Background/Aims: Patients with concurrent type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD) face elevated cardiovascular risks. However, optimal oral antidiabetic drug (OAD) selection for this population remains unclear. Methods: Using the Korean National Health Information Database, we conducted a target trial emulation comparing cardiovascular outcomes among patients with T2DM and MASLD (defined by fatty liver index ≥30) who initiated sodium-glucose cotransporter 2 (SGLT2) inhibitors, thiazolidinediones, dipeptidyl peptidase-4 (DPP-4) inhibitors, or sulfonylureas with metformin. The primary outcome was major adverse cardiovascular events (MACE), including cardiovascular mortality, nonfatal myocardial infarction, and nonfatal stroke. Results: Among 71,071 patients (331,726 person-years), SGLT2 inhibitor users experienced a significantly lower MACE risk compared to sulfonylurea users (adjusted subdistribution hazard ratio [aSHR], 0.44; 95% confidence interval [CI], 0.31–0.62). SGLT2 inhibitors also demonstrated a lower MACE risk compared to thiazolidinediones (aSHR, 0.61; 95% CI, 0.39–0.96) and DPP-4 inhibitors (aSHR, 0.59; 95% CI, 0.42–0.96). Cardiovascular mortality risk was notably reduced with SGLT2 inhibitors compared to sulfonylureas (aSHR, 0.13; 95% CI, 0.03–0.50), thiazolidinediones (aSHR, 0.19; 95% CI, 0.04–0.86), and DPP-4 inhibitors (aSHR, 0.22; 95% CI, 0.06–0.84). Mediation analysis revealed that MASLD regression accounted for 8.7% of the total cardiovascular benefit when comparing SGLT2 inhibitors to sulfonylureas. Conclusions In patients with concurrent T2DM and MASLD, SGLT2 inhibitors demonstrated better cardiovascular outcomes compared to other OADs. These findings suggest that SGLT2 inhibitors may be the preferred OAD choice for cardiovascular risk reduction in this high-risk population.

Background and Objectives: Clinical characteristics and outcomes in acute heart failure (AHF) vary by phenotype. We assessed phenotype-specific features, treatment patterns, and outcomes in a nationwide Korean cohort. Methods: The Korean Heart Failure III registry prospectively enrolled 7,351 AHF admissions at 47 hospitals. Among 6,777 patients with available left ventricular ejection fraction (EF), phenotypes were defined as heart failure with reduced EF (HFrEF, ≤40%), mildly reduced EF (HFmrEF,41–49%), or preserved EF (HFpEF, ≥50%). The primary endpoint was a 12-month composite of all-cause death or heart transplantation, evaluated from index admission and, among hospital survivors, from discharge. We used inverse probability weighting (multinomial generalized boosted models with stabilized, trimmed weights) and weighted Cox proportional-hazards models to estimate hazard ratios (HRs). Results: Phenotype distribution was 58.9% HFrEF, 13.6% HFmrEF, and 27.5% HFpEF. Crude 12-month composite rates from index admission were 13.4% (HFrEF), 12.7% (HFmrEF), and 16.8% (HFpEF). After weighting, from index admission, HFmrEF (HR, 0.892; 95% confidence interval [CI], 0.731–1.088) and HFpEF (HR, 1.101; 95% CI, 0.939–1.291) did not differ from HFrEF; from discharge, HFpEF had modestly higher risk (HR, 1.207; 95% CI, 1.008–1.445) whereas HFmrEF did not (HR, 1.039; 95% CI, 0.844–1.279). Hyponatremia and chronic kidney disease were consistent adverse markers, while angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker use at discharge was protective. Conclusions Across the EF spectrum, phenotypes showed distinct profiles and risk. Postdischarge risk was modestly higher in HFpEF, supporting phenotype-tailored care and systematic discharge optimization in Korean patients with AHF.

Background and Objectives: We evaluated 20-year trends in heart failure (HF) epidemiology in Korea to quantify changes in its burden from 2002 to 2023. Methods: A nationwide analysis was conducted using a random 50% sample from the Korean National Health Information Database linked to mortality records (2002–2023). HF was defined using diagnostic codes recorded as a primary or secondary condition. We calculated crude and age-standardized rates of prevalence, incidence, hospitalization, and mortality. Survival was assessed using the Kaplan–Meier method, stratified by inpatient versus outpatient diagnosis.Trends in heart transplantation and left ventricular assist device implantations were also examined. Results: By 2023, approximately 1,750,228 individuals had HF (3.41% prevalence). The age-standardized prevalence has more than doubled from 2002 to 2023. The crude incidence increased over time; the age-standardized incidence remained stable in men and declined in women.Hospitalization rates for any cause or secondary HF diagnoses have increased substantially, whereas primary HF hospitalization rates have remained relatively stable. The annual mortality rate in patients with HF was approximately 6.0% in 2023, being markedly higher in older adults.Although short-term survival has improved, particularly in hospitalized patients, long-term survival remains limited. Use of advanced therapies significantly increased. Conclusions The burden of HF in Korea has increased substantially over the past two decades, driven primarily by population aging and improved survival rather than increasing age-adjusted incidence. Despite therapeutic advances, hospitalization and long-term mortality rates remain high, highlighting the need for comprehensive HF strategies in aging societies.

