Poochanasri, Methavee

Background Prior authorization (PA) is a major source of administrative burden, treatment delay, and clinician burnout. Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used to assist with clinical documentation, yet its reliability for payer-facing administrative tasks remains uncertain. Objective To evaluate the quality of PA letters drafted by ChatGPT-5 for commonly used medications requiring PA in nephrology. Quality was evaluated based on correctness and strength of clinical reasoning. Methods We created a single standardized prompt and applied it across 29 nephrology scenarios to generate PA letters. Each PA letter was reviewed against four criteria: 1) absence of false statements or hallucinations, 2) correctness of ICD-10 coding, 3) presence and validity of citations, and 4) clinical reasoning, rated on a 4-point Likert scale (illogical, weak, adequate and strong). FDA drug labels, KDIGO guidelines and related randomized controlled trials were used as reference standards. Results Out of 29 letters, one letter (3.5%) contained false statements mentioning an irrelevant clinical trial. The ICD-10 diagnosis code was correct in 23 letters (79.3%), most errors were related to chronic kidney disease (CKD) staging or internal diagnostic inconsistencies. 27 letters (93.1%) cited valid references, with one letter citing an incorrect trial and another one citing a correct KDIGO guideline with inaccessible link. Twenty-six letters (89.7%) demonstrated strong clinical reasoning, supported by guideline-oriented or FDA label–aligned justification. The remaining 3 letters were rated as adequate reasoning. The main areas for improvement involved citing relevant references and emphasizing special considerations, for example Risk Evaluation and Mitigation Strategy (REMS) compliance for eculizumab. Conclusions ChatGPT-5 can generate clinically coherent PA drafts for nephrology medications, but limitations in coding precision and citation reliability persist. With appropriate oversight, AI-assisted documentation may reduce administrative burden while maintaining safety and accuracy.

Background: Although BP is associated with kidney outcomes, its clinical significance in living kidney donors (LKD) is unknown. We aim to examine the association between pre-donation BP and the risk of kidney function decline in LKD. Methods: A retrospective cohort study using OPTN/SRTR including adult LKD, who underwent donation between 6/1972 and 9/2022, was utilized to examine the association of quartile (Q) of pre-donation SBP and DBP with time-to-event of ≥35% decline in post-donation eGFR from pre-donation eGFR by multiple Cox regression. Results: Of 174,311 adult LKD, mean±SD age was 41±12 years and 60% were female. Mean pre-donation SBP and DBP were 122±13 and 74±9 mmHg, respectively. Mean pre-donation SBP in Q 1, 2, 3, and 4 were 106±6, 117±2, 126±3, and 140±9 mmHg, respectively and the corresponding DBP were 62±5, 71±2, 78±2, and 86±5 mmHg, respectively. The median (IQR) of pre-donation eGFR was 91 (75, 111) mL/min/1.73 m2. Median(IQR) eGFR at immediate post-donation, 6-, 12-, and 24-months post-donation were 50 (40, 62), 54 (44, 67), 55 (45, 68), and 57 (47, 70) mL/min/1.73 m2, respectively. Out of 38,780 LKD with post-donation eGFR data at 6, 12, or 24 months, 31,054 had the event of ≥35% decline in post-donation eGFR from pre-donation eGFR with a median time to follow-up of 6.77 months(IQR 5.93, 12.67). The incidence rate of the event was 0.08 person-months. Compared to patients with SBP in Q 1, those with SBP in Q 2, 3, and 4 had a significantly increased risk for the event(HRSBP Q2,3,4 1.05, 1.08, and 1.15; P <0.001 – 0.003) but only patients with DBP in Q 3 and 4 had a significantly increased risk for the event (HRSBP Q3,4 1.04 and 1.09). After adjusting for age, gender, race/ethnicity, U.S. citizenship, education level, history of pre-donation HTN, diabetes, pre-donation BMI, DBP, and urine protein:creatinine ratio, there was a J-shaped association between pre-donation SBP and post-donation eGFR decline and only patients in the Q 2 became significantly associated with lower the risk for the event (HR 0.87, 95%CI 0.76, 0.99; Fig1). The pre-donation DBP – post-donation eGFR association was similar to the SBP – eGFR association but there was no statistically significant in all Q (Fig2). There was no effect-measured modifier for all covariates. Conclusions: Pre-donation SBP in a normal range(113 - 120 mmHg) is associated with the lowest risk for a post-donation eGFR decline. BP control in the normal range should be achieved for potential LKD.

Background: Hypertension is well known for its association with cardiovascular events and arthritis. However, epidemiological data on the direct relationship between heart failure and arthritis in hypertensive patients is limited. This study examines the hypothesis that arthritis increases the risk of heart failure among patients with hypertension. Additionally, the effect of gender on these outcomes was assessed. Method: In our study, we analyzed data from 6,789 adults with hypertension aged 18 to 80 years, sourced from the National Health and Nutrition Examination Survey (NHANES) for the years 2017-2018. Arthritis was defined based on participants’ self-reported diagnosis by a doctor. We utilized logistic regression to explore the relationship between arthritis and the presence of heart failure across three different models. The first model included heart failure alone, the second model included age, gender, education, race, body mass index (BMI), smoking, and drinking, and the third model additionally included coronary artery disease and stroke. Result: The prevalence of heart failure among individuals with both arthritis and hypertension stands at 6.0%. Investigation into gender reveals a notable prevalence in males at 8.2% compared to 4.5% in females [Fig 1]. The analysis revealed a positive correlation between the two conditions across the entire population, with correlation coefficients of 2.38 (95% CI: 1.68-3.37), 1.61 (95% CI: 1.11-2.34), and 1.51 (95% CI: 1.01-2.25) for the three models, respectively. Specifically, in males, there was a notably increased association in the first and second models, with correlation coefficients of 2.77 (95% CI: 1.76-4.35) and 1.69 (95% CI: 1.05-2.74), respectively. Conversely, among females, a significant association was observed only in the first model, with a correlation coefficient of 2.24 (95% CI: 1.29-3.89) [Fig 2]. Conclusion: A robust positive correlation between arthritis and heart failure was observed in hypertensive U.S. patients. Gender was found to be a factor influencing this relationship, with males having a higher risk and prevalence of heart failure compared to the overall population.