Introduction: The 2021 IDF-DAR risk calculator had been previously evaluated in multiple studies and subsequently widely accepted and applied in clinical practice as a practical standardized tool for patient risk stratification. Recently updated, the 2026 IDF-DAR Risk calculator enables a more individualized, evidence-related evaluation of patientrelated and disease-related risk factors, incorporating modern diabetes technologies, including continuous glucose monitoring (CGM), automated insulin delivery (AID) systems, and advanced insulin formulations to enhance risk stratification. This tool allows medical professionals to tailor Ramadan practices based on overall factors toward promoting safe fasting. Methodology: This prospective multicentre observational study recruited adults with Type 1 and Type 2 diabetes attending public hospitals nationwide. People with diabetes (PwD) intending to perform Ramadan fasting were invited to participate and assessed using the 2026 IDF-DAR Risk Calculator in the 6-week pre-Ramadan period between 30th January and 19th March 2026. Results: A total of 458 PwD were evaluated and stratified into low (15.7%), moderate (41%), and high risk (43.3%) categories. Most participants had Type 2 diabetes (83.6%), with 60.3% having a disease duration exceeding 10 years and 43% exhibiting poor glycemic control (hemoglobin A1c >9%). Insulin therapy was used by 76.4% of participants, including two individuals with Type 1 diabetes using AID systems. Most participants reported no recent hypoglycemia (76.4%), 81.0% performed glucose monitoring, and 3.3% used CGM. Severe comorbidities were uncommon, with 1.1% having unstable macrovascular disease and 4.4% advanced chronic kidney disease (estimated glomerular filtration rate <30). Notably, 72.2% received structured Ramadan education. Conclusion Majority of PwD attending tertiary diabetes clinics were in the moderate- to high-risk category and intended to fast despite medical advice against fasting in some cases. Although most participants were on insulin therapy, hypoglycemia was low in the pre-Ramadan period. Integration of modern technologies, advanced insulin therapies, and structured education may support safer fasting practices.
Risk Assessment ; Diabetes Mellitus ; Hospitals ; Fasting

Hospitals worldwide recognise the importance of data and digital transformation in healthcare. We traced a smart hospital's data-driven journey to build an artificial intelligence and digital ecosystem (AIDE) to achieve healthcare excellence. We measured the impact of data and digital transformation on patient care and hospital operations, identifying key success factors, challenges, and opportunities. The use of data analytics and data science, robotic process automation, AI, cloud computing, Medical Internet of Things and robotics were stand-out areas for a hospital's data-driven journey. In the future, the adoption of a robust AI governance framework, enterprise risk management system, AI assurance and AI literacy are critical for success. Hospitals must adopt a digital-ready, digital-first strategy to build a thriving healthcare system and innovate care for tomorrow.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Hospitals ; Cloud Computing ; Robotics ; Internet of Things ; Data Science

Background/Aims: Chronic hepatitis B (CHB) and fatty liver (FL) often co-exist, but natural history data of this dual condition (CHB-FL) are sparse. Via a systematic review, conventional meta-analysis (MA) and individual patient-level data MA (IPDMA), we compared liver-related outcomes and mortality between CHB-FL and CHB-no FL patients. Methods: We searched 4 databases from inception to December 2021 and pooled study-level estimates using a random- effects model for conventional MA. For IPDMA, we evaluated outcomes after balancing the two study groups with inverse probability treatment weighting (IPTW) on age, sex, cirrhosis, diabetes, ALT, HBeAg, HBV DNA, and antiviral treatment. Results: We screened 2,157 articles and included 19 eligible studies (17,955 patients: 11,908 CHB-no FL; 6,047 CHB-FL) in conventional MA, which found severe heterogeneity (I2=88–95%) and no significant differences in HCC, cirrhosis, mortality, or HBsAg seroclearance incidence (P=0.27–0.93). IPDMA included 13,262 patients: 8,625 CHB-no FL and 4,637 CHB-FL patients who differed in several characteristics. The IPTW cohort included 6,955 CHB-no FL and 3,346 CHB-FL well-matched patients. CHB-FL patients (vs. CHB-no FL) had significantly lower HCC, cirrhosis, mortality and higher HBsAg seroclearance incidence (all p≤0.002), with consistent results in subgroups. CHB-FL diagnosed by liver biopsy had a higher 10-year cumulative HCC incidence than CHB-FL diagnosed with non-invasive methods (63.6% vs. 4.3%, p<0.0001). Conclusions IPDMA data with well-matched CHB patient groups showed that FL (vs. no FL) was associated with significantly lower HCC, cirrhosis, and mortality risk and higher HBsAg seroclearance probability.

INTRODUCTIONIntravenous tissue plasminogen activator (tPA) within 3 hours of stroke onset is a licensed proven therapy for ischaemic stroke, with recent trial data showing benefit up to 4.5 hours. We previously published in this journal data of a survey conducted in 2004 showing only 9% of ischaemic stroke patients presenting to the Singapore General Hospital (SGH) arrived within 2 hours of onset. We aimed to determine whether the problem of delayed hospital arrival persists in 2009 and to establish the impact of widening the time window for intravenous tPA to 4.5 hours.

MATERIALS AND METHODSWe prospectively surveyed consecutive ischaemic stroke patients admitted to the SGH from 9th March to 30th April 2009. Patients and/or relatives were interviewed with a standardised form similar to the 2004 survey.

RESULTSAmong the 146 ischaemic stroke patients surveyed (median age 67 years, 59% male, median NIHSS score 2), 6% presented to SGH within 2 hours and 15% within 3.5 hours of onset. Median time from stroke onset to hospital arrival was 1245 minutes (20.75 hours). Pre-hospital consultation was significantly associated with hospital arrival after 2 hours from onset. Main reasons cited for delay were not realising the gravity of symptoms (31%) and not recognising them as stroke (27%).

CONCLUSIONDelayed arrival to SGH following acute ischaemic stroke remains a problem in 2009. This confirms the lack of stroke awareness in Singapore and highlights the need for public stroke education. Furthermore, these data confirm that widening the time window for intravenous tPA treatment to 4.5 hours at SGH will increase its utilisation.

Acute Disease ; therapy ; Aged ; Emergency Service, Hospital ; statistics & numerical data ; Female ; Hospitalization ; trends ; Humans ; Male ; Middle Aged ; Patient Acceptance of Health Care ; statistics & numerical data ; Prospective Studies ; Stroke ; therapy ; Time Factors