Heart failure (HF) prevalence and associated socio-economic costs have risen rapidly in Korea, creating urgent demand for a national, standardized prospective registry to support quality monitoring, outcome comparison, and real-world evidence. Standardized data definitions are essential for valid comparisons across studies and healthcare settings. We created a unified national HF cohort by integrating the two largest Korean registries, KorAHF (2011–2014;n=5,625) and KorHF III (2018–2023; n=7,351), yielding 12,976 hospitalized HF patients. Because heterogeneous variable definitions impeded direct comparisons, we implemented a harmonization pipeline across demographic, clinical, imaging, biomarker, therapeutic, and outcome domains, aligned with international standards and reinforced by systematic data quality checks.A multidisciplinary panel then conducted a structured decision process to produce the Korean HF Standard Dataset, a tiered and interoperable variable set spanning six domains: demographics, presentation at admission, in-hospital events and comorbidities, discharge status, outpatient follow-up, and readmission events. This integrated platform and standardized variables enable precise identification of HF patient phenotypes and prognostic factors, establishing an evidence-based foundation for disease management. This supports an analysis of temporal changes in treatment patterns and outcomes, generates data to inform future clinical practice guidelines, and provides a practical basis for planning large, continuous, prospective HF registries. Furthermore, it also facilitates pragmatic, registry-based randomized clinical trials and unmet-needs assessments that can guide national health policy. Establishing a foundation for future linkage with national health databases, this platform will allow for enhanced accuracy, comparability, and representativeness, powering quality improvement initiatives and population-level monitoring of Korea’s growing HF burden.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Background: Dual energy X-ray absorptiometry (DXA) measurements vary among instruments from different manufacturers. This study aimed to assess the correlation between bone mineral density (BMD) and body composition measured by the GE Lunar Prodigy and Hologic Horizon W DXA systems and to develop conversion equations. Methods: A total of 120 healthy volunteers (59 males, 61 females) aged 20 to 70 years were scanned on both DXA systems on the same day. BMD was measured at the lumbar spine (L1-L4) and bilateral femurs. Body composition parameters were measured for the whole body and regional sites. Bland-Altman analysis, Pearson correlation, and concordance correlation coefficient (CCC) were used to assess agreement. Linear regression was performed to derive conversion equations. Results: The Hologic system measured BMD values 14% to 20% lower at the lumbar spine and 8% to 17% lower at femoral sites compared to the GE Lunar system (all P<0.0001). Lean mass values were 4% to 10% lower on the Hologic system. Very strong correlations were observed for both BMD (r=0.944-0.980) and lean mass (r=0.963-0.984). Cross-calibration equations were developed for all measurement sites. Conclusions Despite significant systematic differences, there was very high correlation between the two DXA systems. The conversion equations can facilitate comparison of measurements between these systems in clinical practice and research.

Purpose: Based on the KEYNOTE-522 study, neoadjuvant pembrolizumab plus chemotherapy has become the standard treatment for early-stage triple-negative breast cancer (TNBC).This study evaluated the real-world efficacy, safety, and predictors of pathologic complete response (pCR) in Korean patients. Methods: We conducted a retrospective cohort study of 174 patients with early-stage TNBC who received the KEYNOTE-522 regimen (neoadjuvant pembrolizumab plus paclitaxel and carboplatin, followed by doxorubicin and cyclophosphamide) at a tertiary cancer center between August 2022 and July 2024. We assessed the primary endpoints, including pCR rate and event-free survival (EFS). We performed univariable and multivariable logistic regression analyses to identify independent predictors of pCR. Results: The median patient age was 50 years (range, 24–74 years). The clinical stages were II and III in 79.3% and 20.1% of patients, respectively, and 10.9% had clinical N3 disease. The overall pCR rate was 62.1%, and the N3 subgroup had a pCR rate of 47.4%. On multivariable analysis, high baseline Ki-67 expression (≥ median, 75%) was significantly associated with pCR (odds ratio, 2.84; 95% confidence interval, 1.45 to 5.66; p = 0.002). At a median followup of 18.4 months, the 12-month EFS rate was 97.4%, with significantly superior outcomes observed in patients who achieved pCR compared with those who did not achieve pCR (100% vs. 93.1%, p = 0.007). The treatment completion rate was 92.0%, and immune-related adverse events occurred in 13.8% of patients. Conclusion In this real-world analysis of one of the largest Asian cohorts of patients with earlystage TNBC treated with neoadjuvant pembrolizumab, the KEYNOTE-522 regimen demonstrated substantial efficacy and manageable toxicity, consistent with the original trial findings.

Background: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) mod‑ els show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients. Methods: In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating char‑ acteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan–Meier analysis was used to compare clinical outcomes. Results: Among the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924–0.937]; AUPRC, 0.913 [95% CI, 0.902–0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53–2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28). Conclusions AiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, support‑ ing its clinical utility for early detection and patient management.

Reference management software (RMS) represents a cornerstone of modern academic writing and publishing. For decades, programs such as EndNote, Zotero, and Mendeley have played central roles in facilitating citation organization, bibliography formatting, and collaborative scholarship. Although each platform has introduced unique innovations, persistent limitations remain, particularly with respect to usability, accessibility, and accuracy. In parallel, the rise of generative artificial intelligence has introduced an unprecedented challenge: the inadvertent inclusion of fabricated or incorrect references mistakenly incorporated into manuscripts. This phenomenon has exposed a critical limitation of traditional RMS platforms, namely their inability to verify reference authenticity. Against this backdrop, new solutions have emerged. One such example is CiteWell (https://citewell.org/), an artificial intelligence (AI)-era RMS that introduces several notable innovations, including PubMed-integrated verification, an intuitive interface for new users, customizable journal-specific styles, and multilingual accessibility. This review provides a comprehensive historical overview of RMS, evaluates the strengths and weaknesses of major platforms, and positions emerging AI-based tools as a new paradigm that combines traditional reference management with essential safeguards for contemporary academic challenges.