Hematological marker for early diagnosis of novel coronavirus infection in COVID-19 virus infected person

Abstract

The invention relates to a hematological marker for early diagnosis of novel coronavirus infection in a COVID-19 virus infected person. The invention discloses ROC analysis results of RAR, CRP, LMR, CLR, NAR, CAR and RDW-CV in evaluation of novel coronavirus infection diseases. Wherein the maximum AUC value of the RAR is 0.769, and the minimum AUC value of the RDW-CV is 0.488; the AUC values of CRP, LMR, CLR, NAR and CAR are respectively 0.603, 0.556, 0.591, 0.538 and 0.631, and the AUC values of CRP, LMR, CLR, NAR and CAR are respectively 0.603, 0.556, in addition, the critical value of the RAR is 0.154, and under the critical value, the sensitivity of the RAR is 76.8%, the specificity is 65.3%, and the negative predictive value NPV is 91.7%.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to a hematological marker for early diagnosis of novel coronavirus infection in COVID-19 virus-infected patients. Background Art

[0002] During the COVID-19 pandemic, laboratory and instrumental examinations that can be used to predict clinical damage and prognosis are crucial. Traditional diagnostic methods for novel coronavirus infection (such as chest X-ray or CT scan) can detect structural damage in the lungs, but cannot quickly reflect the early inflammatory or immune response in patients, and the examination cost is high, requiring relatively expensive imaging equipment and professional technicians for operation, which is not conducive to the implementation in primary medical units. In addition, the above examinations also have the defects of radiation and lack of simplicity and speed. Compared with traditional imaging examinations, hematological indicators have the advantages of low cost, simplicity, speed, and easy acquisition, so they have become an important tool for early diagnosis and prognosis evaluation of COVID-19. Through hematological indicators, doctors can monitor the changes of the disease in real time and adjust the treatment strategy in a timely manner according to the immune status or inflammatory response of the patients. Among them, inflammatory markers play an important role in predicting disease progression. As a common inflammatory marker, C-reactive protein (CRP) has been widely used in the clinical evaluation of COVID-19. Among many inflammatory markers, the ratio of red blood cell distribution width to albumin (RAR) has gradually attracted attention as a promising indicator of inflammation and disease severity.

[0003] Red blood cell distribution width (RDW) is a simple and easily detectable hematological indicator, which reflects the degree of variation in red blood cell size. Increased RDW is closely related to elevated levels of cytokines, indicating that RDW may be used as a marker of inflammation. Previous studies have found that RDW is closely related to systemic inflammation and poor outcomes of various chronic diseases, including heart failure, stroke, and cancer, etc. Albumin (ALB) is a recognized marker reflecting nutritional status and liver function. Hypoalbuminemia is common in inflammatory states, and the ratio of RDW to ALB, RAR, provides a unique perspective that can reflect both inflammation and nutritional status.

[0004] So far, RAR has not been used for the early diagnosis of novel coronavirus infection in COVID-19-infected patients. Summary of the Invention

[0005] The present invention provides an application of a hematological marker in the preparation of an early diagnosis marker for novel coronavirus infection, and the hematological marker is selected from one or more of RAR, CRP, LMR, CLR, CAR, and ALB.

[0006] The present invention provides an application of a hematological marker in the preparation of an early diagnosis marker for novel coronavirus infection in COVID-19 virus-infected patients.

[0007] The present invention provides an application of a hematological marker in the preparation of an early risk stratification and / or prognosis prediction marker for novel coronavirus infection.

[0008] The levels of RAR, CRP, LMR, CLR, and CAR in patients with novel coronavirus infection are higher than those in the non-pneumonia group.

[0009] The level of ALB in patients with novel coronavirus infection is lower than that in the non-pneumonia group.

[0010] Preferably, the hematological marker is RAR.

[0011] The present invention provides an application of a reagent for detecting a hematological marker in the preparation of an early diagnosis kit for novel coronavirus infection, and the hematological marker includes one or more of RAR, CRP, LMR, CLR, CAR, and ALB.

[0012] The present invention provides an application of a reagent for detecting a hematological marker in the preparation of an early risk stratification and / or prognosis prediction kit for novel coronavirus infection, and the hematological marker includes one or more of RAR, CRP, LMR, CLR, CAR, and ALB.

