Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infection Article Swipe
Tianyu Lu
,
Qiuhong Man
,
Xueying Yu
,
Shuai Xia
,
Lu Lu
,
Shibo Jiang
,
Lize Xiong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2023.1157892
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2023.1157892
Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has prevailed globally since November 2021. The extremely high transmissibility and occult manifestations were notable, but the severity and mortality associated with the Omicron variant and subvariants cannot be ignored, especially for immunocompromised populations. However, no prognostic model for specially predicting the severity of the Omicron variant infection is available yet. In this study, we aim to develop and validate a prognostic model based on immune variables to early recognize potentially severe cases of Omicron variant-infected patients. Methods This was a single-center prognostic study involving patients with SARS-CoV-2 Omicron variant infection. Eligible patients were randomly divided into the training and validation cohorts. Variables were collected immediately after admission. Candidate variables were selected by three variable-selecting methods and were used to construct Cox regression as the prognostic model. Discrimination, calibration, and net benefit of the model were evaluated in both training and validation cohorts. Results Six hundred eighty-nine of the involved 2,645 patients were eligible, consisting of 630 non-ICU cases and 59 ICU cases. Six predictors were finally selected to establish the prognostic model: age, neutrophils, lymphocytes, procalcitonin, IL-2, and IL-10. For discrimination, concordance indexes in the training and validation cohorts were 0.822 (95% CI: 0.748-0.896) and 0.853 (95% CI: 0.769-0.942). For calibration, predicted probabilities and observed proportions displayed high agreements. In the 21-day decision curve analysis, the threshold probability ranges with positive net benefit were 0~1 and nearly 0~0.75 in the training and validation cohorts, correspondingly. Conclusions This model had satisfactory high discrimination, calibration, and net benefit. It can be used to early recognize potentially severe cases of Omicron variant-infected patients so that they can be treated timely and rationally to reduce the severity and mortality of Omicron variant infection.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2023.1157892
- OA Status
- gold
- Cited By
- 4
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322617904
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322617904Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fimmu.2023.1157892Digital Object Identifier
- Title
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Development and validation of a prognostic model based on immune variables to early predict severe cases of SARS-CoV-2 Omicron variant infectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-01Full publication date if available
- Authors
-
Tianyu Lu, Qiuhong Man, Xueying Yu, Shuai Xia, Lu Lu, Shibo Jiang, Lize XiongList of authors in order
- Landing page
-
https://doi.org/10.3389/fimmu.2023.1157892Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fimmu.2023.1157892Direct OA link when available
- Concepts
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Medicine, Concordance, Procalcitonin, Internal medicine, Proportional hazards model, Prognostic variable, Sepsis, Overall survivalTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2024: 3, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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