METHODS: In this single centre, retrospective cohort study, we linked a detailed clinical registry with provincial administrative databases to obtain short and long-term outcomes. Validated algorithms were used to established baseline comorbidities and adverse outcomes.
RESULTS: Between 1999 and 2017, 479 patients had PFO closure with an Amplatzer PFO Occluder. The average age of the patients was 47.3 years (standard deviation (SD) = 12.4), and 54.7% were males. The procedural success was 100%, and 96% of patients were discharged on the same day. Any in-hospital complication was observed in 2.5% (n = 12) of patients. At 30 days post-discharge, 18% of patients had an ED visit and 5% a hospitalization. Over a mean follow-up of 9.1 (SD = 3.8) years, 4% experienced TIA, 1.5% stroke, and 7.6% atrial fibrillation. The composite outcome of stroke/TIA/death was observed in 10.9% of patients (1.22 events per 100 person-years). Patients >60 years old experienced higher rates of adverse events than younger patients.
CONCLUSIONS: In this large real-world cohort of patients with cryptogenic stroke, we observed excellent safety and effectiveness outcomes for PFO closure conducted with Amplatzer PFO Occluder, similar to randomized controlled trials or other long-term cohort studies. New onset atrial fibrillation was one of the most commonly adverse events. Future studies should investigate early post-discharge management of patients to prevent readmissions.
METHODOLOGY: The Asia Pacific Evaluation of Cardiovascular Therapies (ASPECT) collaboration consists of data from various PCI registries across Australia, Hong Kong, Singapore, Malaysia, Indonesia and Vietnam. Clinical characteristics, lesion characteristics, and outcomes were provided for STEMI patients. Key outcomes included 30-day overall mortality and major adverse cardiovascular events (MACE).
RESULTS: A total of 12,144 STEMI patients (mean(SD) age 59.3(12.3)) were included, of which 3912 (32.2%) had diabetes. Patients with diabetes were likely to have a higher baseline risk profile, poorer clinical presentation, and more complex lesion patterns (all p
METHODS: Medline and Embase were searched for articles reporting outcomes of ACS patients stratified by SES using a multidimensional index, comprising at least 2 of the following components: Income, Education and Employment. A comparative meta-analysis was conducted using random-effects models to estimate the risk ratio of all-cause mortality in low SES vs high SES populations, stratified according to geographical region, study year, follow-up duration and SES index.
RESULTS: A total of 29 studies comprising of 301,340 individuals were included, of whom 43.7% were classified as low SES. While patients of both SES groups had similar cardiovascular risk profiles, ACS patients of low SES had significantly higher risk of all-cause mortality (adjusted HR:1.19, 95%CI: 1.10-1.1.29, p
METHODS: A panel comprising cardiologists from China, Hong Kong, India, Japan, Malaysia, Pakistan, Philippines, Singapore, South Korea, Taiwan, and Thailand convened to share insights and provide guidance for the optimal management of iron deficiency in patients with HF, tailored for the Asian community.
RESULTS: Expert opinions were provided for the screening, diagnosis, treatment and monitoring of iron deficiency in patients with HF. It was recommended that all patients with HF with reduced ejection fraction should be screened for iron deficiency, and iron-deficient patients should be treated with intravenous iron. Monitoring of iron levels in patients with HF should be carried out once or twice yearly. Barriers to the management of iron deficiency in patients with HF in the region include low awareness of iron deficiency amongst general physicians, lack of reimbursement for screening and treatment, and lack of proper facilities for administration of intravenous iron.
CONCLUSIONS: These recommendations provide a structured approach to the management of iron deficiency in patients with HF in Asia.
METHODS AND RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).
CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.