Construction of a predictive diagnostic model for pulmonary aspergillosis using GM test combined with serum albumin
10.3969/j.issn.1673-4130.2024.21.002
- VernacularTitle:GM试验联合血清白蛋白构建肺曲霉菌病预测诊断模型
- Author:
Yunxia ZHAI
1
;
Ping XU
;
Jing ZHAO
;
Jing XUE
;
Fanghua LI
;
Jin LI
Author Information
1. 苏州市第五人民医院检验中心,江苏苏州 215131
- Keywords:
pulmonary aspergillosis;
1,3-β-D Glucan test;
galactomannan test;
albumin;
predictive model
- From:
International Journal of Laboratory Medicine
2024;45(21):2566-2571,2576
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the biochemical indicators,nutritional status,and immune levels of pa-tients with pulmonary aspergillosis(PA)and other pulmonary diseases,and to construct a predictive model for PA so as to improve the diagnostic efficacy of clinical PA.Methods A total of 40 PA patients and 39 pa-tients with other pulmonary diseases who were hospitalized in the hospital from January 2020 to August 2022 were retrospectively analyzed.The expression trends and differences of serum 1,3-β-D Glucan(G test),galac-tomannan test(GM test),biochemical indexes,blood routine indexes and immune cell subsets were analyzed and compared.The receiver operating characteristic(ROC)curve and binary Logistic regression analysis were used to construct the predictive model for PA by the combination of clinical indicators.Results Serum GM test,G test,albumin,hemoglobin,hematocrit,lymphocytes,B lymphocytes,CD44 T lymphocytes and CD4/CD8 ratio displayed significant differences between PA patients and patients with other lung disease(P＜0.05).The levels of GM test in alveolar lavage fluid of PA patients were significantly higher than that in the serum,and the differences were statistically significant(P＜0.05).The ROC curve analysis showed that the GM test,as an independent predictor of PA,had good predictive accuracy[0.85＜area under the curve(AUC)＜0.95].Besides,albumin,natural killev cells,CD4+T lymphocytes and CD4/CD8 ratio had general predictive efficacy(0.70＜AUC＜0.85).The prediction efficacy of G test and B lymphocytes was poor(AUC＜0.70).The Logistic regression analysis showed that the combination of GM test and serum albumin could construct the optimal prediction model,and the prediction formula of the combined model was as fol-lows:Logit(P)=17.781× GM-0.131×albumin+1.394.The prediction accuracy of the combined model was 0.924(95％CI:0.865-0.982),the sensitivity was 87.5％,the specificity was 81.2％,and the cut off value was 17.781×GM-0.131×albumin-1.735.Conclusion This study retrospectively analyzed the differences in various clinical indicators between patients with PA and patients with other pulmonary diseases,and then screen the key clinical indicators as candidate predictors which displayed significantly different ex-pression between the two groups.The optimal prediction model for the diagnosis of PA is constructed by the combination of GM test and serum albumin through ROC curve and Logistic regression analysis.This model may significantly improve the diagnostic efficiency of PA in clinical,and provide the reference for the early di-agnosis and effective treatment of PA patients.
