A deep learning phenome wide association study of the electrocardiogram.

Aims: Deep learning methods have shown impressive performance in detecting a range of diseases from electrocardiogram (ECG) waveforms, but the breadth of diseases that can be detected with high accuracy remains unknown, and in many cases the changes to the ECG allowing these classifications are also opaque. In this study, we aim to determine the full set of cardiac and non-cardiac conditions detectable from the ECG and to understand which ECG features contribute to the disease classification.

Methods And Results: Using large datasets of ECGs and connected electronic health records from two separate medical centres, we independently trained PheWASNet, a multi-task deep learning model, to detect 1243 different disease phenotypes from the raw ECG waveform.

Trends in drug-drug interaction prevalence and longitudinal associations with health outcomes in the older community-dwelling population: Findings from The Irish LongituDinal study on Ageing.

Aims: We estimate trends in severe drug-drug interaction (DDI) prevalence and examine longitudinal associations between DDI exposure and health outcomes (emergency department [ED] visits, quality-of-life [QoL] and functional decline) over approximately 10-years in the older community-dwelling population in Ireland.

Methods: This is a cohort study using linked national-pharmacy-claims data and health outcomes data from waves 1-5 (waves every 2-years; October 2009-January 2019) of The Irish LongituDinal study on Ageing. Community-dwellers aged ≥65 years dispensed ≥2 drugs per claim were eligible for inclusion.

Detecting structural heart disease from electrocardiograms using AI.

Early detection of structural heart disease is critical to improving outcomes, but widespread screening remains limited by the cost and accessibility of imaging tools such as echocardiography. Recent advances in machine learning applied to heart rhythm recordings have shown promise in identifying disease, although previous work has been limited by development in narrow populations or targeting only select heart conditions. Here we introduce a deep learning model, EchoNext, trained on more than 1 million heart rhythm and imaging records across a large and diverse health system to detect many forms of structural heart disease.

Fast Bayesian Inference for Spatial Mean-Parameterized Conway-Maxwell-Poisson Models.

Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are common features in count data. There are currently two classes of models that allow for these features-the mode-parameterized Conway-Maxwell-Poisson (COMP) distribution and the generalized Poisson model.

Deep learning on electrocardiogram waveforms to stratify risk of obstructive stable coronary artery disease.

Aims: Coronary artery disease (CAD) incidence continues to rise with an increasing burden of chronic coronary disease (CCD). Current probability-based risk assessment for obstructive CAD (oCAD) lacks sufficient diagnostic accuracy. We aimed to develop and validate a deep learning (DL) algorithm utilizing electrocardiogram (ECG) waveforms and clinical features to predict oCAD in patients with suspected CCD.

The risk and cost of drug-drug interactions in an older population acutely admitted to hospital in Ireland.

Background: Adverse drug reactions (ADRs) are associated with greater healthcare costs; drug-drug interactions (DDIs) are a common cause of ADRs.

Aim: To estimate the risk and cost of ADR-related hospital admission associated with DDI-exposure versus no DDI-exposure in an older community-dwelling population.

Method: This is a secondary analysis of a cohort study among 798 older individuals admitted acutely to hospital in Ireland (2016-2017).

Bayesian Gower agreement for categorical data.

In this work I present two methods for measuring agreement in nominal and ordinal data. The measures, which employ Gower-type distances, are simple, intuitive, and easy to compute for any number of units and any number of coders. Influential units and/or coders are easily identified.

Can Wearable Device Promote Physical Activity and Reduce Pain in People with Chronic Musculoskeletal Conditions?

: The purpose of this systematic review is to identify and appraise the evidence on the effectiveness of using wearable devices to promote physical activity and reduce pain in people with chronic musculoskeletal pain. : Systematic searches of electronic databases PubMed, CINAHL, and Medline (Ovid) were undertaken for randomised control trials and observational studies of wearable-based interventions in patients with chronic musculoskeletal conditions. : Thirteen studies were included in this review.

Complications of Ambulatory Procedures: Prevalence and Hospital Outpatient Department Variation.

Ambulatory procedures performed electively in hospital outpatient departments are of increasing complexity and constitute a growing share of total procedure volume. Despite their importance, little is known of the prevalence of complications from routine procedures once patients are discharged. This study utilizes a 100% Medicare Fee-for-Service claims data file for the years 2019-2022 to assess the relative frequency of hospital-based ambulatory procedures and 30-day patient postprocedure emergency room and hospitalization complication rates utilizing the Ambulatory Potentially Preventable Complication (AM-PPC) classification method.

Enhanced spatial analysis assessing the association between PFAS-contaminated water and cancer incidence: rationale, study design, and methods.

Background: Cancer is a complex set of diseases, and many have decades-long lag times between possible exposure and diagnosis. Environmental exposures, such as per- and poly-fluoroalkyl substances (PFAS) and area-level risk factors (e.g.

Evaluation of Drug-Drug Interactions in Pharmacoepidemiologic Research.

