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Alendronate and raloxifene are among the most popular anti-osteoporosis medications. However, there is a lack of head-to-head comparative effectiveness studies comparing the two treatments. We conducted a retrospective large-scale multicenter study encompassing over 300 million patients across nine databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporotic hip fracture, while secondary outcomes were vertebral fracture, atypical femoral fracture (AFF), osteonecrosis of the jaw (ONJ), and esophageal cancer. We used propensity score trimming and stratification based on an expansive propensity score model with all pre-treatment patient characteritistcs. We accounted for unmeasured confounding using negative control outcomes to estimate and adjust for residual systematic bias in each data source. We identified 283,586 alendronate patients and 40,463 raloxifene patients. There were 7.48 hip fracture, 8.18 vertebral fracture, 1.14 AFF, 0.21 esophageal cancer and 0.09 ONJ events per 1,000 person-years in the alendronate cohort and 6.62, 7.36, 0.69, 0.22 and 0.06 events per 1,000 person-years, respectively, in the raloxifene cohort. Alendronate and raloxifene have a similar hip fracture risk (hazard ratio [HR] 1.03, 95% confidence interval [CI] 0.94–1.13), but alendronate users are more likely to have vertebral fractures (HR 1.07, 95% CI 1.01–1.14). Alendronate has higher risk for AFF (HR 1.51, 95% CI 1.23–1.84) but similar risk for esophageal cancer (HR 0.95, 95% CI 0.53–1.70), and ONJ (HR 1.62, 95% CI 0.78–3.34). We demonstrated substantial control of measured confounding by propensity score adjustment, and minimal residual systematic bias through negative control experiments, lending credibility to our effect estimates. Raloxifene is as effective as alendronate and may remain an option in the prevention of osteoporotic fracture.

To evaluate the risk of developing psychiatric disorders within three years after non-cardiac surgery in patients exposed to fentanyl analogs versus other opioids. Retrospective observational study. Postoperative period. The study included 52,640adult patients who underwent non-cardiac surgery at Samsung Medical Center, Seoul, Korea, between January 2011 and June 2019. Patients were divided into those exposed to fentanyl analogs and those exposed to other opioids. Propensity score matching and Cox regression analysis were used to compare the incidence of psychiatric disorders between the groups. Psychiatric outcomes, including depression, anxiety, stress-related disorders, substance use disorders, and psychotic disorders, were assessed. The study included 52,640 patients, evenly split between the fentanyl and other opioid groups. Fentanyl exposure was associated with a higher incidence of composite psychiatric outcomes (hazard ratio [HR] 1.28 [1.13-1.46]; P < 0.001), including depression (HR 1.25 [1.08-1.45]; P = 0.003) and stress-related disorders (HR 1.45 [1.02-2.04]; P = 0.036). Subgroup analyses indicated increased risks in males, females, patients without alcohol history, and those not undergoing emergency surgeries. Perioperative fentanyl use is linked to a higher risk of psychiatric disorders compared to other opioids. These findings emphasize the need for careful use and monitoring of fentanyl in surgical patients, considering both immediate and long-term mental health effects.

Central and cerebral haemodynamic parameters can vary under similar brachial blood pressure (BP). We aimed to investigate the effects of antihypertensive agents on central and cerebral haemodynamic parameters in hypertensive patients with ischaemic stroke. The Fimasartan, Atenolol, and Valsartan On haemodynamic paRameters (FAVOR) study was conducted in a prospective, double-blinded manner. One hundred five patients were randomly administered atenolol, valsartan, or fimasartan during 12 weeks. We measured brachial, central, cerebral haemodynamic parameters and plasma N-terminal pro-brain natriuretic peptide (NT-proBNP) levels at baseline and after 12-week. Baseline haemodynamic parameters were balanced among the three groups. Even with similar brachial BP reduction, significantly lower central systolic BP (atenolol; 146.5 ± 18.8 vs. valsartan; 133.5 ± 20.7 vs. fimasartan; 133.6 ± 19.8 mmHg, p  =  0 . 017 ) and augmentation index values (89.8 ± 13.2 vs. 80.6 ± 9.2 vs. 79.2 ± 11.6%; p  =  0 . 001 ) were seen in the angiotensin receptor blockers (ARBs) groups. The pulsatility index on transcranial Doppler was significantly reduced in valsartan ( p  =  0.002 ) and fimasartan group ( p  =  0 . 008 ). Plasma NT-proBNP level was also significantly decreased in ARB groups, especially for the fimasartan group (37.8 ± 50.6 vs. 29.2 ± 36.9 vs.19.2 ± 27.8 pg/mL; p = 0.006). These findings suggest that short-term ARB administration would be favourable for ischaemic stroke patients with hypertension, permitting effective reduction of central pressure and dampening of cerebral pulsatility.

