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. 2025 Apr 25:18:2093-2104.
doi: 10.2147/IDR.S509178. eCollection 2025.

A Nomogram for Predicting Survival in Patients with SARS-CoV-2 Omicron Variant Pneumonia Based on Admission Data

Yinghao Yang #  1   2 , Dong Li #  1   3 , Jinqiu Nie #  1 , Junxue Wang  1 , Huili Huang  1 , Xiaofeng Hang  1
Affiliations

A Nomogram for Predicting Survival in Patients with SARS-CoV-2 Omicron Variant Pneumonia Based on Admission Data

Yinghao Yang et al. Infect Drug Resist. .

Abstract

Purpose: Patients with severe SARS-CoV-2 omicron variant pneumonia pose a serious challenge. This study aimed to develop a nomogram for predicting survival using chest computed tomography (CT) imaging features and laboratory test results based on admission data.

Patients and methods: A total of 436 patients with SARS-CoV-2 pneumonia (323 and 113 in the training and validation groups, respectively) were enrolled. Pneumonitis volume, assessed on chest CT scans at admission, was used to identify low- and high-risk groups. Risk analysis was performed using clinical symptoms, laboratory findings, and chest CT imaging features. A predictive algorithm was developed using Cox multivariate analysis.

Results: The high-risk group had a shorter survival duration than the low-risk group. Significant differences in mortality rate, neutrophil and lymphocyte counts, C-reactive protein (CRP) concentration, and urea nitrogen level were observed between the two groups. In the training group, age, pneumonia volume, total bilirubin, and blood urea nitrogen were independent prognostic factors. In the validation group, age, pneumonia volume, neutrophil count, and CRP were independent prognostic factors. A personalized prediction model for survival outcomes was developed using independent predictors.

Conclusion: A personalized prediction model was created to forecast the 5-, 10-, 15-, 20-, and 30-day survival rates of patients with COVID-19 omicron variant pneumonia based on admission data, and can be used to determine the survival rate and early treatment of severe patients.

Keywords: COVID-19; omicron; pneumonia; predictive nomogram; prognosis.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.

Figures

Figure 1
Figure 1
Flowchart of participant inclusion and exclusion.
Figure 2
Figure 2
The relationship between pneumonia volume and clinical characteristics of COVID-19. (A and B) Violin charts showed the distribution of clinical characteristics between different pneumonia volume of COVID-19 in training and validation groups. The significance of the difference between the two groups was verified by Mann Whitney test.
Figure 3
Figure 3
The signature was found to well predict the survival information. (A) Pneumonia volume replied to survival information in the training database (P < 0.0001). (B) Pneumonia volume replied to survival information in the validation database (P =0.03).
Figure 4
Figure 4
The individualized prediction models for survival in COVID-19. (A) The 5, 10, 15, 20, and 30-day survival of COVID-19 patients could exactly be predicted by the nomogram. (B) The Calibration plots showed the comparison of survival for 5, 10, 15, 20, and 30-day survival probabilities in training and validation groups. (C) The predictive effect of the individualized prediction model, pneumonia volume and clinical prognostic factors of COVID-19 patients on survival was evaluated by C-Index.
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