Objective: Patients with severe symptomatic aortic stenosis are assessed for coronary artery disease (CAD) prior to transcatheter aortic valve implantation (TAVI) with treatment implications. Invasive coronary angiography (ICA) is the recommended modality but is associated with peri-procedural complications. Integrating machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) into existing TAVI-planning CT protocol may aid exclusion of significant CAD and thus avoiding ICA in selected patients. Materials and Methods: A single-center, retrospective study was conducted, 41 TAVI candidates with both TAVI-planning CT and ICA performed were analyzed. CT datasets were evaluated by a ML-based CT-FFR software. Beta-blocker and nitroglycerin were not administered in these patients. The primary outcome was to identify significant CAD. The diagnostic performance of CT-FFR was compared against ICA. Results: On per-patient level, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy were 89%, 94%, 80%, 97% and 93%, respectively. On per-vessel level, the sensitivity, specificity, PPV, NPV and diagnostic accuracy were 75%, 94%, 67%, 96% and 92%, respectively. The area under the receiver operative characteristics curve per individual coronary vessels yielded overall 0.90 (95% confidence interval 85%–95%). ICA may be avoided in up to 80% of patients if CT-FFR results were negative. Conclusion ML-based CT-FFR can provide accurate screening capabilities for significant CAD thus avoiding ICA. Its integration to existing TAVI-planning CT is feasible with the potential of improving the safety and efficiency of pre-TAVI CAD assessment.

INTRODUCTION: Obstructive sleep apnoea (OSA) is common in Singapore, with moderate to severe OSA affecting around 30% of residents. These consensus statements aim to provide scientifically grounded recommendations for the management of OSA, standar-dise the management of OSA in Singapore and promote multidisciplinary collaboration. METHOD: An expert panel, which was convened in 2024, identified several areas of OSA management that require guidance. The expert panel reviewed the current literature and developed consensus statements, which were later independently voted on using a 3-point Likert scale (agree, neutral or disagree). Consensus (total ratings of agree and neutral) was set a priori at ≥80% agreement. Any statement not reaching consensus was excluded. RESULTS: The final consensus included 49 statements that provide guidance on the screening, diagnosis and management of adults with OSA. Additionally, 23 statements on the screening, diagnosis and management of paediatric OSA achieved consensus. These 72 consensus statements considered not only the latest clinical evidence but also the benefits and harms, resource implications, feasibility, acceptability and equity impact of the recommendations. CONCLUSION The statements presented in this paper aim to guide clinicians based on the most updated evidence and collective expert opinion from sleep specialists in Singapore. These recommendations should augment clinical judgement rather than replace it. Management decisions should be individualised, taking into account the patient's clinical characteristics, as well as patient and caregiver concerns and preferences.
Humans ; Sleep Apnea, Obstructive/diagnosis* ; Singapore ; Consensus ; Adult

We review the evolution of Brachytherapy to interventional radiotherapy and its current and potential roles in HNC management, and the requirements and challenges towards its effective and sustainable implementation in SEA.
Brachytherapy ; Radiotherapy

Background: We evaluated changes in glycemic status, over 1 year, of coronavirus disease 2019 (COVID-19) survivors with dysglycemia in acute COVID-19. Methods: COVID-19 survivors who had dysglycemia (defined by glycosylated hemoglobin [HbA1c] 5.7% to 6.4% or random glucose ≥10.0 mmol/L) in acute COVID-19 were recruited from a major COVID-19 treatment center from September to October 2020. Matched non-COVID controls were recruited from community. The 75-g oral glucose tolerance test (OGTT) were performed at baseline (6 weeks after acute COVID-19) and 1 year after acute COVID-19, with HbA1c, insulin and C-peptide measurements. Progression in glycemic status was defined by progression from normoglycemia to prediabetes/diabetes, or prediabetes to diabetes. Results: Fifty-two COVID-19 survivors were recruited. Compared with non-COVID controls, they had higher C-peptide (P< 0.001) and trend towards higher homeostasis model assessment of insulin resistance (P=0.065). Forty-three COVID-19 survivors attended 1-year reassessment. HbA1c increased from 5.5%±0.3% to 5.7%±0.2% (P<0.001), with increases in glucose on OGTT at fasting (P=0.089), 30-minute (P=0.126), 1-hour (P=0.014), and 2-hour (P=0.165). At baseline, 19 subjects had normoglycemia, 23 had prediabetes, and one had diabetes. Over 1 year, 10 subjects (23.8%; of 42 non-diabetes subjects at baseline) had progression in glycemic status. C-peptide levels remained unchanged (P=0.835). Matsuda index decreased (P=0.007) and there was a trend of body mass index increase from 24.4±2.7 kg/m2 to 25.6±5.2 (P=0.083). Subjects with progression in glycemic status had more severe COVID-19 illness than non-progressors (P=0.030). Reassessment was not performed in the control group. Conclusion Subjects who had dysglycemia in acute COVID-19 were characterized by insulin resistance. Over 1 year, a quarter had progression in glycemic status, especially those with more severe COVID-19. Importantly, there was no significant deterioration in insulin secretory capacity.