Application of multi-parameter model based on test indicators in clinical evaluation of idiopathic pulmonary fibrosis
10.3760/cma.j.cn114452-20240306-00114
- VernacularTitle:基于检验指标构建多参数模型在特发性肺纤维化临床评估中的应用
- Author:
Lijuan HU
1
;
Ruoyu LIU
;
Yun ZHOU
;
Yongtong CAO
Author Information
1. 北京协和医学院研究生院，北京 100730
- Keywords:
Pulmonary fibrosis;
Idiopathic pulmonary fibrosis;
Biomarkers;
Combined diagnostic model
- From:
Chinese Journal of Laboratory Medicine
2024;47(10):1139-1151
- CountryChina
- Language:Chinese
-
Abstract:
Objective:The combined diagnosis models was constructed with the test indicators and its application value in the clinical evaluation of patients with interstitial lung disease was evaluated.Methods:Methodology development and validation. A total of 101 patients with idiopathic pulmonary fibrosis (IPF) and 107 patients with non-IPF interstitial lung disease admitted to China-Japan Friendship Hospital from 2022 to 2023 were collected, and 98 healthy people were collected during the same period. The population in each group was divided into modeling group (180 cases) and validation group (126 cases) by complete randomization. Serum samples and clinical test results were collected. The test indicators included white blood cell count, lymphocyte count, monocyte count, hemoglobin concentration, highly sensitive C-reactive protein, Krebs von den Lungen 6, total cholesterol, triglycerides, high density lipoprotein cholesterol, low density lipoprotein cholesterol, adenosine deaminase, neuron-specific enolase, alpha-fetoprotein, carcinoembryonic antigen, cytokeratin 19 fragment, carbohydrate antigen 15-3, gastrin releasing peptide precursor, squamous cell carcinoma antigen and interleukin 1 (IL-1), IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, tumor necrosis factor-α, interferon-α, interferon-γ. Multiple collinearity test, univariate and multivariate logistic regression were performed for the included test indicators in each group, and nomograms were established and validated by receiver operating characteristic (ROC) curves, calibration curves and clinical decision curves.Results:By comparing interstitial lung disease to healthy people, carbohydrate antigen 15-3 ( OR=1.285, 95% CI 1.178-1.402), IL-6 ( OR=1.128, 95% CI 1.011-1.258), adenosine deaminase ( OR=1.465, 95% CI 1.261-1.702), and Krebs von den Lungen-6 ( OR=1.013, 95% CI 1.008-1.017) were independent risk factors for interstitial lung disease. Based on these four indexes, the nomogram model was constructed. The AUCs of the combined diagnosis model in the modeling group and validation group were 0.967(95%CI 0.941-0.993)and 0.948(95% CI 0.911-0.984), respectively.Decision curve analysis showed that the net benefit of the combined diagnosis model in diagnosing IPF was higher than that of a single indicator within the threshold range of 0.01-1. In the comparison of IPF and non-IPF interstitial lung disease, alpha-fetoprotein ( OR=1.403, 95% CI 0.975-2.019) and squamous cell carcinoma antigen ( OR=0.531, 95% CI 0.321-0.878) were independent risk factors for IPF. The AUCs of the combined diagnosis model in the modeling group and validation group were 0.703 (95% CI 0.597-0.81) and 0.642 (95% CI 0.528-0.757), respectively. Through calibration curve and clinical decision curve verification, it was found that it had a certain value in the differential diagnosis of IPF. Conclusions:Carbohydrate antigen 15-3, IL-6, adenosine deaminase and Krebs von den Lungen 6 are risk factors of interstitial lung disease, which can be used to construct a combined diagnostic model for the diagnosis of interstitial lung disease. Alpha-fetoprotein and squamous cell carcinoma antigen are risk factors of IPF, which can be used to construct a combined diagnostic model to distinguish IPF from non-IPF interstitial lung disease and assist clinical diagnosis of IPF.
