Ayoub, Chadi

Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.

Lipoprotein(a) [Lp(a)] is a lipid molecule with atherogenic, inflammatory, thrombotic, and antifibrinolytic effects, whose concentrations are predominantly genetically determined. The association between Lp(a) and cardiovascular diseases (CVDs) has been well-established in numerous studies, and the ability to measure Lp(a) levels is widely available in the community. As such, there has been increasing interest in Lp(a) as a therapeutic target for the prevention of CVD. The impact of the currently available lipid-modifying agents on Lp(a) is modest and heterogeneous, except for the monoclonal antibody proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i), which demonstrated a significant reduction in Lp(a) levels. However, the absolute reduction in Lp(a) to significantly decrease CVD outcomes has not been definitely established, and the magnitude of the effect of PCSK9i seems insufficient to directly reduce the Lp(a)-related CVD risk. Therefore, emerging therapies are being developed that specifically aim to lower Lp(a) levels and the risk of CVD, including RNA interference (RNAi) agents, which have the capacity for temporary and reversible downregulation of gene expression. This review article aims to summarize the effects of Lp(a) on CVD and to evaluate the available evidence on established and emerging therapies targeting Lp(a) levels, focusing on the potential reduction of CVD risk attributable to Lp(a) concentrations.

Introduction Many patients experience unrelieved neuropathic cancer-related pain. Most current analgesic therapies have psychoactive side effects, lack efficacy data for this indication and have potential medication-related harms. The local anaesthetic lidocaine (lignocaine) has the potential to help manage neuropathic cancer-related pain when administered as an extended, continuous subcutaneous infusion. Data support lidocaine as a promising, safe agent in this setting, warranting further evaluation in robust, randomised controlled trials. This protocol describes the design of a pilot study to evaluate this intervention and explains the pharmacokinetic, efficacy and adverse effects evidence informing the design. Methods and analysis A mixed-methods pilot study will determine the feasibility of an international first, definitive phase III trial to evaluate the efficacy and safety of an extended continuous subcutaneous infusion of lidocaine for neuropathic cancer-related pain. This study will comprise: a phase II double-blind randomised controlled parallel-group pilot of subcutaneous infusion of lidocaine hydrochloride 10% w/v (3000 mg/30 mL) or placebo (sodium chloride 0.9%) over 72 hours for neuropathic cancer-related pain, a pharmacokinetic substudy and a qualitative substudy of patients’ and carers’ experiences. The pilot study will provide important safety data and help inform the methodology of a definitive trial, including testing proposed recruitment strategy, randomisation, outcome measures and patients’ acceptability of the methodology, as well as providing a signal of whether this area should be further investigated. Ethics and dissemination Participant safety is paramount and standardised assessments for adverse effects are built into the trial protocol. Findings will be published in a peer-reviewed journal and presented at conferences. This study will be considered suitable to progress to a phase III study if there is a completion rate where the CI includes 80% and excludes 60%. The protocol and Patient Information and Consent Form have been approved by Sydney Local Health District (Concord) Human Research Ethics Committee 2019/ETH07984 and University of Technology Sydney ETH17-1820. Trial registration number ANZCTR ACTRN12617000747325.

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.