Kookanok, Chutawat

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.

Introduction: Menopause is known to be associated with myocardial infarction (MI). However, the association between menopause age and MI onset in a nationally representative population remains unknown. Objective: To examine the correlation between age of menopause and age of MI onset in the general population and the association between early menopause (onset of menopause ≤ 40 years old) and early onset of MI (onset of MI ≤ 40 years old). Methods: A cross-sectional study was conducted using a population from the 2017-2020 National Health and Nutrition Examination Survey (NHANES). The questionnaire collected self-reported data on the participants' menopause age and their age of first MI. The relationship between these two parameters was analyzed by multivariate linear and logistic regression analyses. Multivariate analysis was adjusted for race, age, body mass index (BMI), smoking status, diabetes mellitus (DM), hypertension, and dyslipidemia status. Results: Among 2,200 postmenopausal females, 90 (4.1%) have had MI. The study involved 85 postmenopausal females who reported both menopause age and MI onset, with a mean ± SD age of 55 ± 13 years. This study found a significant association between menopause age and MI onset. After adjusting for confounders, it was found that every 1-year increase in the age of menopause will delay the onset of MI by 0.28 years, with a 95% CI of 0.013 to 0.552 (Table 1A, Graph 1A, Graph 1B). Additionally, early menopause carries an almost significantly increased risk of 2.11 times (95% CI: 1.00, 4.45) for early-onset MI. After adjusting for confounders, the magnitude of the association was lower and not statistically significant, but the direction remains (OR (95% CI): 2.08 (0.87, 4.95)). However, after stratification with comorbidities, DM status was identified as an effect modifier, with early MI significantly observed in patients with DM in both univariate and multivariate analyses, with OR (95% CI) of 3.03 (1.10, 8.37) and 4.88 (1.26, 18.96), respectively (Table 2A). Conclusion: This study demonstrated a positive association between menopause age and MI onset in a generally representative population. This suggests that prolonged estrogen exposure due to late menopause may delay the onset of MI. Additionally, early menopause was associated with early MI onset in the DM population. Further longitudinal studies are required to examine the potential mechanisms involved in menopause onset and MI onset.

Background: There is suspicion that estrogen may protect against atherosclerosis. Women who experience menarche earlier, and thus have earlier estrogen exposure, might develop atherosclerosis later. The relationship between menarche age and the onset of angina pectoris, a symptom of coronary artery disease, remains unknown. Goal: This study aims to find the correlation between menarche age and the onset age of angina pectoris, and the association between early menarche (onset of menarche ≤ 10 y/o) and early onset of angina pectoris (onset of angina pectoris ≤ 40 y/o). Methods: A cross-sectional study using a population from the 2017-2020 National Health and Nutrition Examination Survey (NHANES). The question collects self-reported data on onset of menarche and onset of angina pectoris. The relationship between menarche age and age in onset of angina pectoris was analyzed by linear and logistic regression analysis. Multivariate analysis adjusted for race, age, body mass index (BMI), smoking , diabetes mellitus (DM), hypertension, and dyslipidemia status. Results: Among 4,816 females who reported their menarche age, 88(1.8 %) have had an angina pectoris. The study involved 82 females who reported both the age of menarche and the age of onset of angina pectoris. The mean ± SD of the age at menarche and the age at onset of angina pectoris was 12.53 ± 1.79 y/o and 51.66 ± 15.16 y/o, respectively. The study found a significant association between the menarche age and the age at onset of angina pectoris. After adjusting for confounders, we found that every 1-year increase in the age of menarche will delay the onset of angina pectoris by 1.43 years, with a 95% CI of 0.01 to 2.86.(Table 1A,Graph 1A-1B) We also found that early menarche has a significantly increased risk 2.8 times (95%CI: 1.3, 6.2) of early onset of angina pectoris.After adjusting for confounders, the association became even stronger. The odds ratio (OR) increased to 3.3 (95%CI: 1.4, 8.0), indicating that women with early menarche were 3.3 times more likely to experience early angina pectoris compared to those with later menarche.(Table 2A) Conclusion: In contrast to our hypothesis, age at menarche is positively associated with onset of angina pectoris. The earlier onset of menarche is linked to the earlier onset of angina pectoris. We suspected that factors beyond estrogen levels may contribute to this unexpected finding. Additional studies are needed to examine the cause of this correlation.

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.