Abstract
BackgroundRisk prediction of atrial fibrillation (AF) is of importance to improve the early diagnosis and treatment of AF. Latent class analysis takes into account the possible existence of classes of individuals each with shared risk factors, and maybe a better method of incorporating the phenotypic heterogeneity underlying AF.Methods and findingsTwo prospective community-based cohort studies from Netherlands and United States were used. Prevention of Renal and Vascular End-stage Disease (PREVEND) study, started in 1997, and the Framingham Heart Study (FHS) Offspring cohort started in 1971, both with 10-years follow-up. The main objective was to determine the risk of AF using a latent class analysis, and compare the discrimination and reclassification performance with traditional regression analysis. Mean age in PREVEND was 49±13 years, 49.8% were men. During follow-up, 250(3%) individuals developed AF. We built a latent class model based on 18 risk factors. A model with 7 distinct classes (ranging from 341 to 1517 individuals) gave the optimum tradeoff between a high statistical model-likelihood and a low number of model parameters. All classes had a specific profile. The incidence of AF varied; class 1 0.0%, class 2 0.3%, class 3 7.5%, class 4 0.2%, class 5 1.3%, class 6 4.2%, class 7 21.7% (p<0.001). The discrimination (C-statistic 0.830 vs. 0.842, delta-C -0.013, p = 0.22) and reclassification (IDI -0.028, p<0.001, NRI -0.090, p = 0.049, and category-less-NRI -0.049, p = 0.495) performance of both models was comparable. The results were successfully replicated in a sample of the FHS study (n = 3162; mean age 58±9 years, 46.3% men).ConclusionsLatent class analysis to build an AF risk model is feasible. Despite the heterogeneity in number and severity of risk factors between individuals at risk for AF, latent class analysis produces distinguishable groups.
Highlights
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is not a benign condition.[1,2,3,4,5] Despite the fact that in the last decades many risk factors for atrial fibrillation (AF), such as advancing age, hypertension, obesity, diabetes, and cardiovascular diseases, such as heart failure, valve disease, and myocardial infarction, have been identified, the development of AF and its complications remains highly variable.[5,6,7,8] Some AF patients have multiple risk factors, where others have none; others have multiple risk factors but never develop AF
Latent class analysis is a statistical method that can be used for risk prediction, taking into account the possible existence of classes of individuals each with a different distribution of cardiovascular risk factors and diseases
We aim to determine the risk of AF in individuals of the community-based Prevention of Renal and Vascular End-stage Disease (PREVEND) study (Netherlands), using a latent class analysis, and compare the discrimination and reclassification performance with traditional Cox regression analysis-based AF risk prediction, and validate the risk model based on latent class analysis in the Framingham Heart Study (FHS) (United States)
Summary
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is not a benign condition.[1,2,3,4,5] Despite the fact that in the last decades many risk factors for AF, such as advancing age, hypertension, obesity, diabetes, and cardiovascular diseases, such as heart failure, valve disease, and myocardial infarction, have been identified, the development of AF and its complications remains highly variable.[5,6,7,8] Some AF patients have multiple risk factors, where others have none; others have multiple risk factors but never develop AF. Traditional risk-factor-based AF prediction models are far from ideal and do not account for the wide biological heterogeneity underlying AF risk.[9,10] Adequate risk assessment is of utmost importance to improve the utilization of diagnostic tools to detect AF in those at risk for AF, and to apply therapeutic strategies to prevent AF and its related morbidity and mortality.[11,12]. Latent class analysis is a statistical method that can be used for risk prediction, taking into account the possible existence of classes of individuals each with a different distribution of cardiovascular risk factors and diseases. Risk prediction of atrial fibrillation (AF) is of importance to improve the early diagnosis and treatment of AF. Latent class analysis takes into account the possible existence of classes of individuals each with shared risk factors, and maybe a better method of incorporating the phenotypic heterogeneity underlying AF
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
