Dynamic changes of lymphocyte counts in adult patients with severe pandemic H1N1 influenza A

Cheng, Yandong; Zhao, Hong; Song, Peixin; Zhang, Zhaoping; Chen, Junhao; Zhou, Yi‐Hua

doi:10.1016/j.jiph.2019.05.017

Cited by 48 publications

(45 citation statements)

References 31 publications

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“…[20] Giving further strength to our study, a study by Geng et al demonstrated the decline in the populations of T lymphocytes and their subsets, after in uenza A virus infection, to be positively correlated with prognosis. [21]We further performed ow cytometric analysis to enumerate total T cell population, CD4 + and CD8 + T cell subsets, B cells and NK cells in patients with non-severe COVID-19, severe COVID-19, and severe in uenza A to determine signi cant changes in different lymphocyte subsets during their rst week of illness. Our study demonstrated that patients with severe COVID-19 and severe in uenza A had a signi cantly lower number of total T cells (P = 0.001 and P < 0.0001, respectively), CD4 + T cell subsets (P = 0.001 and P < 0.0001, respectively), and CD8 + T cell subsets (P = 0.001 and P < 0.0001, respectively) than healthy controls.…”

Section: Discussionmentioning

confidence: 99%

Specific Dynamic Variations in the Peripheral Blood Lymphocyte Subsets in COVID-19 and Severe Influenza a Patients: A Retrospective Observational Study

Qian

Gao

Song

et al. 2020

Preprint

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Background: Both COVID-19 and influenza A contribute to increased mortality among the elderly and those with existing comorbidities. Changes in the underlying immune mechanisms determine patient prognosis. This study aimed to analyze the role of lymphocyte subsets in the immunopathogenesisof COVID-19 and severe influenza A, and examined the clinical significance of their alterations in the prognosis and recovery duration. Methods: By retrospectively reviewing of patients in four groups (healthy controls, severe influenza A, non-severe COVID-19 and severe COVID-19) who were admitted to Ditan hospital between 2018 to 2020, we performed flow cytometric analysis and compared the absolute counts of leukocytes, lymphocytes, and lymphocyte subsets of the patients at different time points (weeks 1–4). Results: We reviewed the patients’ data of 110 healthy blood donors, 80 Non-severe-COVID-19, 19 Severe-COVID-19 and 43 severe influenza A. We found total lymphocytes (0.93 × 109/L, 0.84 × 109/L vs 1.78 × 109/L, P < 0.0001) and lymphocyte subsets (T cells, CD4+ and CD8+ T cell subsets) of both severe patients to be significantly lower than those of healthy donors at early infection stages. Further, significant dynamic variations were observed at different time points (weeks 1–4). Conclusions: Our study indicates lymphopenia to be associated with disease severity and suggests the plausible role of lymphocyte subsets in disease progression, which in turn affects prognosis and recovery duration in patients with severe COVID-19 and influenza A.

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Section: Discussionmentioning

confidence: 99%

Specific Dynamic Variations in the Peripheral Blood Lymphocyte Subsets in COVID-19 and Severe Influenza a Patients: A Retrospective Observational Study

Qian

Gao

Song

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Another factor that could have contributed to lymphopenia in our patient is the influenza virus infection. However, grade 4 lymphopenia such as the one experienced by our patient is rare in otherwise immunocompetent patients with influenza virus infection [ 8 , 9 ].…”

Section: Discussionmentioning

confidence: 99%

Severe lymphopenia after subcutaneous cladribine in a patient with multiple sclerosis: To vaccinate or not?

et al. 2020

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Objective To describe a fatal case of influenza A pneumonia in a patient with severe lymphopenia after receiving subcutaneous cladribine to treat her multiple sclerosis (MS). Methods Case report. Results A 53-year-old woman developed fatal influenza pneumonia associated with grade 4 lymphopenia two months after receiving a total dose of 60mg subcutaneous cladribine. Despite treatment with oseltamivir, her condition deteriorated and the patient passed away after developing respiratory failure. Conclusion Cladribine-related lymphopenia is usually mild to moderate, however severe lymphopenia may occur. People with MS, especially those who are immunosuppressed, should be offered the inactivated influenza vaccine annually.

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“…For our machine learning classification task to discriminate COVID-19 patients from influenza patients, we used clinical variables for 21 influenza patients from a study by Cheng et al and 1050 patients from the Influenza Research Database [9,10]. Only H1N1 Influenza A virus cases were included because of difficulties locating data from other strains.…”

Section: Literature Search and Inclusion Criteria For Studiesmentioning

confidence: 99%

“…Because it can be difficult to distinguish influenza from COVID-19, we downloaded clinical data collected for influenza from a study by Cheng et al and from the Influenza Research Database [9,10]. Machine learning was then used to perform a classification task to discriminate between influenza and COVID-19.…”

Section: Creation Of a Diagnostic Model For Covid-19 Based On Clinicamentioning

confidence: 99%

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis

Shende

et al. 2020

BMC Med Inform Decis Mak

130

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Background The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. Methods In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. Results We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. Conclusions We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

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Dynamic changes of lymphocyte counts in adult patients with severe pandemic H1N1 influenza A

Cited by 48 publications

References 31 publications

Specific Dynamic Variations in the Peripheral Blood Lymphocyte Subsets in COVID-19 and Severe Influenza a Patients: A Retrospective Observational Study

Specific Dynamic Variations in the Peripheral Blood Lymphocyte Subsets in COVID-19 and Severe Influenza a Patients: A Retrospective Observational Study

Severe lymphopenia after subcutaneous cladribine in a patient with multiple sclerosis: To vaccinate or not?

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis

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