Constructing A Nomogram Prediction Model for Active Pulmonary Tuberculosis Based on AAT and Cytokines

Hao FAN; Xing LIU; Lingjun SHEN; Haiwen LI; Chunhong YU; Jingwei LI

doi:10.12259/j.issn.2095-610X.S20220816

Volume 43 Issue 8

Jul. 2022

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Hao FAN, Xing LIU, Lingjun SHEN, Haiwen LI, Chunhong YU, Jingwei LI. Constructing A Nomogram Prediction Model for Active Pulmonary Tuberculosis Based on AAT and Cytokines[J]. Journal of Kunming Medical University, 2022, 43(8): 106-112. doi: 10.12259/j.issn.2095-610X.S20220816

Citation:

Hao FAN, Xing LIU, Lingjun SHEN, Haiwen LI, Chunhong YU, Jingwei LI. Constructing A Nomogram Prediction Model for Active Pulmonary Tuberculosis Based on AAT and Cytokines[J]. Journal of Kunming Medical University, 2022, 43(8): 106-112. doi: 10.12259/j.issn.2095-610X.S20220816

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Constructing A Nomogram Prediction Model for Active Pulmonary Tuberculosis Based on AAT and Cytokines

doi: 10.12259/j.issn.2095-610X.S20220816

Hao FAN¹,
Xing LIU^{2
,
,},
Lingjun SHEN^{2
,
,},
Haiwen LI²,
Chunhong YU¹,
Jingwei LI¹

1.
School of Pharmaceutical Sciences，Dali University，Dali Yunnan 671000
2.
Dept. of Pharmacy，Yunnan Clinical Center for Infectious Diseases/Kunming Third People’s Hospital，Kunming Yunnan 650041，China

Received Date: 2022-04-12
Publish Date: 2022-08-25

Abstract

Abstract

Objective To construct a predictive model of AAT and cytokines in the diagnosis of active pulmonary tuberculosis. Methods A total of 96 patients with active pulmonary tuberculosis admitted to the Third People’s Hospital of Kunming from March 2020 to March 2021 were collected as the experimental group, and 82 healthy subjects during the same period were selected as the control group. HAP, CRP, AAT and cytokines were compared between the two groups. Based on the results of Logistic regression analysis, a Nomogram prediction model was constructed, and the model was verified and evaluated. Results Multivariate logistic regression analysis showed that AAT （OR = 0.983, 95% CI = 0.968-0.999, P = 0.039）, IFN-γ （OR = 0.783, 95% CI = 0.659-0.931, P = 0.006）, TNF-α （OR = 1.495, 95% CI = 1.106-2.020, P = 0.009） are predictors of active pulmonary tuberculosis （P < 0.05）. Conclusion The fit of the model and the area under the ROC curve were good, which confirmed that the model had a high prediction accuracy. The Nomogram model established based on the above predictors has good predictive performance and can provide a certain reference value for the laboratory diagnosis of active pulmonary tuberculosis.
- Active pulmonary tuberculosis,
- Predictors,
- Nomogram model

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References(26)

References

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