Clinical features and corresponding immune function status of recurrent viral polymerase chain reaction positivity in patients with COVID-19 : A meta- analysis and systematic review

Ren, Xiaotun; Wang, Xiankun; Ge, Ziruo; Cui, Shu; Chen, Zhihai

doi:10.1177/20587384211027679

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Moreover, the use of tocilizumab, a blocker of IL-6 receptor (IL-6R), has been recommended for severe cases of COVID-19 ( Huang et al., 2020 ; Ruan et al., 2020 ; Wu et al., 2020a ; Angriman et al., 2021 ; Galván-Román et al., 2021 ; Mcelvaney et al., 2021 ). IL-6 is also reported as one of the good predictors of progression and severity in patients with COVID-19 ( Guirao et al., 2020 ; Liu et al., 2020 ; Broman et al., 2021 ; Ren et al., 2021 ). In addition, it is suggested that an elevated level of IL-6 is an important predictor of patients with severe COVID-19 needing ventilator support ( Galván-Román et al., 2021 ).…”

Section: Discussionmentioning

confidence: 98%

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

Hong

Zhou

Jin

et al. 2022

Front. Cell. Infect. Microbiol.

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Background and AimsThe aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients.MethodsClinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis.ResultsUnivariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot.ConclusionsXGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

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Section: Discussionmentioning

confidence: 98%

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

Hong

Zhou

Jin

et al. 2022

Front. Cell. Infect. Microbiol.

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“…Furthermore, recent animal experiments have revealed a strong correlation between IL-6 and TNF-α and the pathological progression of Streptococcus pneumoniae-induced pneumonia [6]. The latest research suggests that the in ammatory response plays a pivotal role in coronavirus disease 2019 (COVID- 19), while an excessive release of in ammatory cytokines exacerbates the severity of COVID-19 [7]. The serum levels of IL-6 and CRP exhibit a signi cant correlation with the severity of COVID-19, thereby serving as independent prognostic factors for disease risk prediction [8].…”

Section: Introductionmentioning

confidence: 99%

Prognostic value of Th1/Th2/Th17-related cytokines in severe pneumonia

Zhang,

Fang,

Zhou

et al. 2023

Preprint

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Background To investigate the value of Th1/Th2/Th17-related cytokine expression in the prognosis of patients with severe pneumonia (SP). Methods The observational group consisted of 106 patients diagnosed with severe pneumonia who were admitted to the Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University or the Affiliated Hospital of Xuzhou Medical University between June 2018 and July 2022. On the day of admission, flow cytometry was used to evaluate serum levels of Th1/Th2/Th17 cytokines, including tumor necrosis factor-α (TNF-α), interferon-γ (INF-γ), interleukin (IL)-2, IL-4, IL-6, IL-10, IL-17A, procalcitonin (PCT) and high-sensitivity C-reactive protein (hs-CRP). Meanwhile, Pneumonia Severity Index (PSI) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were assessed in all patients within 24 hours of admission. The clinical outcomes of patients with severe pneumonia on the 30th day were followed up. According to the results, the participants were categorized into two groups based on their survival status: the survival group and the deceased group. Subsequently, an analysis was conducted to identify the risk factors that significantly influenced the outcome. Results According to the 30th day outcome, 106 patients were divided into the survival group and the deceased group. The levels of IL-6, IL-10, and IL-17A were significantly elevated in the deceased group compared to the survival group (P<0.05). Univariate logistic regression analysis identified age, diabetes, high PSI score and APACHE II score at admission, as well as elevated levels of IL-6, IL-10, IL-17A, PCT and hs-CRP as significant prognostic risk factors (P < 0.05). The area under the Receiver Operating Characteristic (ROC) curve for IL-6, PCT, IL-17A, IL-10, and hs-CRP was 0.862, 0.714, 0.673, 0.639, and 0.642,respectively, indicating that IL-6 exhibited superior predictive value for mortality. Significant indicators in single-factor regression analysis included age, diabetes, APACHE II score, PSI score, Th17/Th1/Th2 cytokines, PCT and hs-CRP. After controlling for confounding variables using multivariate logistic regression analysis, high APACHE II score, high PSI score and elevated IL-6 levels at admission were identified as independent risk factors affecting patient outcomes (OR=1.305, 1.093 and 1.054; 95% CI: 1.009-1.687, 1.040-1.148 and 1.026-1.082). Based on the results of multivariate regression analysis, ROC curves were generated to predict the clinical outcome of SP patients using three factors alone and in combination. The area under the curve (AUC) values for IL-6, APACHE II score, PSI score, and their combination were 0.862, 0.826, 0.872, and 0.963 respectively, indicating that the incorporation of these three factors in a combined prediction model demonstrated superior efficacy. Conclusions IL-6, IL-10, and IL-17A are prognostic risk factors for patients with severe pneumonia, with IL-6 being an independent factor. The combination of IL-6, PSI score and APACHE II score has greater clinical significance in predicting the prognosis of SP patients.

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Clinical features and independent predictors for recurrence of positive SARS‐CoV‐2 RNA: A propensity score‐matched analysis

Liu

Yang

Feng

et al. 2021

Journal of Medical Virology

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Patients with COVID-19 may be recurrence positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA after being cured and discharged from the hospital. The aim of this study was to explore independent influencing factors as markers for predicting positive SARS-CoV-2 RNA recurrence. The study included 601 COVID-19 patients who were cured and discharged from the Public and Health Clinic Centre of Chengdu from January 2020 to March 2021, and the recurrence positive of patients within 6 weeks after SARS-CoV-2 RNA turned negative was followed up. We used propensity score matching to eliminate the influence of confounding factors, and multivariate Logistic regression analysis was used to determine the independent influencing factors for positive SARS-CoV-2 RNA recurrence. Multivariate Logistic regression showed that the elevated serum potassium

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Clinical features and corresponding immune function status of recurrent viral polymerase chain reaction positivity in patients with COVID-19 : A meta- analysis and systematic review

Cited by 3 publications

References 38 publications

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

Prognostic value of Th1/Th2/Th17-related cytokines in severe pneumonia

Clinical features and independent predictors for recurrence of positive SARS‐CoV‐2 RNA: A propensity score‐matched analysis

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