The association between adverse childhood experiences and adult cardiac function in the UK Biobank.

Aims: The importance of early life factors in determining health in later adulthood is increasingly recognized. This study evaluated the association of adverse childhood experiences (ACEs) with cardiovascular magnetic resonance (CMR) phenotypes.

Methods And Results: UK Biobank participants who had completed CMR and the self-reported questionnaire on traumatic childhood experiences were included.

Predicting remission following CBT for childhood anxiety disorders: a machine learning approach.

Background: The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

Methods: A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5-18 years).

Factors associated with electroconvulsive therapy treatment for adults with serious psychiatric conditions in Australia.

Objective: To identify factors associated with receiving electroconvulsive therapy (ECT) for serious psychiatric conditions.

Methods: Retrospective observational study using hospital administrative data linked with death registrations and outpatient mental health data in New South Wales (NSW), Australia. The cohort included patients admitted with a primary psychiatric diagnosis between 2013 and 2022.

Novel Biomarkers for the diagnosis of diabetic nephropathy.

Diabetes mellitus and its complications are a known public health problem nowadays. Diabetic nephropathy is one of the main complications and the result of multiple mechanisms, including: activation of the renin-angiotensin-aldosterone system, formation of advanced glycation end products and chronic inflammation that led to glomerular and tubulo-interstitial damage producing mesangial expansion and glomerulosclerosis, which finally results in chronic kidney disease. Early detection of diabetic nephropathy is essential for adequate intervention to stop, or at least slow down its progression.

Use of digital patient decision-support tools for atrial fibrillation treatments: a systematic review and meta-analysis.

Objectives: To assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.

Study Design: Systematic review and meta-analysis.

Eligibility Criteria: Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.

Predicting Adverse Outcomes Following Catheter Ablation Treatment for Atrial Flutter/Fibrillation.

Background & Aim: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF) and/or atrial flutter (AFL).

Methods: We used a linked dataset including hospital administrative data, prescription medicine claims, emergency department presentations, and death registrations of patients in New South Wales, Australia. The cohort included patients who received catheter ablation for AF and/or AFL.

Association between altered tryptophan metabolism, plasma aryl hydrocarbon receptor agonists, and inflammatory Chagas disease.

Introduction: Chagas disease causes a cardiac illness characterized by immunoinflammatory reactions leading to myocardial fibrosis and remodeling. The development of Chronic Chagas Cardiomyopathy (CCC) in some patients while others remain asymptomatic is not fully understood, but dysregulated inflammatory responses are implicated. The Aryl hydrocarbon receptor (AhR) plays a crucial role in regulating inflammation.

Neuroticism personality traits are linked to adverse cardiovascular phenotypes in the UK Biobank.

Aims: To evaluate the relationship between neuroticism personality traits and cardiovascular magnetic resonance (CMR) measures of cardiac morphology and function, considering potential differential associations in men and women.

Methods And Results: The analysis includes 36 309 UK Biobank participants (average age = 63.9 ± 7.

Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.

Background: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication.

The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: A cross-sectional survey.

Objectives: To examine i) the use of mobile apps and fitness trackers in adults during the COVID-19 pandemic to support health behaviors; ii) the use of COVID-19 apps; iii) associations between using mobile apps and fitness trackers, and health behaviors; iv) differences in usage amongst population subgroups.

Methods: An online cross-sectional survey was conducted during June-September 2020. The survey was developed and reviewed independently by co-authors to establish face validity.

A personalized mobile app for physical activity: An experimental mixed-methods study.

Objectives: To investigate the feasibility of the be.well app and its personalization approach which regularly considers users' preferences, amongst university students.

Methods: We conducted a mixed-methods, pre-post experiment, where participants used the app for 2 months.

Assessing the effectiveness of empirical calibration under different bias scenarios.

Background: Estimations of causal effects from observational data are subject to various sources of bias. One method for adjusting for the residual biases in the estimation of treatment effects is through the use of negative control outcomes, which are outcomes not believed to be affected by the treatment of interest. The empirical calibration procedure is a technique that uses negative control outcomes to calibrate p-values.

An Observational Study of Clinical and Health System Factors Associated With Catheter Ablation and Early Ablation Treatment for Atrial Fibrillation in Australia.

Objective: To investigate clinical and health system factors associated with receiving catheter ablation (CA) and earlier ablation for non-valvular atrial fibrillation (AF).

Methods: We used hospital administrative data linked with death registrations in New South Wales, Australia for patients with a primary diagnosis of AF between 2009 and 2017. Outcome measures included receipt of CA versus not receiving CA during follow-up (using Cox regression) and receipt of early ablation (using logistic regression).

Extract, transform, load framework for the conversion of health databases to OMOP.

Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to convert datasets to OMOP, the conversion is time and resource-intensive, leaving the research community in need of tools for mapping data to OMOP.

Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression.

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e.

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study.

Background: COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated.

Objective: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data.

Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression.

Objective: To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.

Design: Systematic review and meta-analysis.

Data Sources: PubMed and seven additional databases, from 2007 to 2020.

A Mobile Social Networking App for Weight Management and Physical Activity Promotion: Results From an Experimental Mixed Methods Study.

Background: Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users.

Objective: This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features.

Empirical analysis of Zipf's law, power law, and lognormal distributions in medical discharge reports.

Background: Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions.

Objective: This paper empirically analyses whether text in medical discharge reports follow Zipf's law, a commonly assumed statistical property of language where word frequency follows a discrete power-law distribution.

Method: We examined 20,000 medical discharge reports from the MIMIC-III dataset.