Large language models (LLMs) have emerged as promising tools for addressing global disparities in mental health care. However, cloud-based proprietary models raise concerns about data privacy and limited adaptability to local health care systems. In contrast, open-source LLMs offer several advantages, including enhanced data security, the ability to operate offline in resource-limited settings, and greater adaptability to non-English clinical environments. Nevertheless, their performance in psychiatric applications involving non-English language inputs remains largely unexplored. This study aimed to systematically evaluate the clinical reasoning capabilities and diagnostic accuracy of a locally deployable open-source LLM in both Korean and English psychiatric contexts. The openbuddy-mistral-7b-v13.1 model, fine-tuned from Mistral 7B to enable conversational capabilities in Korean, was selected. A total of 200 deidentified psychiatric interview notes, documented during initial assessments of emergency department patients, were randomly selected from the electronic medical records of a tertiary hospital in South Korea. The dataset included 50 cases each of schizophrenia, bipolar disorder, depressive disorder, and anxiety disorder. The model translated the Korean notes into English and was prompted to extract 5 clinically meaningful diagnostic clues and generate the 2 most likely diagnoses using both the original Korean and translated English inputs. The hallucination rate and clinical relevance of the generated clues were manually evaluated. Top-1 and top-2 diagnostic accuracy were assessed by comparing the model's prediction with the ground truth labels. Additionally, the model's performance on a structured diagnostic task was evaluated using the psychiatry section of the Korean Medical Licensing Examination and its English-translated version. The model generated 997 clues from Korean interview notes and 1003 clues from English-translated notes. Hallucinations were more frequent with Korean input (n=301, 30.2%) than with English (n=134, 13.4%). Diagnostic relevance was also higher in English (n=429, 42.8%) compared to Korean (n=341, 34.2%). The model showed significantly higher top-1 diagnostic accuracy with English input (74.5% vs 59%; P<.001), while top-2 accuracy was comparable (89.5% vs 90%; P=.56). Across 115 questions from the medical licensing examination, the model performed better in English (n=53, 46.1%) than in Korean (n=37, 32.2%), with superior results in 7 of 11 diagnostic categories. This study provides an in-depth evaluation of an open-source LLM in multilingual psychiatric settings. The model's performance varied notably by language, with English input consistently outperforming Korean. These findings highlight the importance of assessing LLMs in diverse linguistic and clinical contexts. To ensure equitable mental health artificial intelligence, further development of high-quality psychiatric datasets in underrepresented languages and culturally adapted training strategies will be essential.

Treatment-resistant depression (TRD) affects 10-30% of patients with major depressive disorder, leading to increased comorbidities, higher mortality, and significant economic and social burdens. This study aimed to compare the efficacy and safety of bupropion and aripiprazole as augmentation therapies for TRD. This population-based, retrospective cohort study included adults aged ≥18 years with a diagnosis of depressive disorder who met the criteria for TRD. Data were collected from a nationwide claims database in South Korea. Patients prescribed bupropion were matched 1:1 with those prescribed aripiprazole. Subgroup analyses were performed according to age. An as-treated analysis was performed as the primary analysis, and an intention-to-treat analysis was performed to identify different risk windows. The primary outcome was depression-related hospitalization, and the secondary outcomes were first-time diagnoses of movement disorder and seizure. A total of 5,619 patients (bupropion:  = 1,568; aripiprazole:  = 4,051) were included in this study. Bupropion was associated with lower risks of hospitalization (hazard ratio [HR]: 0.51; 95% confidence interval [CI] 0.29-0.86) and movement disorders (HR: 0.56; 95% CI 0.36-0.85) than aripiprazole. No significant difference in seizure risk (HR: 0.65; 95% CI 0.30-1.31) was observed between the two treatments. The subgroup analysis of participants aged ≥60 years revealed no significant differences in the three outcomes between the two medications. Bupropion augmentation is associated with a significantly lower risk of depression-related re-hospitalization and movement disorders in patients with TRD. Therefore, bupropion augmentation can be a comprehensive treatment strategy for TRD.

