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. 2025 Apr 3:15:1499154.
doi: 10.3389/fcimb.2025.1499154. eCollection 2025.

A model based on PT-INR and age serves as a promising predictor for evaluating mortality risk in patients with SARS-CoV-2 infection

Yongjie Xu #  1   2   3   4   5   6 , Minjie Tang #  1   2   3   4   5   6 , Zhaopei Guo #  1   2   3   4   5 , Yanping Lin #  7 , Hongyan Guo  1   2   3   4   5 , Fengling Fang  1   2   3   4   5 , Lin Lin  1   2   3   4   5 , Yue Shi  1   2   3   4   5 , Lu Lai  1   2   3   4   5 , Yan Pan  1   2   3   4   5 , Xiangjun Tang  1   2   3   4   5 , Weiquan You #  1   2   3   4   5 , Zishun Li  7 , Jialin Song  8 , Liang Wang  9 , Weidong Cai  1   2   3   4   5   6 , Ya Fu  1   2   3   4   5
Affiliations

A model based on PT-INR and age serves as a promising predictor for evaluating mortality risk in patients with SARS-CoV-2 infection

Yongjie Xu et al. Front Cell Infect Microbiol. .

Abstract

COVID-19 caused by the coronavirus SARS-CoV-2 has resulted in a global pandemic. Considering some patients with COVID-19 rapidly develop respiratory distress and hypoxemia, early assessment of the prognosis for COVID-19 patients is important, yet there is currently a lack of research on a comprehensive multi-marker approach for disease prognosis assessment. Here, we utilized a large sample of hospitalized individuals with COVID-19 to systematically compare the clinical characteristics at admission and developed a nomogram model that was used to predict prognosis. In all cases, those with pneumonia, older age, and higher PT-INR had a poor prognosis. Besides, pneumonia patients with older age and higher PT-INR also had a poor prognosis. A nomogram model incorporating presence of pneumonia, age and PT-INR could evaluate the prognosis in all patients with SARS-CoV-2 infections well, while a nomogram model incorporating age and PT-INR could evaluate the prognosis in those with pneumonia well. Together, our study establishes a prognostic prediction model that aids in the timely identification of patients with poor prognosis and helps facilitate the improvement of treatment strategies in clinical practice in the future.

Keywords: PT-INR; SARS-CoV-2; age; mortality; predictor.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Study flow diagram.
Figure 2
Figure 2
A nomogram model built based on pneumonia, age and PT-INR at admission for predicting prognosis post COVID-19 infection. (A, B) Lasso regression analysis. (C) Multivariate logistic regression analysis. (D) Construction of nomogram model based on multivariate logistic regression analysis. (E) Construction of calibration curves for the nomogram model. (F) Construction of ROC curves for nomogram scores, presence/absence of pneumonia, age, and PT-INR, respectively. (G-J) Cumulative events of effective treatment within 30 days of hospitalization based on cox regression analysis.
Figure 3
Figure 3
A nomogram model built based on age and PT-INR at admission for predicting prognosis post COVID-19 infection in patients with pneumonia. (A, B) Lasso regression analysis. (C) Multivariate logistic regression analysis. (D) Construction of nomogram model based on multivariate logistic regression analysis. (E) Construction of calibration curves for the nomogram model. (F) Construction of ROC curves for nomogram scores, age, and PT-INR, respectively. (G-I) Cumulative events of effective treatment within 30 days of hospitalization based on cox regression analysis.
Figure 4
Figure 4
The validation of the constructed nomogram models for evaluating prognosis. (A) The validation of the constructed nomogram model incorporating the presence of presence/absence of pneumonia, age and PT-INR. (B) The validation of the constructed nomogram model incorporating age and PT-INR.
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