) and Hispanic (14%) persons were not (p<0.001). The corresponding percentage was 16.7% among individuals who did not disclose their race (p<0.001). Conclusion: Among hypertensive adults, BP was not at goal in most patients based on their most recent office BP measurement with a higher rate among those living in rural areas. About 12.5% were not on anti-hypertensives with a disproportionate larger percentage of Black and Hispanic compared to White and Asian persons.