Al-Abcha, Abdullah

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

ABSTRACTPlatelets are small, anucleate particles derived from bone marrow megakaryocytes that circulate in the blood and are central to hemostasis, atherothrombosis, and post‐angioplasty coronary restenosis. Initially thought to involve a simple process of platelet adhesion, activation, and aggregation, recent insights reveal an important interplay with genetics and other organ systems, especially in the setting of antiplatelet drug therapy. Interaction involving the hepatic cytochrome P450 enzymes, which regulate drug metabolism, and genetic variation in those enzymes affecting drug response, underscores the liver's role in modulating platelet function by attenuating circulating active drug metabolites with eventual cardiovascular implications. Circulating adhesive proteins such as von Willebrand Factor and fibronectin also significantly regulate platelet aggregation, a process that is further influenced by varying shear conditions within the vascular system. The efficacy of antiplatelet therapies can be attenuated by such individual genetic variation, which can affect multiple cellular pathways involved not only in drug metabolism and clearance but also the drug target itself. Identifying these genetic differences allows for tailored therapy, enhancing treatment outcomes and minimizing adverse effects. Genetic variation, particularly in the CYP2C19 gene, influences individual responses to the most commonly used antiplatelet drug other than aspirin, clopidogrel, with certain variants leading to reduced drug metabolism and antiplatelet effects. Therefore, genotype‐guided antiplatelet therapy has emerged as a promising approach to reduce ischemic events and minimize bleeding events. This personalized strategy requires an understanding of genetic variation, drug‐gene interaction, and the liver's role in impacting platelet function, the coagulation system, and cardiovascular systems.

Purpose of review Advances in pharmacogenomics have paved the way for personalized medicine. The purpose of this review is to summarize the background, rationale, and evidence for pharmacogenomics in cardiovascular medicine. Recent findings Randomized clinical trials have supported the role of a genotype-guided approach for antiplatelet therapy in patients with coronary artery disease undergoing percutaneous coronary interventions. Additionally, there is increasing evidence supporting the association of certain genetic variants and risk of statin associated muscle symptoms. Furthermore, germline genetic variation is being used as a biomarker to target patients with specific therapy. Summary Pharmacogenomics has the potential to improve patient care by providing the right drug to the right patient and could guide the identification of novel drug therapies for cardiovascular disease.

Heart failure (HF) is a chronic, debilitating condition associated with significant morbidity, mortality, and socioeconomic burden. Patients with end-stage HF (ESHF) who are not a candidate for advanced therapies will continue to progress despite standard medical therapy. Thus, the focus of care shifts from prolonging life to controlling symptoms and improving quality of life through palliative care ( PC). Because the condition and prognosis of HF patients evolve and can rapidly deteriorate, it is imperative to begin the discussion on end-of-life (EOL) issues early during HF management. These include the completion of an advance directive, do-not-resuscitate orders, and policies on device therapy and discontinuation as part of advance care planning (ACP). ESHF patients who do not have indications for advanced therapies or those who wish not to have a left ventricular assist device (LVAD) or heart transplant (HT) often experience high symptom burden despite adequate medical management. The proper identification and assessment of symptoms such as pain, dyspnea, nausea, depression, and anxiety are essential to the management of ESHF and may be underdiagnosed and undertreated. Psychological support and spiritual care are also crucial to improving the quality of life during EOL. Caregivers of ESHF patients must also be provided supportive care to prevent compassion fatigue and improve resilience in patient care. In this narrative review, we compare the international guidelines and provide an overview of end-of-life and palliative care for patients with ESHF.