Genetic Risk Scores for Atrial Fibrillation: Do They Improve Risk Estimation?

Huang, Henry; Darbar, Dawood

doi:10.1016/j.cjca.2016.12.006

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…genome-wide association studies, to derive polygenic risk scores for the development of AF. 92–94 Nonetheless, statistical approaches are limited in their ability to identify relevant, potentially non-linear, interactions between numerous parameters that may be required for optimal prediction of the outcome of interest in today’s large data sets. Artificial intelligence and ML may overcome this limitation.…”

Section: Data-driven Models For Af Managementmentioning

confidence: 99%

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Heijman

Sutanto

Crijns

et al. 2021

Cardiovascular Research

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Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodeling processes. Atrial remodeling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modeling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modeling in AF management. We summarize clinical challenges that may benefit from computational modeling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modeling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

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Section: Data-driven Models For Af Managementmentioning

confidence: 99%

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Heijman

Sutanto

Crijns

et al. 2021

Cardiovascular Research

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show abstract

“…In many patients, these changes may include left ventricular hypertrophy, diastolic dysfunction, left atrial enlargement, left atrial fibrosis, left atrial stiffness, and autonomic dysfunction. In other cases of atrial fibrillation especially in young patients, no identifiable risk factors may exist, suggesting a possible genetic predisposition 232425. Regardless, atrial fibrillation can by itself sustain and further promote atrial, ventricular, and systemic structural and functional alterations.…”

Section: Epidemiologymentioning

confidence: 99%

Screening and management of atrial fibrillation in primary care

Ponamgi

Siontis

Rushlow

et al. 2021

BMJ

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Atrial fibrillation is a common chronic disease seen in primary care offices, emergency departments, inpatient hospital services, and many subspecialty practices. Atrial fibrillation care is complicated and multifaceted, and, at various points, clinicians may see it as a consequence and cause of multi-morbidity, as a silent driver of stroke risk, as a bellwether of an acute medical illness, or as a primary rhythm disturbance that requires targeted treatment. Primary care physicians in particular must navigate these priorities, perspectives, and resources to meet the needs of individual patients. This includes judicious use of diagnostic testing, thoughtful use of novel therapeutic agents and procedures, and providing access to subspecialty expertise. This review explores the epidemiology, screening, and risk assessment of atrial fibrillation, as well as management of its symptoms (rate and various rhythm control options) and stroke risk (anticoagulation and other treatments), and offers a model for the integration of the components of atrial fibrillation care.

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“…However, the accuracy of current polygenic risk scores, measured with the AUC metric (Area under the ROC Curve, where ROC stands for receiver operating characteristic, see Mandrekar (2010)), varies substantially for important diseases. For instance, the AUC achieved by state-of-the-art methods ranges from around 0.8 for type 1 diabetes to around 0.7 for coronary artery disease and schizophrenia (Mak et al, 2017), while for atrial fibrillation the AUC is around 0.64 (Huang and Darbar, 2017), a value which is considered less than acceptable (Mandrekar, 2010; Hosmer and Lemeshow, 2000). For this reason, increasing the accuracy of scores is desirable, which is the focus of the proposed smoothing approach.…”

Section: Introductionmentioning

confidence: 99%

A smoothed version of the Lassosum penalty for fitting integrated risk models

Hahn

Prokopenko

Lutz

et al. 2021

Preprint

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Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the "Lassosum" penalty used to fit polygenic risk scores and integrated risk models. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm, allowing it to draw equal with state-of-the-art methodology such as LDpred when evaluated via the AUC (area under the ROC curve) metric.

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Genetic Risk Scores for Atrial Fibrillation: Do They Improve Risk Estimation?

Cited by 14 publications

References 19 publications

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Screening and management of atrial fibrillation in primary care

A smoothed version of the Lassosum penalty for fitting integrated risk models

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