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood. Although it requires timely detection and intervention, existing continuous performance tests (CPTs) have limited efficacy. Research suggests that eye movement could offer important diagnostic information for ADHD. This study aimed to compare the performance of eye-tracking with that of CPTs, both alone and in combination, and to evaluate the effect of medication on eye movement and CPT outcomes. We recruited participants into an ADHD group and a healthy control group between July 2021 and March 2022 from among children aged 6–10 years (n = 30 per group). The integration of eye-tracking with CPTs produced higher values for the area under the receiver operating characteristic (AUC, 0.889) compared with using CPTs only (AUC, 0.769) for identifying patients with ADHD. The use of eye-tracking alone showed higher performance compare with the use of CPTs alone (AUC of EYE: 0.856, AUC of CPT: 0.769, p  = 0.029). Follow-up analysis revealed that most eye-tracking and CPT indicators improved significantly after taking an ADHD medication. The use of eye movement scales could be used to differentiate children with ADHD, with the possibility that integrating eye movement scales and CPTs could improve diagnostic precision.

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.

The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results. To construct an ADR reference dataset, we extracted known drug-laboratory event pairs represented by a laboratory test from the EU-SPC and SIDER databases. All possible drug-laboratory event pairs, except known ones, are considered unknown. To detect a known drug-laboratory event pair, three existing algorithms-CERT, CLEAR, and PACE-were applied to 21-year inpatient EHR data. We also constructed ML models (based on random forest, L1 regularized logistic regression, support vector machine, and a neural network) that use the intermediate products of the CERT, CLEAR, and PACE algorithms as inputs and determine whether a drug-laboratory event pair is associated. For performance comparison, we evaluated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-measure, and area under receiver operating characteristic (AUROC). All measures of ML models outperformed those of existing algorithms with sensitivity of 0.593-0.793, specificity of 0.619-0.796, NPV of 0.645-0.727, PPV of 0.680-0.777, F1-measure of 0.629-0.709, and AUROC of 0.737-0.816. Features related to change or distribution of shape were considered important for detecting ADR signals. Improved performance of ML models indicated that applying our model to EHR data is feasible and promising for detecting more accurate and comprehensive ADR signals.

Antidepressants play a crucial role in treating mental health disorders such as depression and anxiety. Understanding of patients' perspective on antidepressants is essential for improving treatment outcomes; however, year-to-year change in the public's perception of antidepressants remains unclear. We aimed to analyze changes in public sentiments and predominant perceptions regarding antidepressants using artificial intelligence pipeline. This study analyzed online discussions related to antidepressants on Reddit from January 1, 2009, to December 31, 2022. Antidepressant-associated communities were explored to collect a list of discussions relevant to antidepressant therapy. Discussion topics on antidepressants were identified using BERTopic, and the sentiments were analyzed using a RoBERTa model. Trends were assessed using the Mann-Kendall test to evaluate shifts in sentiments over time. We analyzed 429,510 antidepressant-related discourse over 14 years and found a predominance in negative sentiments. Key discussion topics include the benefits and side effects of antidepressants, experiences with drug switching, and specific concerns regarding bupropion therapy. In trend analyses, negative sentiments decreased, while neutral sentiments increased over time. This aligns with a decline in the annual proportion of topics associated with side effects within each cluster. Negative perceptions toward antidepressants are prevalent on social media, mainly focusing on efficacy and side effects. However, a decade-long analysis shows a decline in negative sentiments, with an increase in neutral sentiments with a downturn in yearly proportion of side-effected related topics within each cluster. These trends and information may help improve strategies to address barriers to antidepressant use and adherence.

Background Postoperative delirium is a common complication that is distressing. This study aimed to demonstrate a prediction model for delirium. Methods Among 203,374undergoing non-cardiac surgery between January 2011 and June 2019 at Samsung Medical Center, 2,865 (1.4%) were diagnosed with postoperative delirium. After comparing performances of machine learning algorithms, we chose variables for a prediction model based on an extreme gradient boosting algorithm. Using the top five variables, we generated a prediction model for delirium and conducted an external validation. The Kaplan–Meier and Cox survival analyses were used to analyse the difference of delirium occurrence in patients classified as a prediction model. Results The top five variables selected for the postoperative delirium prediction model were age, operation duration, physical status classification, male sex, and surgical risk. An optimal probability threshold in this model was estimated to be 0.02. The area under the receiver operating characteristic (AUROC) curve was 0.870 with a 95% confidence interval of 0.855–0.885, and the sensitivity and specificity of the model were 0.76 and 0.84, respectively. In an external validation, the AUROC was 0.867 (0.845–0.877). In the survival analysis, delirium occurred more frequently in the group of patients predicted as delirium using an internal validation dataset ( p  < 0.001). Conclusion Based on machine learning techniques, we analyzed a prediction model of delirium in patients who underwent non-cardiac surgery. Screening for delirium based on the prediction model could improve postoperative care. The working model is provided online and is available for further verification among other populations. Trial registration KCT 0006363.