Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

Background: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP.

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain.

Background: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).

Methods: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography.

What do we mean by complex percutaneous coronary intervention? An assessment of agreement amongst interventional cardiologists for defining complexity.

Background: In the last decade, percutaneous coronary intervention (PCI) has evolved toward the treatment of complex disease in patients with multiple comorbidities. Whilst there are several definitions of complexity, it is unclear whether there is agreement between cardiologists in classifying complexity of cases. Inconsistent identification of complex PCI can lead to significant variation in clinical decision-making.

TACE/ADAM17 substrates associate with ACS (Ep-CAM, HB-EGF) and follow-up MACE (TNFR1 and TNFR2).

Background And Aims: TACE/ADAM17 is a membrane bound metalloprotease, which cleaves substrates involved in immune and inflammatory responses and plays a role in coronary artery disease (CAD). We measured TACE and its substrates in CAD patients to identify potential biomarkers within this molecular pathway with potential for acute coronary syndrome (ACS) and major adverse cardiovascular events (MACE) prediction.

Methods: Blood samples were obtained from consecutive patients (n = 229) with coronary angiographic evidence of CAD admitted with ACS or electively.

Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.

Background: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data.

Objective: The aim of this study is to review the use of ML with ECG data using a time series approach.

Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation.

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement.

Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult.

Background: Treatment decisions in myocardial infarction (MI) are currently stratified by ST elevation (ST-elevation myocardial infarction [STEMI]) or lack of ST elevation (non-ST elevation myocardial infarction [NSTEMI]) on the electrocardiogram. This arose from the assumption that ST elevation indicated acute coronary artery occlusion (OMI). However, one-quarter of all NSTEMI cases are an OMI, and have a higher mortality.

The Interdependency and Co-Regulation of the Vitamin D and Cholesterol Metabolism.

Vitamin D and cholesterol metabolism overlap significantly in the pathways that contribute to their biosynthesis. However, our understanding of their independent and co-regulation is limited. Cardiovascular disease is the leading cause of death globally and atherosclerosis, the pathology associated with elevated cholesterol, is the leading cause of cardiovascular disease.

Targeting the NLRP3 Inflammasome in Glaucoma.

Glaucoma is a group of optic neuropathies characterised by the degeneration of retinal ganglion cells, resulting in damage to the optic nerve head (ONH) and loss of vision in one or both eyes. Increased intraocular pressure (IOP) is one of the major aetiological risk factors in glaucoma, and is currently the only modifiable risk factor. However, 30-40% of glaucoma patients do not present with elevated IOP and still proceed to lose vision.

Comparing Single-Page, Multipage, and Conversational Digital Forms in Health Care: Usability Study.

Background: Even in the era of digital technology, several hospitals still rely on paper-based forms for data entry for patient admission, triage, drug prescriptions, and procedures. Paper-based forms can be quick and convenient to complete but often at the expense of data quality, completeness, sustainability, and automated data analytics. Digital forms can improve data quality by assisting the user when deciding on the appropriate response to certain data inputs (eg, classifying symptoms).

The role of senescence in the pathogenesis of atrial fibrillation: A target process for health improvement and drug development.

Cellular senescence is a state of growth arrest that occurs after cells encounter various stresses. Senescence contributes to tumour suppression, embryonic development, and wound healing. It impacts on the pathology of various diseases by secreting inflammatory chemokines, immune modulators and other bioactive factors.

Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation.

Background: A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes.

Objective: The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement.

Human-Computer Agreement of Electrocardiogram Interpretation for Patients Referred to and Declined for Primary Percutaneous Coronary Intervention: Retrospective Data Analysis Study.

Background: When a patient is suspected of having an acute myocardial infarction, they are accepted or declined for primary percutaneous coronary intervention partly based on clinical assessment of their 12-lead electrocardiogram (ECG) and ST-elevation myocardial infarction criteria.

Objective: We retrospectively determined the agreement rate between human (specialists called activator nurses) and computer interpretations of ECGs of patients who were declined for primary percutaneous coronary intervention.

Methods: Various features of patients who were referred for primary percutaneous coronary intervention were analyzed.

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis.

Introduction: Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange.

Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification.

Objectives: Timely prehospital diagnosis and treatment of acute coronary syndrome (ACS) are required to achieve optimal outcomes. Clinical decision support systems (CDSS) are platforms designed to integrate multiple data and can aid with management decisions in the prehospital environment. The review aim was to describe the accuracy of CDSS and individual components in the prehospital ACS management.

Computational time series analysis of patient referrals to a primary percutaneous coronary intervention service.

This article retrospectively analyses a primary percutaneous coronary intervention dataset comprising patient referrals that were accepted for percutaneous coronary intervention and those who were turned down between January 2015 and December 2018 at Altnagelvin Hospital (United Kingdom). Time series analysis of these referrals was undertaken for analysing the referral rates per year, month, day and per hour. The overall referrals have 70 per cent (n = 1466, p < 0.

An exploratory analysis investigating blood protein biomarkers to augment ECG diagnosis of ACS.

Background: Acute Coronary Syndrome (ACS) is currently diagnosed using a 12‑lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12‑lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients.

Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12‑lead electrocardiogram.

Background: Electrocardiogram (ECG) lead misplacement can adversely affect ECG diagnosis and subsequent clinical decisions. V1 and V2 are commonly placed superior of their correct position. The aim of the current study was to use machine learning approaches to detect V1 and V2 lead misplacement to enhance ECG data quality.

Exploring the Involvement of NLRP3 and IL-1 in Osteoarthritis of the Hand: Results from a Pilot Study.

Hand osteoarthritis (HOA) includes different subsets; a particular and uncommon form is erosive HOA (EHOA). Interleukin- (IL-) 1 plays a crucial role in the pathogenesis of osteoarthritis (OA); it is synthesized as an inactive precursor which requires the intervention of a cytosolic multiprotein complex, named inflammasome, for its activation. The aim of this study was to investigate the involvement of IL-1 and the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome in patients with EHOA and nonerosive HOA (NEHOA) compared to healthy controls.

New models of atherosclerosis and multi-drug therapeutic interventions.

Motivation: Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible.

A novel role for CRIM1 in the corneal response to UV and pterygium development.

Pterygium is a pathological proliferative condition of the ocular surface, characterised by formation of a highly vascularised, fibrous tissue arising from the limbus that invades the central cornea leading to visual disturbance and, if untreated, blindness. Whilst chronic ultraviolet (UV) light exposure plays a major role in its pathogenesis, higher susceptibility to pterygium is observed in some families, suggesting a genetic component. In this study, a Northern Irish family affected by pterygium but reporting little direct exposure to UV was identified carrying a missense variant in CRIM1 NM_016441.

Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms.

Introduction: Interpretation of the 12‑lead Electrocardiogram (ECG) is normally assisted with an automated diagnosis (AD), which can facilitate an 'automation bias' where interpreters can be anchored. In this paper, we studied, 1) the effect of an incorrect AD on interpretation accuracy and interpreter confidence (a proxy for uncertainty), and 2) whether confidence and other interpreter features can predict interpretation accuracy using machine learning.

Methods: This study analysed 9000 ECG interpretations from cardiology and non-cardiology fellows (CFs and non-CFs).