Drug-drug interactions (DDIs) represent a significant concern for clinical care and public health, but the health consequences of many DDIs remain largely underexplored. This knowledge gap underscores the critical need for pharmacoepidemiologic research to evaluate real-world health outcomes of DDIs. In this review, we summarize the definitions commonly used in pharmacoepidemiologic DDI studies, discuss common sources of bias, and illustrate through examples how these biases can be mitigated.

The European Drug-Drug Interaction (EuroDDI) Study Protocol: A Cross-Country Comparison of Drug-Drug Interaction Prevalence in the Older Community-Dwelling Population.

Background: Drug-drug interactions (DDIs), highly prevalent amongst the elderly, can lead to avoidable medication-related harm. Cardiovascular and central nervous system (CNS) drugs are commonly implicated. To date, there is no consensus on how to measure DDIs, making comparisons across countries challenging.

Reliable and streamlined model setup for digital twin assessment of fracture healing.

In large animal models of bone fracture repair, postmortem torsional testing is commonly used to assess healing biomechanics. Bending and axial tests are physiologically relevant, but much less commonly performed. Virtual torsional testing using image-based finite element models has been validated to postmortem bench tests, but its predictive value for capturing whole-bone mechanics and fracture healing quality under other physiologically relevant loading modes has not yet been established.

Drug-Drug Interactions and Their Association with Adverse Health Outcomes in the Older Community-Dwelling Population: A Prospective Cohort Study.

Background: Evidence on associations between drug-drug interactions (DDIs) and health outcomes in the older community-dwelling population is limited.

Objective: We estimate potentially clinically important DDI prevalence and examine the association between DDIs and (1) adverse drug events (ADEs), (2) emergency hospital attendance and (3) health-related quality of life (HRQoL) in an older community-dwelling population in Ireland.

Methods: This is a prospective cohort study of community-dwelling older adults (N = 904) aged ≥ 70 years from 15 general practices in Ireland recruited in 2010 (wave-1) and followed-up over 2 years (wave-2; 2012-2013), with linked national pharmacy claims data.

Autogenic training for chronic health conditions: A service evaluation.

Background: Autogenic training (AT) is a structured meditative-style practice, consisting of a sequence of simple mental exercises intended to induce a relaxed state in patients. There is some emerging evidence to suggest that AT can be effective in treating certain chronic conditions, however, further evidence is required. A service evaluation of AT services at the Royal London Hospital for Integrated Medicine was conducted to evaluate the impact of AT on patients with chronic conditions.

Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes.

Objective: Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD).

Methods: Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS).

Individualised prediction of resilience and vulnerability to sleep loss using EEG features.

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep.

Contexts and mechanisms relevant to General Practitioner (GP) based interventions to reduce adverse drug events (ADE) in community dwelling older adults: a rapid realist review.

Background: Older adults in Ireland are at increased risk of adverse drug events (ADE) due, in part, to increasing rates of polypharmacy. Interventions to reduce ADE in community dwelling older adults (CDOA) have had limited success, therefore, new approaches are required.A realist review uses a different lens to examine why and how interventions were supposed to work rather than if, they worked.

A novel abuse liability assessment of e-cigarettes in young adults ii: Reinforcement enhancement and follow-up assessment.

A double-blind study was performed to test the abuse liability of electronic nicotine delivery systems (ENDS) in young adults; in particular, the influence of nicotine on reward sensitivity was assessed. A total of 53 healthy nonusers participated in experimental sessions during which they played a video game made available on a progressive ratio schedule of reinforcement and self-administered nicotine via ENDS. Participants were randomized into one of three groups.

Drug-drug interactions and the risk of adverse drug reaction-related hospital admissions in the older population.

Aims: The aims of this study were to estimate potentially clinically important drug-drug interaction (DDI) prevalence, and the average causal effect of DDI exposure on adverse drug reaction (ADR)-related hospital admission, and to examine differences in health-related quality of life (HRQoL) and length of stay (LOS) per DDI exposure in an older (≥65 years) population acutely hospitalized.

Methods: This was a cross-sectional study conducted among 798 older individuals acutely admitted to hospital in Ireland between 2016 and 2017. Medication (current/recently discontinued/over-the-counter) and clinical data (e.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss.

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep.

The Benefits and Harms of Pharmacological Treatment for Postradiation Pelvic Pain: A Systematic Review by the European Association of Urology Chronic Pelvic Pain Panel with Recommendations for Clinical Practice.

Context: Radiotherapy of the pelvis is a widely used method for the treatment of malignancies, and local complications including pain following pelvic radiation therapy are acknowledged complications.

Objective: The primary objective is to assess the clinical effectiveness and safety of pharmacological therapies on postradiation pelvic pain.

Evidence Acquisition: A systematic review of the use of different pharmacological treatments in the management of post-radiation pelvic pain was conducted (PROSPERO-ID: CRD42021249026).