St. Luke's Medical Center

BACKGROUND:The worldwide acceleration of digital transformation in healthcare underscores the need for professionals to continuously adapt and sustain robust digital health competence, shaped not only by individual characteristics and institutional environments but also by broader social, cultural, and geopolitical factors. OBJECTIVE:This study aimed to identify distinct clusters of digital health competence among healthcare professionals across 19 diverse countries and regions, and to examine the factors influencing the development and distribution of these competence clusters. METHODS:A cross-sectional international survey study was conducted between 2023 and 2024, using a validated survey instrument measuring digital health competence and its influencing factors. Data were collected from healthcare professionals in 19 countries and regions (n = 6440; n = 5945 used for this study), following a harmonised protocol with shared demographic templates and instruments. K-means cluster analysis was employed to derive digital competence profiles, with comparative analyses conducted to investigate associations between the identified clusters and individual characteristics (e.g., age, education, professional experience). RESULTS:Five distinct clusters of digital health competence were identified: (1) Beginners, (2) Developing Professionals, (3) Emerging Users, (4) Proficient Practitioners, and (5) Pioneers. Higher competence clusters (4 and 5) were associated with younger age, higher education, hospital-based work, and stronger perceived support from management, organisational structures, and colleagues. In contrast, lower-performing clusters reported limited digital engagement and minimal support. Perceived leadership influence, particularly managerial commitment to digital change, was a key differentiator across clusters. CONCLUSIONS:The findings demonstrate substantial variation in digital health competence across healthcare professionals internationally. Cluster-specific strategies, such as targeted upskilling, peer mentoring, and leadership engagement, are needed to address competence gaps. The results provide a foundation for policy development and workforce training frameworks aimed at strengthening digital readiness in global healthcare systems. Future research should explore longitudinal competence development and evaluate targeted interventions.

MRI plays a central role in the diagnosis and management of multiple sclerosis (MS), and the 2017 revised McDonald criteria have improved the sensitivity and accuracy for early diagnosis. While these criteria have been validated and improve patient outcomes, the improper application of these practice standards-especially in cases of atypical presentations-can lead to misdiagnosis. To enhance diagnostic specificity, emerging MRI biomarkers such as the central vein sign and paramagnetic rim lesions are now incorporated into the 2024 revised McDonald criteria. These biomarkers show promise for differentiating MS from mimicking conditions. Additional emerging techniques, including artificial intelligence-driven analysis, also have the potential to refine diagnostic clinical workflows by capturing subtle imaging features that are beyond human perception. Accurate interpretation of these MRI features is essential to maintaining diagnostic accuracy across clinical settings. This review highlights conventional and emerging MRI biomarkers for MS diagnosis, highlighting the diagnostic and prognostic utilities of and underlying pathophysiologic processes behind these imaging findings.

Bronchiectasis is a debilitating chronic respiratory condition characterized by a vicious cycle of infection, inflammation, and airway destruction, leading to frequent pulmonary exacerbations. While long-term macrolide therapy, particularly azithromycin, has been proposed for its antibiotic and anti-inflammatory effects, previous systematic reviews have reported inconsistent findings or failed to include the most recent randomized controlled trials (RCTs). This study aimed to provide a comprehensive, updated meta-analysis of the efficacy and safety of long-term azithromycin in reducing pulmonary exacerbations. We systematically searched PubMed, ClinicalTrials.gov, and the Cochrane Library through December 2025, identifying six RCTs involving 562 patients. The analysis revealed that long-term azithromycin prophylaxis significantly reduced the rate of pulmonary exacerbations compared to placebo, with a pooled standardized mean difference (SMD) of -0.63 (95% CI, -0.90 to -0.35; p < 0.00001). Subgroup analysis demonstrated consistent benefits in both adult (SMD -1.03) and pediatric (SMD -1.52) populations (p = 0.10 for subgroup difference), despite moderate heterogeneity (I² = 58%). While treatment did not yield statistically significant improvements in forced expiratory volume in 1 second (FEV1) predicted (p = 0.08) or St. George's Respiratory Questionnaire (SGRQ) scores (p = 0.81), the incidence of adverse events was comparable to placebo (relative risk (RR) 1.24; 95% CI, 0.95 to 1.61; p = 0.11). These findings indicate that azithromycin is a safe and effective intervention for preventing exacerbations in both children and adults and should be considered in the management of patients with frequent respiratory events.

