Nomogram construction to predict dyslipidemia based on a logistic regression analysis

Dyslipidemia is a chronic disease requiring continuous management and is a well-known risk factor for cardiovascular diseases as well as hypertension and diabetes. However, no studies have so far visualized and predicted the probability of dyslipidemia. Hence, this study proposes a nomogram based on...

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Bibliographic Details
Published inJournal of applied statistics Vol. 47; no. 5; pp. 914 - 926
Main Authors Seo, Ju-Hyun, Kim, Hyun-Ji, Lee, Jea-Young
Format Journal Article
LanguageEnglish
Published England Taylor & Francis 03.04.2020
Taylor & Francis Ltd
Subjects
Application Notes
Chi-square test
Dyslipidemia
Health risk assessment
Hypertension
incidence rate
logistic regression analysis
Metabolic disorders
nomogram
Nomograms
Other
Regression analysis
Regression models
Risk analysis
risk factors
Statistical analysis
Statistical methods
Statistical tests
logistic regression analysis
risk factors
nomogram
incidence rate
Dyslipidemia
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Summary:Dyslipidemia is a chronic disease requiring continuous management and is a well-known risk factor for cardiovascular diseases as well as hypertension and diabetes. However, no studies have so far visualized and predicted the probability of dyslipidemia. Hence, this study proposes a nomogram based on a logistic regression model that can visualize its risk factors and predict the probability of developing dyslipidemia. Twelve risk factors for dyslipidemia are identified through a chi-squared test. We then conduct a logistic regression analysis with two interaction variables to obtain a model and build a nomogram for dyslipidemia. Finally, we verify the constructed nomogram using a receiver operation characteristic curve and calibration plot.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2019.1660760