Use of SGLT2 Inhibitors vs GLP-1 RAs and Anemia in Patients With Diabetes and CKD.

Importance: Sodium-glucose cotransporter 2 (SGLT2) inhibitors are associated with lower anemia risk, based on findings from post hoc analyses of the CREDENCE and DAPA-CKD trials; however, the effectiveness of SGLT2 inhibitors in a more generalizable type 2 diabetes (T2D) and chronic kidney disease (CKD) population, with active comparisons pertinent to current practice, is unknown. Objective: To evaluate and compare anemia incidence between SGLT2 inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) among patients with T2D and CKD stages 1 to 3. Design, Setting, and Participants: This retrospective cohort study used target trial emulation of an expanded CREDENCE and DAPA-CKD study framework. The study was conducted among adults with T2D and CKD initiating SGLT2 inhibitors or GLP-1 RAs between January 1, 2016, and December 31, 2021, with follow-up until December 31, 2022. The study was conducted at the Chang Gung Medical Foundation, the largest multi-institutional hospital system in Taiwan. Exposures: Initiation of SGLT2 inhibitors or GLP-1 RAs. Main Outcomes and Measures: The primary outcome was a composite of anemia outcomes, including anemia event occurrence (hemoglobin level <12-13 g/dL or International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnosis codes) or anemia treatment initiation. Changes in hematological parameters, including hemoglobin level, hematocrit level, and red blood cell count, were evaluated during the follow-up period for as long as 3 years. Results: The cohort included a total of 13 799 patients with T2D and CKD, initiating SGLT2 inhibitors (12 331 patients; mean [SD] age, 62.4 [12.3] years; 7548 [61.2%] male) or GLP-1 RAs (1468 patients; mean [SD] age, 61.5 [13.3] years; 900 [61.3%] male). After the median follow-up period of 2.5 years, patients receiving SGLT2 inhibitors had lower incidence of composite anemia outcomes (hazard ratio [HR], 0.81; 95% CI, 0.73-0.90) compared with those receiving GLP-1 RAs. SGLT2 inhibitors were associated with a lower incidence of anemia events (HR, 0.79; 95% CI, 0.71-0.87) but not with a lower rate of anemia treatment initiation (HR, 0.99; 95% CI, 0.83-1.19). Changes in hematological parameters for SGLT2 inhibitors and GLP-1 RAs throughout the 3-year follow-up period supported the primary analyses. Conclusions and Relevance: In this multi-institutional cohort study with target trial emulation, SGLT2 inhibitors were associated with a decreased risk of composite anemia outcomes, especially anemia event occurrences. SGLT2 inhibitors may be considered as an adjunct therapy to reduce anemia incidence in patients with T2D and CKD.

Cross-Regional Data Initiative for the Assessment and Development of Treatment for Neurological and Mental Disorders.

Purpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN). Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives. Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK. Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.

Association between recently raised anticholinergic burden and risk of acute cardiovascular events: nationwide case-case-time-control study.

OBJECTIVE: To evaluate the association between recently raised anticholinergic burden and risk of acute cardiovascular events in older adults. DESIGN: Case-case-time-control study (ie, incorporating a case crossover design and a control crossover design consisting of future cases). SETTING: Taiwan's National Health Insurance Research Database. PARTICIPANTS: 317 446 adults aged ≥65 who were admitted to hospital because of an incident acute cardiovascular event between 2011 and 2018. Acute cardiovascular events included myocardial infarction, strokes, arrhythmias, conduction disorders, and cardiovascular death. MAIN OUTCOME MEASURES: The anticholinergic burden was measured for each participant by adding up the anticholinergic scores for individual drugs using the Anticholinergic Cognitive Burden Scale. Scores were classified into three levels (0 points, 1-2 points, and ≥3 points). For each participant, anticholinergic burden levels during hazard periods (day -1 to -30 before the cardiovascular event) were compared with randomly selected 30 day reference periods (ie, periods between days -61 and -180). Conditional logistic regression determined odds ratios with 95% confidence intervals to evaluate the association between acute cardiovascular events and recently raised anticholinergic burden. RESULTS: The crossover analyses included 248 579 current cases. Participants' average age on the index date was 78.4 years (standard deviation 0.01), and 53.4% were men. The most frequently prescribed drugs with anticholinergic activity were antihistamines (68.9%), gastrointestinal antispasmodics (40.9%), and diuretics (33.8%). Among patients with varying levels of anticholinergic burden in different periods, more patients carried higher levels of anticholinergic burden during hazard periods than during reference periods. For example, 17 603 current cases had 1-2 points of anticholinergic burden in the hazard period with 0 points in the reference period, while 8507 current cases had 0 points in the hazard period and 1-2 points in the reference period. In the comparison of 1-2 points versus 0 points of anticholinergic burden, the odds ratio was 1.86 (95% confidence interval 1.83 to 1.90) in the case crossover analysis and 1.35 (1.33 to 1.38) in the control crossover analysis, which yielded a case-case-time-control odds ratio of 1.38 (1.34 to 1.42). Similar results were found in the comparison of ≥3 versus 0 points (2.03, 1.98 to 2.09) and ≥3 versus 1-2 points (1.48, 1.44 to 1.52). The findings remained consistent throughout a series of sensitivity analyses (eg, cut-off points for anticholinergic burden categories were redefined and different scales were used to measure anticholinergic burden). CONCLUSIONS: An association was found between recently raised anticholinergic burden and increased risk of acute cardiovascular events. Furthermore, a greater increase in anticholinergic burden was associated with a higher risk of acute cardiovascular events.

Machine learning applications on neonatal sepsis treatment: a scoping review.

INTRODUCTION: Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment. METHODS: PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning. RESULTS: There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks. CONCLUSION: Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.

Post-discharge use of antipsychotics in patients with hospital-acquired delirium and associated risk of mortality - A population-based nested case-control study.

OBJECTIVE: To evaluate post-discharge use of antipsychotics in patients with incident hospital-acquired delirium and the associated risk of mortality. METHODS: We conducted a nested case-control study for patients newly diagnosed with hospital-acquired delirium and subsequently discharged from hospital using Taiwan's National Health Insurance Database (NHID) from 2011 to 2018. RESULTS: The use of antipsychotics after discharge did not increase the risk of mortality (adjusted OR: 1·03; 95% CI: 0·98-1·09). CONCLUSIONS: The findings suggested that using antipsychotics after discharge in patients with hospital-acquired delirium may not increase the risk of mortality.