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Observational Study
. 2024 May 29;24(1):261.
doi: 10.1186/s12890-024-03073-y.

Predicting the potentially exacerbation of severe viral pneumonia in hospital by MuLBSTA score joint CD4 + and CD8 +T cell counts: construction and verification of risk warning model

Xi Chen #  1   2 , Bei Ma #  2 , Yu Yang  2 , Mu Zhang  3 , Fang Xu  4
Affiliations
Observational Study

Predicting the potentially exacerbation of severe viral pneumonia in hospital by MuLBSTA score joint CD4 + and CD8 +T cell counts: construction and verification of risk warning model

Xi Chen et al. BMC Pulm Med. .

Abstract

Purpose: This study mainly focuses on the immune function and introduces CD4+, CD8+ T cells and their ratios based on the MuLBSTA score, a previous viral pneumonia mortality risk warning model, to construct an early warning model of severe viral pneumonia risk.

Methods: A retrospective single-center observational study was operated from January 2021 to December 2022 at the People's Hospital of Liangjiang New Area, Chongqing, China. A total of 138 patients who met the criteria for viral pneumonia in hospital were selected and their data, including demographic data, comorbidities, laboratory results, CT scans, immunologic and pathogenic tests, treatment regimens, and clinical outcomes, were collected and statistically analyzed.

Results: Forty-one patients (29.7%) developed severe or critical illness. A viral pneumonia severe risk warning model was successfully constructed, including eight parameters: age, bacterial coinfection, CD4+, CD4+/CD8+, multiple lung lobe infiltrations, smoking, hypertension, and hospital admission days. The risk score for severe illness in patients was set at 600 points. The model had good predictive performance (AUROC = 0.94397), better than the original MuLBSTA score (AUROC = 0.8241).

Conclusion: A warning system constructed based on immune function has a good warning effect on the risk of severe conversion in patients with viral pneumonia.

Keywords: MuLBSTA score; Viral pneumonia; Warning system.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
LASSO regression analysis: 9 features were identified as the potential predictors
Fig. 2
Fig. 2
Building a Prediction Model and Evaluating its Performance. A ROC diagram of the training set model; B K-S diagram of the training set model; C Lift diagram of the training set model. D ROC diagram of test set model; E K-S diagram of test set model; F Lift diagram of test set model; G ROC curve of MulBSTA score prediction
Fig. 3
Fig. 3
Evaluation of a severe risk scorecard. A Model nomogram; B Evaluation card PSI evaluation result plot
See this image and copyright information in PMC

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