The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study
| Autor: | Ying Zhang, Jun Li, Hongbo Chi, Linping Chen, Minfei Peng, Donglian Wang, Yufen Zheng, Lifei Luo, Shiyong Chen, Bo Shen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male 0301 basic medicine China medicine.medical_specialty Hemocytes Neutrophils Pneumonia Viral Clinical Biochemistry Betacoronavirus Leukocyte Count 03 medical and health sciences 0302 clinical medicine Risk Factors White blood cell Internal medicine Humans Medicine Lymphocyte Count Pandemics Retrospective Studies Leukopenia Platelet Count SARS-CoV-2 business.industry Proportional hazards model Biochemistry (medical) Hazard ratio COVID-19 Retrospective cohort study General Medicine Middle Aged Nomogram Prognosis Coronavirus 030104 developmental biology medicine.anatomical_structure 030220 oncology & carcinogenesis Cohort Disease Progression Absolute neutrophil count Female medicine.symptom Coronavirus Infections business Biomarkers |
| Zdroj: | Clinical Chemistry and Laboratory Medicine (CCLM). 58:1106-1115 |
| ISSN: | 1437-4331 1434-6621 |
| DOI: | 10.1515/cclm-2020-0377 |
| Popis: | Objectives In December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19. Methods In this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy. Results The data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954–10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097–0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111–0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746–0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients. Conclusions We designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management. |
| Databáze: | OpenAIRE |
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