Background: Non-group 1 pulmonary hypertension, also known as secondary pulmonary hypertension (SPH), is predominantly observed among females. However, there is a significant lack of data concerning factors associated with hospitalization among patients diagnosed with SPH. This study aims to provide clinicians with vital insights for the identification of high-risk groups and for the more effective management of contributory risk factors within the female population affected by SPH. Methods: Using the 2019 National Inpatient Sample, we identified female admissions with SPH (n = 648,190), accounting for 3.8% of the total 17,236,228 female admissions. An Artificial Neural Network (ANN) analysis was conducted to evaluate predictive factors. We randomly allocated 3,319,543 patients into training and testing datasets at a ratio of 70:30, comprising 2,323,696 (70%) for training and 995,847 (30%) for testing, to calibrate and validate the performance of the ANN algorithm. Model performance was assessed by comparing misclassification rates between training and testing sets and by the area under the receiver operating characteristic curve (AUC); only internal validation was performed. Results: Females hospitalized with SPH were generally of older age, with a median of 75 years compared to 58 years, and more frequently identified as White (67.7% versus 65.5%) or Black (20.5% versus 15.5%) relative to those without SPH. They also demonstrated a higher prevalence of most atherosclerotic cardiovascular disease (ASCVD) risk factors or their equivalents, including complicated hypertension (50.6% versus 17.8%), diabetes with chronic complications (30.6% versus 13.7%), and hyperlipidemia (50.8% versus 29.2%), as well as other comorbidities such as COPD (43.4% versus 20.2%) and CKD (43.3% versus 14.0%), and exhibited increased all-cause mortality (4.5% versus 1.8%) (p < 0.001). Our ANN model achieved an AUC of 0.823, indicating good predictive capability. The rates of incorrect predictions were comparable in both the testing and training cohorts, at 3.8% each. The factors most strongly associated with a coded SPH diagnosis included age at admission, complicated hypertension, chronic kidney disease, chronic obstructive pulmonary disease, uncomplicated hypertension, prior VTE, race, arthropathies, and AIDS. Conclusions: Our ANN model identified demographic and comorbidity factors associated with a coded SPH diagnosis among hospitalized females, with good discrimination (AUC = 0.823). Because the model classifies the presence of an existing diagnosis rather than predicting future hospitalization, and was validated only internally, external and prospective validation is required before clinical application. Once validated, these factors could support individualized, sex-specific risk stratification for high-risk female populations, consistent with the goals of personalized medicine.

Marginal zone lymphoma (MZL) represents a diverse subgroup of non-Hodgkin lymphoma, with limited data to guide therapeutic decision making, particularly in Asian settings. To understand the factors for decision-making and the current management strategies for MZL in Asia, an online survey using a Best-Worst Scaling (BWS) instrument was conducted amongst hemato-oncologists (n = 18) representing eleven countries/territories in Southeast and East Asia. This was followed by a focus group discussion to refine the validity and interpretation of the survey results. Amongst all patients with MZL, approximately one-third are initially managed on a “watch and wait” strategy. The most important clinical characteristics influencing decision making for initiating systemic therapy include the presence of compression syndrome, followed by cytopenia and bulky disease. Both efficacy indicators such as “overall survival”, “overall response rate”, and “progression-free survival” as well as toxicity indicators “treatment-related cardiovascular-related side effects” and “treatment-related death” were rated as the topmost important factors in deciding treatment initiation for patients. In terms of treatment-related toxicities, cardiovascular side effects and death ranked as more important considerations, as compared with bleeding and infection risks. In addition, patient affordability was amongst the top considerations for initiating therapy, with financial barriers presenting as the major challenge for preferred use of BTK inhibitors and other standard therapies. Efficacy outcomes, balanced against treatment-related toxicity, are main considerations for systemic therapy initiation, and should be personalized for each patient. Financial barriers remain as the major challenge for the optimal treatment of patients with MZL in Asia.