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The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study
Yufen Zheng1, Ying Zhang1, Hongbo Chi1
1Department of Clinical Laboratory, Taizhou Hospital, Wenzhou Medical University, Linhai, P.R. China.
Insights
This study identified neutrophil, lymphocyte, and platelet counts as key predictors of severe COVID-19 progression. The developed NLP score aids in early risk stratification and management of coronavirus disease 2019 patients.
Area of Science:
- Hematology
- Infectious Diseases
- Clinical Medicine
Background:
- The COVID-19 pandemic caused by SARS-CoV-2 has led to a global health crisis.
- Limited information exists on early predictors for severe COVID-19.
- Identifying risk factors is crucial for timely intervention and patient management.
Purpose of the Study:
- To investigate hemocyte count differences between severe and non-severe COVID-19 cases.
- To identify early risk factors for COVID-19 disease progression.
- To develop a predictive tool for assessing COVID-19 severity.
Main Methods:
- Retrospective cohort study of 141 COVID-19 patients.
- Analysis of clinical characteristics and hemocyte counts (white blood cells, neutrophils, lymphocytes, platelets).
- Multivariate Cox regression and nomogram construction for predictive accuracy assessment.
Main Results:
- Lymphopenia was prevalent in severe COVID-19 cases.
- Neutrophil count (HR=4.441), lymphocyte count (HR=0.255), and platelet count (HR=0.244) were independent predictors of progression.
- A nomogram incorporating these factors demonstrated high predictive accuracy (C-index=0.821).
Conclusions:
- Neutrophil, lymphocyte, and platelet (NLP) counts can predict COVID-19 progression.
- The developed NLP score offers a simple, clinically useful tool for risk stratification.
- This tool can facilitate improved management strategies for COVID-19 patients.
Abstract:
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.
