key: cord-0837559-3x49j21p
authors: Shi, Shaorui; Nie, Bin; Chen, Xinzu; Cai, Qiang; Lin, Chunxin; Zhao, Guangda; Zhang, Xingying
title: Clinical and laboratory characteristics of severe and non‐severe patients with COVID‐19: A retrospective cohort study in China
date: 2021-01-02
journal: J Clin Lab Anal
DOI: 10.1002/jcla.23692
sha: e9f26ecb749541fa7b976d5a52d9520a13c37f0e
doc_id: 837559
cord_uid: 3x49j21p

BACKGROUND: Patients diagnosed with the novel coronavirus disease (COVID‐19) who develop severe symptoms need to be determined in advance so that appropriate treatment strategies are in place. METHODS: To determine the clinic features of patients diagnosed definitely with COVID‐19 and evaluate risk factors for severe outcome, the medical records of hospitalized patients were reviewed retrospectively by us and data were compiled. Laboratory data from 90 cases were analyzed, and COVID‐19 patients were classified into two groups (severe and non‐severe) based on the severity. RESULTS: Severe COVID‐19 cases on admission had higher leukocyte and neutrophil counts, neutrophil‐to‐lymphocyte ratio (NLR), D‐dimer, fibrinogen, C‐reactive protein levels, and lower lymphocyte counts compared with those of non‐severe cases (p < 0.05). The area under the curve (AUC) for leukocyte counts, neutrophil counts, and levels of C‐reactive protein was 0.778, 0.831, and 0.800, respectively. The thresholds were 7.70 × 10(9)/L for leukocyte counts, 5.93 × 10⁹/L for neutrophil counts, and 75.07 mg/L for C‐reactive protein, respectively. Logistic regression analyses indicated that higher white blood cell (WBC) counts (OR, 1.34; 95% CI, 1.05–1.71), neutrophil counts (OR, 1.35; 95% CI, 1.06–1.73), and C‐reactive protein levels (OR, 1.02; 95% CI, 1.0–1.04) were several predictive factors for severe outcome. Severe COVID‐19 patients had a reduction in WBC counts, D‐dimer, C‐reactive protein, and fibrinogen upon discharge from hospital, while lymphocyte counts increased (p < 0.05). CONCLUSION: Counts of WBC, neutrophil, and lymphocyte, NLR, and levels of C‐reactive protein, D‐dimer, and fibrinogen are helpful for prediction of the deterioration trend in patients diagnosed with COVID‐19.

The novel coronavirus disease 2019 (COVID-19) since its outbreak in December 2019 has caused a pandemic worldwide. More than 19949122 cases were infected with COVID-19 as of August 10, 2020 , around more than 200 countries.

The rapid spread of COVID-19 has significantly impacted human health. [1] [2] [3] [4] To date, reports from the World Health Organization (WHO) have shown that COVID-19 is still spreading rapidly resulting in severely burdening the healthcare systems of several countries.

Countries around the world have paid a heavy price for prevention of the COVID-19 spread and treatment of infected patients. 5 No specific drug has been developed for COVID-19 at present. On August 4, Sarah Gilbert, professor of vaccines at Oxford University, said there were still many uncertainties regarding the successful development of a COVID-19 vaccine and how it could be used on a large scale. Fever, fatigue, cough, and headaches are the common symptoms in COVID- 19 patients, and dyspnea in some severe cases. [6] [7] [8] [9] There are no specific symptoms during the early stages; hence, it is hard to predict whether individuals will develop severe symptoms. Patients who develop severe symptoms need to be determined in advance so that appropriate treatment strategies are in place. Identifying laboratory predictors of severe and fatal type progression is essential. 10 These predictors will be used for risk stratification and treatment guidance of at-risk patients. This is essential for the allocation of limited medical resources to those of greatest need. 10 on laboratory data during the initial stages of the disease derived from univariate analysis. This study compared predictors between COVID-19 patients with severe and non-severe symptoms on admission and those with severe COVID-19 before and after treatment. Several predictive factors for severe outcomes were evaluated by logistic regression analyses. The optimal threshold for these predictors was identified by ROC curves. In the present study, we aimed to determine the clinic features of definitely diagnosed COVID-19 patients and evaluate risk factors for severe outcomes.

