Importance Non–vitamin K oral anticoagulants (NOACs) are commonly prescribed with other medications that share metabolic pathways that may increase major bleeding risk. Objective To assess the association between use of NOACs with and without concurrent medications and risk of major bleeding in patients with nonvalvular atrial fibrillation. Design, Setting, and Participants Retrospective cohort study using data from the Taiwan National Health Insurance database and including 91 330 patients with nonvalvular atrial fibrillation who received at least 1 NOAC prescription of dabigatran, rivaroxaban, or apixaban from January 1, 2012, through December 31, 2016, with final follow-up on December 31, 2016. Exposures NOAC with or without concurrent use of atorvastatin; digoxin; verapamil; diltiazem; amiodarone; fluconazole; ketoconazole, itraconazole, voriconazole, or posaconazole; cyclosporine; erythromycin or clarithromycin; dronedarone; rifampin; or phenytoin. Main Outcomes and Measures Major bleeding, defined as hospitalization or emergency department visit with a primary diagnosis of intracranial hemorrhage or gastrointestinal, urogenital, or other bleeding. Adjusted incidence rate differences between person-quarters (exposure time for each person during each quarter of the calendar year) of NOAC with or without concurrent medications were estimated using Poisson regression and inverse probability of treatment weighting using the propensity score. Results Among 91 330 patients with nonvalvular atrial fibrillation (mean age, 74.7 years [SD, 10.8]; men, 55.8%; NOAC exposure: dabigatran, 45 347 patients; rivaroxaban, 54 006 patients; and apixaban, 12 886 patients), 4770 major bleeding events occurred during 447 037 person-quarters with NOAC prescriptions. The most common medications co-prescribed with NOACs over all person-quarters were atorvastatin (27.6%), diltiazem (22.7%), digoxin (22.5%), and amiodarone (21.1%). Concurrent use of amiodarone, fluconazole, rifampin, and phenytoin with NOACs had a significant increase in adjusted incidence rates per 1000 person-years of major bleeding than NOACs alone: 38.09 for NOAC use alone vs 52.04 for amiodarone (difference, 13.94 [99% CI, 9.76-18.13]); 102.77 for NOAC use alone vs 241.92 for fluconazole (difference, 138.46 [99% CI, 80.96-195.97]); 65.66 for NOAC use alone vs 103.14 for rifampin (difference, 36.90 [99% CI, 1.59-72.22); and 56.07 for NOAC use alone vs 108.52 for phenytoin (difference, 52.31 [99% CI, 32.18-72.44];P < .01 for all comparisons). Compared with NOAC use alone, the adjusted incidence rate for major bleeding was significantly lower for concurrent use of atorvastatin, digoxin, and erythromycin or clarithromycin and was not significantly different for concurrent use of verapamil; diltiazem; cyclosporine; ketoconazole, itraconazole, voriconazole, or posaconazole; and dronedarone. Conclusions and Relevance Among patients taking NOACs for nonvalvular atrial fibrillation, concurrent use of amiodarone, fluconazole, rifampin, and phenytoin compared with the use of NOACs alone, was associated with increased risk of major bleeding. Physicians prescribing NOAC medications should consider the potential risks associated with concomitant use of other drugs.

Systemic lupus erythematosus (SLE) is the prototype autoimmune disease with features of autoantibody production, immune complex deposition, and multiple target organ damage. The disease can affect any part of the body,¹ and the course of the disease is diverse and unpredictable.² The prevalence of SLE ranges from 0.02% to 0.15%.³ Our group estimated that the prevalence of SLE was 0.10% in the United Kingdom⁴ and 0.07% in Taiwan.⁵

