Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD). We developed hART (heart failure Attentive Risk Trajectory), a deep-learning model to predict HF trajectories in CHD patients. hART is designed to capture the contextual relationships between medical events within a patient's history. It is trained to predict future HF risk by using the masked self-attention mechanism that forces it to focus only on the most relevant segments of the past medical events. To demonstrate the utility of hART, we used a large cohort containing healthcare administrative data from the Quebec CHD database (137,493 patients, 35-year follow-up). hART achieves an area under the precision-recall of 28% for HF risk prediction, which is 33% improvement over existing methods. Patients with severe CHD lesion showed a consistently elevated predicted HF risks throughout their lifespan, and patients with genetic syndromes exhibited elevated HF risks until the age of 50. The impact of the birth condition decreases on long-term HF risk. The timing of interventions such as arrhythmia surgery had varying impacts on the lifespan HF risk among the individuals. Arrhythmic surgery performed at a younger age had minimal long-term effects on HF risk, while surgeries during adulthood had a significant lasting impact. Together, we show that hART can detect meaningful lifelong HF risk in CHD patients by capturing both long and short-range dependencies in their past medical events.

Diabetic retinopathy (DR) remains the leading cause of preventable blindness among working-age adults worldwide, driven by the growing prevalence of diabetes mellitus. The aim of this comprehensive literature review is to provide an insightful analysis of recent advances in the pathogenesis of DR, followed by a summary of emerging technologies for its diagnosis and treatment. Recent studies have explored the roles of cell death pathways, immune activation, and lipid peroxidation in the pathology of DR. However, at the core of DR pathology lies neovascularization driven by vascular endothelial growth factor (VEGF), and mitochondrial damage due to dysregulated oxidative stress. These dysregulated pathways manifest clinically as DR, with specific subtypes including non-proliferative DR, proliferative DR and diabetic macular edema, which can be diagnosed through various imaging modalities. Recently, novel advances have been made using liquid biopsy and artificial (AI)-based algorithms with the goal of transforming DR diagnostics. AI models show distinct promise with the capacity to provide automated interpretation of retinal imaging. Furthermore, conventional anti-VEGF injectable agents have revolutionized DR treatment in the past decades. Today, as the pathogenesis of DR becomes better understood, new pathways, such as the ROS-VEGF loop, are being elucidated in greater depth, enabling the development of targeted therapies. In addition, new innovations such as intravitreal implants are transforming the delivery of DR-specific medication. This paper will discuss the current understanding of the pathogenesis of DR, which is leading to new diagnostic and therapeutic tools that will transform clinical management of DR.

Background: Benefiting from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated risks of cardiac and noncardiac complications. Understanding how comorbidity evolves across the lifespan is the cornerstone to improving long‐term outcomes. We hypothesized that comorbidity occurs in clusters that change over time in patients with CHD. Methods: Using the Quebec CHD database with 35 years of follow‐up (1983–2017), we designed a longitudinal cohort study evaluating 38 International Classification of Diseases, Ninth Revision/Tenth Revision ( ICD‐9/10 ) coded comorbidities. Observed sequences of comorbidities were mapped using median ages of onset. Associations between disease pairs were quantified using hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and time‐varying predictor diseases. Results: The cohort comprised 9764 individuals with severe CHD and 127 728 with nonsevere CHD. Patients with severe CHD demonstrated higher cumulative probabilities of developing comorbidities than those with nonsevere CHD from birth to older adulthood. By age 40 years, cumulative probabilities for developing cardiac, vascular, endocrine/metabolic, digestive, infectious, renal, and neurological disease were ≥2‐fold higher in severe versus nonsevere CHD. Median ages of onset for most comorbidities occurred below age 40 in severe CHD, 2 to 3 decades earlier than patients with nonsevere CHD where peak comorbidity onsets occurred between their 60s and 80s. Disease progression in severe CHD began with childhood cardiovascular diseases, progressing to early‐adulthood metabolic, hepatic, renal diseases, and later heart failure and dementia. Conclusions: Distinct multimorbidity clusters were observed by CHD severity, with earlier clustering in severe CHD, highlighting the need for early risk characterization and preventive strategies.