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- Title
Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening.
- Authors
Jianning Liu; Zhuoying Feng; Ru Gao; Peng Liu; Fangang Meng; Lijun Fan; Lixiang Liu; Yang Du
- Abstract
Objective: This study aims to analyze the association between the occurrence of thyroid nodules and various factors and to establish a risk factor model for thyroid nodules. Methods: The study population was divided into two groups: a group with thyroid nodules and a group without thyroid nodules. Regression with the least absolute shrinkage and selection operator (Lasso) was applied to the complete dataset for variable selection. Binary logistic regression was used to analyze the relationship between various influencing factors and the prevalence of thyroid nodules. Results: Based on the screening results of Lasso regression and the subsequent establishment of the Binary Logistic Regression Model on the training dataset, it was found that advanced age (OR=1.046, 95% CI: 1.033-1.060), females (OR = 1.709, 95% CI: 1.342-2.181), overweight individuals (OR = 1.546, 95% CI: 1.1652.058), individuals with impaired fasting glucose (OR = 1.590, 95% CI: 1.1932.122), and those with dyslipidemia (OR = 1.588, 95% CI: 1.197-2.112) were potential risk factors for thyroid nodule disease (p<0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the Binary Logistic Regression Model is 0.68 (95% CI: 0.64-0.72). Conclusions: advanced age, females, overweight individuals, those with impaired fasting glucose, and individuals with dyslipidemia are potential risk factors for thyroid nodule disease.
- Subjects
THYROID nodules; NODULAR disease; MEDICAL screening; RECEIVER operating characteristic curves; LOGISTIC regression analysis; THYROID diseases
- Publication
Frontiers in Endocrinology, 2024, p01
- ISSN
1664-2392
- Publication type
Article
- DOI
10.3389/fendo.2024.1346284