Atrial fibrillation (AF), an inflammatory process involving arrhythmia, is associated with severe morbidity and mortality and commonly seen in patients with diabetes mellitus (DM). The effect of metformin, the most commonly used medication for patients with DM, on AF has not been investigated. The primary aim of this study was to examine whether metformin prevented the occurrence of AF in type 2 DM patients by analyzing a nationwide, population-based dynamic cohort. Additionally, we investigated the effect of metformin on tachycardia-induced myolysis and oxidative stress in atrial cells. The study population included 645,710 patients with type 2 diabetes and not using other anti-diabetic medication from a subset of the Taiwan National Health Insurance Research Database. Of these patients, those who used metformin were categorized as the user group, and the remaining were classified as the non-user group. The time-dependent Cox’s proportional hazard model was used to examine the effect of metformin on AF and the status of metformin use was treated as a time-dependent covariate. HL-1 atrial cells were paced with or without metformin, and then troponin and heavy-chain-myosin were measured as markers of myolysis. After 13 years of follow-up, 9,983 patients developed AF with an incidence rate of 1.5% (287 per 100,000 person-years). After adjusting for co-morbidities and medications, metformin independently protected the diabetic patients from new-onset AF with a hazard ratio of .81 (95% confidence interval 0.76-0.86, p < 0.0001). Metformin significantly decreased the extent of pacing-induced myolysis and the production of reactive oxygen species. Metformin use was associated with a decreased risk of AF in patients with type 2 DM who were not using other anti-diabetic medication, probably via attenuation of atrial cell tachycardia-induced myolysis and oxidative stress.

Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = −0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.

Introduction: Gout is the most common inflammatory arthritis worldwide and is the only type of chronic arthritis that potentially can be ‘cured’. However, data on gout incidence, prevalence and management, assessed at multiple time points in the same population, are sparse, particularly in Asian populations. The aim of this study was to describe trends in the epidemiology of gout in the general population of Taiwan. Methods: The National Health Insurance Research Database was used to identify patients with gout and to estimate the prevalence and incidence of gout for each calendar year from 2005 to 2010. The pattern of gout management was also examined. Results: Of 23,371,362 beneficiaries in 2010, there were 1,458,569 prevalent and 56,595 incident cases of gout, giving a prevalence of 6.24% (95% confidence interval (CI), 6.23% to 6.25%) and an incidence of 2.74 (95% CI, 2.72 to 2.76) per 1,000 person-years. The annual percentage change (APC) of the standardised prevalence was −0.7% (95% CI, −1.7% to 0.3%; P = 0.14), suggesting that the prevalence of gout was essentially the same throughout the study period. However, The APC of incidence was −13.4 (95% CI, −16.1 to −10.6) between 2005 and 2007 and −2.1 (95% CI, −10.4 to 7.1) between 2007 and 2010. Regions with the highest prevalence and incidence were eastern coastal counties and offshore islets, where indigenous people are clustered. Among prevalent gout cases in 2010, only 22.93% (95% CI, 22.87% to 23.00%) were prescribed urate-lowering treatment (ULT), which remained unchanged between 2005 and 2010 at an APC of 0.0 (95% CI, −3.8 to 4.0). Uricosuric agents were more commonly prescribed than xanthine oxidase inhibitors in Taiwan. Conclusions: In Taiwan, 1 in 16 people have gout. Whereas the incidence has decreased recently, the prevalence remains unchanged. Management of gout in Taiwan is poor, with only one in five affected people being treated with ULT.

Large Language Models (LLMs) are capable of transforming healthcare by demonstrating remarkable capabilities in language understanding and generation. They have matched or surpassed human performance in standardized medical examinations and assisted in diagnostics across specialties like dermatology, radiology, and ophthalmology. LLMs can enhance patient education by providing accurate, readable, and empathetic responses, and they can streamline clinical workflows through efficient information extraction from unstructured data such as clinical notes. Integrating LLM into clinical practice involves user interface design, clinician training, and effective collaboration between Artificial Intelligence (AI) systems and healthcare professionals. Users must possess a solid understanding of generative AI and domain knowledge to assess the generated content critically. Ethical considerations to ensure patient privacy, data security, mitigating biases, and maintaining transparency are critical for responsible deployment. Future directions for LLMs in healthcare include interdisciplinary collaboration, developing new benchmarks that incorporate safety and ethical measures, advancing multimodal LLMs that integrate text and imaging data, creating LLM-based medical agents capable of complex decision-making, addressing underrepresented specialties like rare diseases, and integrating LLMs with robotic systems to enhance precision in procedures. Emphasizing patient safety, ethical integrity, and human-centered implementation is essential for maximizing the benefits of LLMs, while mitigating potential risks, thereby helping to ensure that these AI tools enhance rather than replace human expertise and compassion in healthcare.