The medical records were retrospectively reviewed, and hospitalization patient data were compiled from two clinical centers, the Wuhan Red Cross Hospital and the Second People's Hospital of Yibin, West China Yibin Hospital, Sichuan University. All enrolled COVID-19 inpatients in this study were confirmed with a positive result tested by high-throughput sequencing or real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay of nasal and pharyngeal swab samples, anal swab samples, sputum samples, and stool samples. As of March 17, 2020, we collected 411 cases diagnosed definitely with COVID-19 in our study. Ninety cases were included in this study, while the remaining cases were excluded due to incomplete medical records. COVID-19 was diagnosed based on the 6th edition of guidelines released by the National Health Commission of China (in Chinese). COVID-19 patients were classified into two groups (severe and non-severe) based on the severity. Severe COVID 19 was diagnosed with at least one of the following conditions: (a) arterial oxygen saturation ≤93% at rest, (b) shortness of breath with respiratory rate (RR) ≥30 times/min, or (c) PaO 2 /FiO 2 ≤300 mmHg. Non-severe patients presented fever, symptoms of respiratory tract, and others with imaging sign of pneumonia. 12 The approval to conduct the study was voted by the Ethics Committee of Wuhan Red Cross Hospital and the Ethics Committee of the Second People's Hospital of Yibin, West China Yibin Hospital, Sichuan University. Considering the urgency to perform this study, waiver of written informed consent was conducted.

Clinical, epidemiological, and demographic information and laboratory test results including biochemical indices, blood routine results, and coagulation test were reviewed by accessing the medical records of COVID 19 inpatients on admission. Final laboratory test results before discharge or death were also collected. Clinical outcomes were followed until discharge or death.

Normally or non-normally distributed continuous data were presented as mean ± SD and median (interquartile range), respectively. Numbers (percentages) were used for categorical variable presentation. Comparison between groups of non-normally or normally distributed data was conducted by the Mann-Whitney U test or the Student t test. A paired t test or Wilcoxon's test was conducted for comparison of laboratory data on admission and before discharge or death based on whether the data were normally distributed.

GraphPad Prism software version 8 was used to generate forest maps representing logistic regression analysis. Categorical data were analyzed by a chi-square test. The dependent variable of the disease severity and the association between abnormal laboratory findings were analyzed by logistic regression analyses. All statistical analyses were carried out with SPSS statistical software, version 26 .0 (IBM Corp.). p < 0.05 was considered of statistical significance.

Ninety COVID-19 patients were included in the present study for analyses. COVID-19 patients were classified into the severe group (35 cases) and the non-severe group (55 cases) to evaluate the characteristics of severe conditions. The average age of patients including 22 males and 13 females in the severe group was 66.00 ± 14.32 years, while the average age of patients in the nonsevere group (26 males and 29 females) was 49.13 ± 16.85 years. The mean age was significantly lower in non-severe patients than severe patients (p < 0.001) ( Table 1) . Severe patients had more chance to present with fever, fatigue, asthma, dyspnea, chest distress, and loss of appetite during the early stages (p < 0.05).

Compared with non-severe patients, the white blood cell counts of severe patients were higher (8.25 vs. 5.29 × 10⁹/L) (p < 0.001).

Neutrophil counts were higher in severe patients compared with those of non-severe patients (7.54 vs. 3.25 × 10⁹/L) (p < 0.001).

However, lymphocyte counts of severe patients were lower compared with those of non-severe patients (0.74 vs. 1.29 × 10⁹/L) (p < 0.001). C-reactive protein was significantly higher in severe patients than non-severe patients (86.41 vs. 6.24 mg/L) (p < 0.001).

D-dimer (1.80 vs. 0.63 mg/L, p < 0.001) and fibrinogen (3.89 vs.

2.78 g/L, p < 0.001) in severe patients were higher compared with those in non-severe patients. Neutrophil-to-lymphocyte ratio (NLR) in severe group was significantly higher than that in non-severe group (9.95 vs. 2.24) (p < 0.001). No difference in red blood cell counts, hemoglobin, platelet counts, partial thromboplastin time, and prothrombin time was found between patients with different severity(p > 0.05) ( Table 2) .

ROC curves with a single predictor showed that WBC and neutrophil counts, and levels of C-reactive protein in COVID-19 patients were valuable predictors for severe conditions. The area under the curve (AUC) was 0.778, 0.831, and 0.800, respectively, for these predictors. The thresholds were 7.70 × 10⁹/L for WBC counts, 5.93 × 10⁹/L for neutrophil counts, and 75.07 mg/L for C-reactive protein ( Figure 1 ). 

