Cluster Individuals Based on Phenotype and Determine the Risk for Atrial Fibrillation in the PREVEND and Framingham Heart Study Populations

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 heteroge...

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Bibliographic Details
Published in:PloS one 2016-11, Vol.11 (11), p.e0165828-e0165828
Main Authors: Rienstra, Michiel, Geelhoed, Bastiaan, Yin, Xiaoyan, Siland, Joylene E, Vermond, Rob A, Mulder, Bart A, Van Der Harst, Pim, Hillege, Hans L, Benjamin, Emelia J, Van Gelder, Isabelle C
Format: Article
Language:English
Subjects:
Adult
Asthma
Atrial fibrillation
Atrial Fibrillation - epidemiology
Attention deficit hyperactivity disorder
Biology and Life Sciences
Cardiac arrhythmia
Cardiology
Cardiovascular disease
Care and treatment
Cholesterol
Cluster Analysis
Diabetes
End-stage renal disease
Feasibility studies
Female
Fibrillation
Follow-Up Studies
Genetic aspects
Health care
Health risk assessment
Health risks
Heart
Heart attacks
Heart failure
Heterogeneity
Humans
Incidence
Kidney diseases
Male
Mathematical models
Medical research
Medicine and Health Sciences
Middle Aged
Netherlands - epidemiology
Offspring
Phenotype
Population studies
Preventive medicine
Prospective Studies
Reclassification
Regression analysis
Risk analysis
Risk Factors
Risk sharing
Smoking
Statistical analysis
Type 2 diabetes
United States - epidemiology
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Summary: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. Two 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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0165828