ProtecT-2-D trial protocol: cardiovascular protection in patients with type 2 diabetes and established heart and/or vascular disease at a cardio-metabolic clinic-a randomized controlled trial.

BACKGROUND: Cardiovascular disease remains the primary cause of morbidity and mortality despite advancements in the treatment of patients with type 2 diabetes. Effective diabetes management extends beyond blood glucose control and includes cardiovascular prevention and treatment. However, the conventional healthcare model often emphasizes single-disease-specific management, leading to fragmented care. We aim to establish an affordable Cardio-Metabolic Clinic (CMC) that can provide comprehensive assessment and specialized care with a focus on cardiovascular protection. METHODS: The ProtecT-2-D study is a prospective, randomized control trial at the Cardiovascular Research Unit, Odense University Hospital Svendborg, Denmark. In this study, 1500 participants with type 2 diabetes and cardiovascular disease will be randomly assigned in a 2:1 ratio to receive either the intervention: treatment in the CMC, or the control: standard of care. The Cardio-Metabolic Clinic applies a decision-making algorithm coded with the latest guidelines to evaluate lifestyle factors and manage medical treatment. Health examinations are conducted at baseline and after three years, and clinical events will be assessed through registry and journal audits after five and ten years. The primary outcome is the time to the first occurrence of a composite of cardiovascular deaths, non-fatal acute myocardial infarctions, non-fatal stroke, or hospitalization due to heart failure at a time frame of five years. DISCUSSION: The Cardio-Metabolic Clinic represents a pioneering approach to diabetes management that aims to improve patient outcomes by reducing the cardiovascular disease burden. This study could transform diabetes care and offer a multidisciplinary, cost-effective, and specialized treatment. We need to establish the efficacy and feasibility of a CMC to integrate comparable clinics into broader healthcare systems, and potentially enhance cardiovascular health in patients with type 2 diabetes. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT06203860.

Statistical Analysis Plan for the AIRCARD Study: Individual Long-term Air and Noise Pollution Exposure and Cardiovascular Disease Incidence and Mortality (AIRCARD): A Prospective Cohort Study Utilizing DANCAVAS and VIVA Screening Trials.

INTRODUCTION: The AIRCARD study is designed to investigate the relationship between long-term exposure to air and noise pollution and cardiovascular disease incidence and mortality. We aim to conduct a robust prospective cohort analysis assessing the cumulative and differential impacts of air and noise pollution exposure on cardiovascular disease and mortality. This study will adjust for relevant confounders, including traditional cardiovascular risk factors, socioeconomic indicators, and lipid-lowering agents. METHODS: This prospective cohort study will include 27,022 male participants aged 65-74, recruited from the two large Danish DANCAVAS and VIVA trials, both population-based randomized, multicentered, clinically controlled studies. We will assess long-term exposure to air pollutants using the state-of-the-art DEHM/UBM/AirGIS modelling system and noise pollution through the Nord2000 and SoundPLAN models, covering data from 1979 to 2019. This statistical analysis plan is strictly formulated to predefine the analytical approach for all outcomes and key study variables before data access. The primary analysis will utilize Cox proportional hazards models, adjusted for confounders identified in our cohort (age, body mass index, hypertension, diabetes, smoking status, family history of heart disease, socioeconomic factors, and lipid-lowering agents). This statistical analysis plan further includes Spearman rank correlation to explore inter-pollutant associations. CONCLUSION: The AIRCARD study addresses global concerns about the impact of air and noise pollution on cardiovascular disease. This research is important for understanding how the pollutants contribute to cardiovascular disease. We aim to provide insights into this area, emphasizing the need for public health measures to mitigate pollution exposure. Our goal is to provide policymakers and healthcare professionals with information on the role of environmental factors in cardiovascular health that could influence global strategies to reduce the cardiovascular disease burden associated with pollution. The design of this SAP ensures transparency and.

Electrocardiogram as a screening tool to exclude chronic systolic heart failure with reduced left ventricular ejection fraction.

INTRODUCTION: In recent years, the waiting time for outpatient echocardiography has been increasing. This has potential consequences for patients with de novo systolic heart failure (HF). Thus, screening methods for HF are needed. One method may be electrocardiogram (ECG). We assessed the diagnostic value of the ECG in identifying HF with reduced left ventricle ejection fraction (LVEF) in patients referred from primary care. METHODS: A 2020-2021 observational retrospective study was conducted on patients referred from primary care on suspicion of HF. All patients had ECG performed before LVEF was documented by echocardiography. RESULTS: In total, 248 patients (61.5%) presented with an abnormal ECG. Among these patients, 4.8% had LVEF 41-49% and 7.7% had LVEF ≤ 40%. An abnormal ECG was found to be associated with reduced LVEF. The negative predictive value of the ECG was 99%, regardless of whether the ECG was interpreted by the cardiologist or automatically. Adding the ECG to a logistic model with traditional risk factors, the ECG increased the area under curve from 0.72 to 0.79. CONCLUSION: This study is the first study to assess the value of automatic ECG interpretation compared with a cardiologist's interpretation. The normal ECG can safely exclude HF with LVEF less-than 50% and may serve as a gatekeeping tool to further assist the primary care physician in identifying patients with de novo systolic HF. FUNDING: None. TRIAL REGISTRATION: Not relevant.

Is There Any Improvement in Image Quality in Obese Patients When Using a New X-ray Tube and Deep Learning Image Reconstruction in Coronary Computed Tomography Angiography?

Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality. This study assessed the impact of using both higher tube currents as well as DLIR on the image quality and diagnostic accuracy. The study consisted of 51 symptomatic obese (BMI > 30 kg/m2) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent coronary computed tomography angiography (CCTA) twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube current. Images were reconstructed using ASiR-V 50% and DLIR. The image quality was evaluated by an observer using a Likert score and by ROI measurements in aorta and the myocardium. Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR resulting in an odds ratio of 1.23 (p = 0.017), and noise was reduced by 41%. A total of 88% of the image sets performed with Revolution Apex + DLIR were assessed as good enough for diagnosis compared to 69% of the image sets performed with Revolution Apex/CT + ASiR-V. In obese patients, the combination of higher tube current and DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.