Comparing laboratory tests on admission and before discharge, only 19 discharged patients were included in the severe group due to patient death, and 36 cases were included in the non-severe group due to incomplete data. The white blood cell counts of severe patients before discharge were lower compared with those on admission (5.96 vs. 7.37 × 10⁹/L) (p = 0.040). Lymphocyte counts of severe patients were higher before discharge than on admission (1.38 vs. 0.75 × 10⁹/L) (p = 0.020). Neutrophil counts before discharge were lower than on admission in the severe group (3.94 vs. 5.98 × 10⁹/L) (p = 0.080); however, the difference was not statistically different.

C-reactive protein of severe patients before discharge was significantly lower than that on admission (3.26 vs. 22.38 mg/L) (p < 0.001).

However, the differences in thrombin time, fibrinogen, and D-dimer before discharge and on admission were not statistically significant in severe patients (p > 0.05). The differences in counts of WBC, neutrophil, and lymphocyte, levels of C-reactive protein, fibrinogen, and D-dimer, and thrombin time of non-severe patients before discharge and on admission were not statistically significant (p > 0.05) ( Table 3 ).

Sixteen of the 35 severe patients died shortly after admission.

Several of the laboratory indicators were not re-tested due to patient death. The WBC counts of severe non-survivors were significantly higher prior to death than that on admission ( 

Since the outbreak of COVID-19, the healthcare system, especially in countries with severe outbreaks, has been exhausted. 13 It is particularly important to manage patients with different symptoms and to identify patients who may progress to severe symptoms.

Our study determined the clinic features of definitely diagnosed COVID-19 patients and evaluated risk factors for severe outcomes.

Severe COVID-19 symptoms increased with age. [14] [15] [16] [17] [18] [19] [20] [21] Our study demonstrated that the age was significantly higher in patients with severe symptoms compared to those with non-severe symptoms.

This may be because older patients have lower immunity and are more likely to have underlying diseases. Hence, more intensive surveillance is needed when treating elderly patients. Severe patients had more chances to have a fever, fatigue, asthma, dyspnea, chest distress, and loss of appetite at an early stage. We demonstrated that the counts of white blood cell and neutrophil in severe patients were higher compared to patients with non-severe symptoms. Previous studies have reached similar conclusions. [22] [23] [24] This study also demonstrated that the counts of white blood cell In the present study, we found that lymphocyte counts were significantly lower in severe patients on admission compared with those in non-severe patients and were higher before discharge than on admission. Our study also showed that lymphocyte counts remained reduced in COVID-19 patients. Hence, lymphocyte depletion could be a good indicator of disease deterioration. 25

The receiver operating characteristic (ROC) curve of white blood cell counts, neutrophil counts and C-reactive protein.

Receiver operating characteristic curve analysis suggested that WBC counts, neutrophils counts and C-reactive protein could be used to assist the prediction of COVID-19 severity on admission In addition, we demonstrated that C-reactive protein was significantly higher in severe COVID-19 patients compared with that in non-severe patients and was significantly lower when they were discharged from the hospital after treatment than on admission. We Elevated C-reactive protein may be served as a better marker for COVID-19 progression.

The NLR has been recently identified as an inflammatory biomarker and a reliable predictor of different acute medical conditions, including not only infections but also cerebral hemorrhage and tumor. [27] [28] [29] [30] In this study, the univariate analysis of NLR in severe group and non-severe group was carried out. It was found that NLR in severe group was higher than that in non-severe group. NLR was a simple, inexpensive, and easily available composite index. Several limitations were existed in the current study, which may have resulted in some potential bias. First, the study was retrospective, and only two centers and a small patient cohort were analyzed.

Second, there were missing data due to patient deaths and incomplete data for some patients.

In conclusion, counts of WBC, neutrophil, and lymphocyte, neutrophil-to-lymphocyte ratio, and levels of D-dimer, C-reactive protein, and fibrinogen are helpful to predict the deterioration trend of COVID-19 patients. Close attention needs to be paid to patients with decreased lymphocyte counts, elevated C-reactive protein levels, WBC counts, and neutrophil counts during the initial stages of the disease. 

None to declare.

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

https://orcid.org/0000-0001-9827-7427

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