Publications

Abstract Background Cardiac autonomic neuropathy (CAN) is an important yet often overlooked complication of diabetes, which significantly increases the risk of cardiovascular (CV) events and mortality. Traditional diagnostic methods like CV autonomic function tests (CARTs) are laborious and rarely evaluated in clinical practice. This study aimed to develop and employ machine learning (ML) algorithms to analyze electrocardiogram (ECG) for the diagnosis of CAN. Methods We utilized motif and discord extraction techniques alongside Long Short-Term Memory (LSTM) networks to analyze 12-lead, 10 seconds ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the Support Vector Machine (SVM) classification model was evaluated using Ten-Cross Validation (TCV) with the following metrics accuracy, precision, recall, F1 score, and area under the ROC Curve (AUC). Results Among 205 patients (mean age 54 ± 17; 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95%CI 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded best results with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95%CI 0.54-0.81). Conclusion Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where CV risk factor modification may be initiated.

Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy.

In critically ill AF patients admitted to a SDU, adverse outcomes are highly prevalent. OAC use is associated to an increased risk of therapeutic failure, clinical scores seem unhelpful in predicting stroke and MH, suggesting a highly individualized approach in AF management in this setting.

Development and implementation of AF-specific training for PCU HCPs are needed in Brazil, along with evidence-based protocols and guidelines, educational programmes for patients, better access to INR tests for patients taking warfarin and availability of NOACs.

Atrial fibrillation (AF) is the commonest cardiac rhythm abnormality and has a significant disease burden. Amongst its devastating complications is stroke, the risk of which increases with age. The stroke risk in an older person with AF is therefore tremendous, and oral-anticoagulation (OAC) therapy is central to minimizing this risk. The presence of age-associated factors such as frailty and multi-morbidities add complexity to OAC prescription decisions in older patients and often, OAC is needlessly withheld from them despite a lack of evidence to support this practice. Generally, this is driven by an over-estimation of the bleeding risk. This review article provides an overview of the concepts and controversies in managing AF in older people, with respect to the existing evidence and current practice. A literature search was conducted on Pubmed and Cochrane using keywords, and relevant articles published by the 1st of May 2019 were included. The article will shed light on common misconceptions that appear to serve as rationale for precluding OAC and focus on clinical considerations that may aid OAC prescription decisions where appropriate, to optimize AF management using an integrated, multi-disciplinary care approach. This is crucial for all patients, particularly older individuals who are most vulnerable to the deleterious consequences of this condition.

Remote PPG monitoring is a viable method of assessing for arrhythmia recurrence post-DCCV. This approach may save patients significant travel time, reduce environmental CO₂ emission, and be cost saving in a publicly-funded healthcare system.

Background: In the ENSURE-AF study (NCT 02072434), comparable low rates of major and clinically relevant nonmajor (CRNM) bleeding and thromboembolism were observed between patients undergoing elect...

Among patients with AF, obesity and higher BMI were associated with a more favorable prognosis in the Gulf SAFE registry. The underlying mechanisms for this obesity "paradox" merit further exploration.

In this large-scale real-world study, SGLT2i use was associated with a lower incidence of gout in patients with T2D compared to both insulin and metformin controls. These findings suggest the potential of SGLT2i as a promising therapeutic option for treating gout in this population.

Atrial fibrillation (AF) has heterogeneous patterns of presentation concerning symptoms, duration of episodes, AF burden, and the tendency to progress towards the terminal step of permanent AF. AF is associated with a risk of stroke/thromboembolism traditionally considered dependent on patient-level risk factors rather than AF type, AF burden, or other characterizations. However, the time spent in AF appears related to an incremental risk of stroke, as suggested by the higher risk of stroke in patients with clinical AF vs. subclinical episodes and in patients with non-paroxysmal AF vs. paroxysmal AF. In patients with device-detected atrial tachyarrhythmias, AF burden is a dynamic process with potential transitions from a lower to a higher maximum daily arrhythmia burden, thus justifying monitoring its temporal evolution. In clinical terms, the appearance of the first episode of AF, the characterization of the arrhythmia in a specific AF type, the progression of AF, and the response to rhythm control therapies, as well as the clinical outcomes, are all conditioned by underlying heart disease, risk factors, and comorbidities. Improved understanding is needed on how to monitor and modulate the effect of factors that condition AF susceptibility and modulate AF-associated outcomes. The increasing use of wearables and apps in practice and clinical research may be useful to predict and quantify AF burden and assess AF susceptibility at the individual patient level. This may help us reveal why AF stops and starts again, or why AF episodes, or burden, cluster. Additionally, whether the distribution of burden is associated with variations in the propensity to thrombosis or other clinical adverse events. Combining the improved methods for data analysis, clinical and translational science could be the basis for the early identification of the subset of patients at risk of progressing to a longer duration/higher burden of AF and the associated adverse outcomes.