Prediction of incident chronic kidney disease in community-based electronic health records: a systematic review and meta-analysis.

Background: Chronic kidney disease (CKD) is a major global health problem and its early identification would allow timely intervention to reduce complications. We performed a systematic review and meta-analysis of multivariable prediction models derived and/or validated in community-based electronic health records (EHRs) for the prediction of incident CKD in the community.

Methods: Ovid Medline and Ovid Embase were searched for records from 1947 to 31 January 2024.

Inequalities in care delivery and outcomes for myocardial infarction, heart failure, atrial fibrillation, and aortic stenosis in the United Kingdom.

Cardiovascular diseases are a leading cause of death and disability globally, with inequalities in burden and care delivery evident in Europe. To address this challenge, The Lancet Regional Health-Europe convened experts from a range of countries to summarise the current state of knowledge on cardiovascular disease inequalities across Europe. This Series paper presents evidence from nationwide secondary care registries and primary care healthcare records regarding inequalities in care delivery and outcomes for myocardial infarction, heart failure, atrial fibrillation, and aortic stenosis in the National Health Service (NHS) across the United Kingdom (UK) by age, sex, ethnicity and geographical location.

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

Introduction: Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening.

Incident cardiovascular, renal, metabolic diseases and death in individuals identified for risk-guided atrial fibrillation screening: a nationwide cohort study.

Objective: Risk-guided atrial fibrillation (AF) screening may be an opportunity to prevent adverse events in addition to stroke. We compared events rates for new diagnoses of cardio-renal-metabolic diseases and death in individuals identified at higher versus lower-predicted AF risk.

Methods: From the UK Clinical Practice Research Datalink-GOLD dataset, 2 January 1998-30 November 2018, we identified individuals aged ≥30 years without known AF.

Prediction models for heart failure in the community: A systematic review and meta-analysis.

Aims: Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models.

Methods And Results: From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts.

Ectopically Expressed Meiosis-Specific Cancer Testis Antigen HORMAD1 Promotes Genomic Instability in Squamous Cell Carcinomas.

Genomic instability is a prominent hallmark of cancer, however the mechanisms that drive and sustain this process remain elusive. Research demonstrates that numerous cancers with increased levels of genomic instability ectopically express meiosis-specific genes and undergo meiomitosis, the clash of mitotic and meiotic processes. These meiotic genes may represent novel therapeutic targets for the treatment of cancer.

Prediction of short-term atrial fibrillation risk using primary care electronic health records.

Objective: Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)).

Methods: We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets.

Tools used to assay genomic instability in cancers and cancer meiomitosis.

Genomic instability is a defining characteristic of cancer and the analysis of DNA damage at the chromosome level is a crucial part of the study of carcinogenesis and genotoxicity. Chromosomal instability (CIN), the most common level of genomic instability in cancers, is defined as the rate of loss or gain of chromosomes through successive divisions. As such, DNA in cancer cells is highly unstable.