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. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST) and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Results: Seven studies met inclusion criteria, describing 12 prediction models, with two eligible for meta-analysis including 2 173 202 patients. The Chronic Kidney Disease Prognosis Consortium (CKD-PC) (summary c-statistic 0.847; 95% CI 0.827-0.867; 95% PI 0.780-0.905) and SCreening for Occult REnal Disease (SCORED) (summary c-statistic 0.811; 95% CI 0.691-0.926; 95% PI 0.514-0.992) models had good model discrimination performance. Risk of bias was high in 64% of models, and driven by the analysis domain. No model met eligibility for meta-analysis if studies at high risk of bias were excluded, and certainty of effect estimates was 'low'. No clinical utility analyses or clinical impact studies were found for any of the models. Conclusions: Models derived and/or externally validated for prediction of incident CKD in community-based EHRs demonstrate good prediction performance, but assessment of clinical usefulness is limited by high risk of bias, low certainty of evidence and a lack of impact studies.

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. Data suggest that women and older people less frequently receive guideline-recommended treatment than men and younger people. There are limited publications about ethnicity in the UK for the studied disease areas. Finally, there is inter-healthcare provider variation in cardiovascular care provision, especially for transcatheter aortic valve implantation, which is associated with differing outcomes for patients with the same disease. Providing equitable care is a founding principle of the UK NHS, which is well positioned to deliver innovative policy responses to reverse observed inequalities. Understanding differences in care may enable the implementation of appropriate strategies to mitigate differences in outcomes.

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. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway. METHODS AND ANALYSIS: The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring, and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring. ETHICS AND DISSEMINATION: The study has ethical approval (the North West-Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder's open access policy. TRIAL REGISTRATION NUMBER: NCT05898165.

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. The risk of AF was estimated using the FIND-AF (Future Innovations in Novel Detection of Atrial Fibrillation) risk score. We calculated cumulative incidence rates and fit Fine and Gray's models at 1, 5 and 10 years for nine diseases and death adjusting for competing risks. RESULTS: Of 416 228 individuals in the cohort, 82 942 were identified as higher risk for AF. Higher-predicted risk, compared with lower-predicted risk, was associated with incident chronic kidney disease (cumulative incidence per 1000 persons at 10 years 245.2; HR 6.85, 95% CI 6.70 to 7.00; median time to event 5.44 years), heart failure (124.7; 12.54, 12.08 to 13.01; 4.06), diabetes mellitus (123.3; 2.05, 2.00 to 2.10; 3.45), stroke/transient ischaemic attack (118.9; 8.07, 7.80 to 8.34; 4.27), myocardial infarction (69.6; 5.02, 4.82 to 5.22; 4.32), peripheral vascular disease (44.6; 6.62, 6.28 to 6.98; 4.28), valvular heart disease (37.8; 6.49, 6.14 to 6.85; 4.54), aortic stenosis (18.7; 9.98, 9.16 to 10.87; 4.41) and death from any cause (273.9; 10.45, 10.23 to 10.68; 4.75). The higher-risk group constituted 74% of deaths from cardiovascular or cerebrovascular causes (8582 of 11 676). CONCLUSIONS: Individuals identified for risk-guided AF screening are at risk of new diseases across the cardio-renal-metabolic spectrum and death, and may benefit from interventions beyond ECG monitoring.

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. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval [CI] 0.707-0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677-0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792-0.843), PCP-HF white women model (0.852, 95% CI 0.804-0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748-0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. CONCLUSIONS: Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

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. We studied the relationship between the expression of the meiosis protein HORMAD1 and genomic instability in squamous cell carcinomas (SCCs). First, we assessed markers of DNA damage and genomic instability following knockdown and overexpression of HORMAD1 in different cell lines representing SCCs and epithelial cancers. shRNA-mediated depletion of HORMAD1 expression resulted in increased genomic instability, DNA damage, increased sensitivity to etoposide, and decreased expression of DNA damage response/repair genes. Conversely, overexpression of HORMAD1 exhibited protective effects leading to decreased DNA damage, enhanced survival and decreased sensitivity to etoposide. Furthermore, we identified a meiotic molecular pathway that regulates HORMAD1 expression by targeting the upstream meiosis transcription factor STRA8. Our results highlight a specific relationship between HORMAD1 and genomic instability in SCCs, suggesting that selectively inhibiting HORMAD1, possibly, through STRA8 signaling, may provide a new paradigm of treatment options for HORMAD1-expressing SCCs.

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. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C2HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. RESULTS: Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA2DS2-VASc (0.784, 0.773 to 0.794) and C2HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). CONCLUSIONS: FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.

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. However, the underlying mechanisms remain elusive. There is a debate as to whether instability succeeds transformation, or if it is a by-product of cancer, and therefore, studying potential molecular and cellular contributors of genomic instability is of high importance. Recent work has suggested an important role for ectopic expression of meiosis genes in driving genomic instability via a process called meiomitosis. Improving understanding of these mechanisms can contribute to the development of targeted therapies that exploit DNA damage and repair mechanisms. Here, we discuss a workflow of novel and established techniques used to assess chromosomal instability as well as the nature of genomic instability such as double strand breaks, micronuclei, and chromatin bridges. For each technique, we discuss their advantages and limitations in a lab setting. Lastly, we provide detailed protocols for the discussed techniques.