[0013] Preferably, the hematological marker is RAR.

[0014] The present invention collected the clinical data of COVID-19 infected patients since the outbreak of the Shanghai Omicron BA.2 epidemic from April to July 2023, and analysis found that RAR is the most effective serological marker for the early diagnosis of novel coronavirus infection, superior to commonly used inflammatory markers such as blood routine and CRP. The findings of the present invention highlight the potential of RAR as a simple, rapid, cost-effective, and reliable tool for early risk stratification and prognosis prediction in COVID-19 infected patients. This will enable clinicians to better identify individuals at higher risk of novel coronavirus infection, thereby enabling early intervention and ultimately improving the prognosis of patients.

[0015] Beneficial effects

[0016] The present invention first proposes that RAR can be used as a novel hematological marker for predicting the risk of novel coronavirus infection in COVID-19 virus-infected patients.

[0017] The ROC analysis results of RAR, CRP, LMR, CLR, NAR, CAR, and RDW-CV in the evaluation of the disease of novel coronavirus infection are shown in the present invention. According to the analysis, the AUC value of RAR is the highest (0.769), while the AUC value of RDW-CV is the lowest (0.488); the AUC values of CRP, LMR, CLR, NAR, and CAR are 0.603, 0.556, 0.591, 0.538, and 0.631 respectively. In addition, the critical value of RAR is 0.154. At this value, the sensitivity of RAR is 76.8%, the specificity is 65.3%, and the negative predictive value (NPV) is 91.7%. Brief Description of the Drawings

[0018] Figure 1 It is the stratified analysis of RAR and the risk of novel coronavirus infection;

[0019] Figure 2 It is the restricted cubic spline (RCS) analysis of RAR and the risk of novel coronavirus infection; among which the odds ratio (OR) of the restricted cubic curve of RAR; OR (red solid line) and 95% confidence interval (pink shaded area) have adjusted for age and gender factors;

[0020] Figure 3 It is the gender-stratified analysis of the restricted cubic spline (RCS) of RAR and the risk of novel coronavirus infection; the odds ratio (OR) of the restricted cubic spline curve of RAR; OR (red solid line for females and blue solid line for males) and 95% confidence interval (red shaded area for females and blue shaded area for males) have adjusted for age factor;

[0021] Figure 4 It is the receiver operating characteristic (ROC) curve for predicting the risk of novel coronavirus infection based on different serological inflammation indicators. Detailed Description of the Invention

[0022] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0023] Example 1

[0024] I. Case Collection and Research Design

[0025] This study adopted a retrospective cohort study design. Patients diagnosed with COVID-19 in Tongji Hospital of Shanghai from April 1, 2023 to July 31, 2023 were selected as the research subjects. All patients were confirmed of their conditions through standard clinical diagnostic methods, and detailed clinical data and laboratory test results were recorded. The inclusion criteria included: age ≥ 18 years old, diagnosed with COVID-19, and their clinical and laboratory data could be obtained through the hospital's data management system.

[0026] The exclusion criteria were as follows: patients < 18 years old, pregnant or lactating, suffering from acute or chronic hematological diseases, liver and kidney failure, long-term alcoholism or drug abuse, previously diagnosed with cancer, suffering from immune diseases or immunodeficiency diseases, having had serious surgeries or traumas recently, and those whose complete data could not be obtained through the hospital's data management system. Finally, a total of 2,205 patients diagnosed with COVID-19 virus infection were included in the study.

[0027] Diagnostic criteria for COVID-19 infection: (1) With or without relevant clinical manifestations of COVID-19 virus infection. (2) Having one or more of the following etiological and serological test results: ① Positive nucleic acid test for COVID-19 virus; ② Positive antigen test for COVID-19 virus; ③ Positive isolation and culture of COVID-19 virus; ④ The level of specific IgG antibody against COVID-19 virus in the convalescent period increased by 4 times or more compared with the acute phase.

[0028] Diagnostic criteria for novel coronavirus infection: (1) Clearly diagnosed with COVID-19 infection; (2) Having relevant clinical manifestations of novel coronavirus infection such as fever and / or respiratory symptoms; (3) Chest CT examination indicating imaging features of novel coronavirus infection.

[0029] II. Data collection

[0030] The age, gender of the patients, and laboratory test results were collected from the outpatient and inpatient medical records of the patients: including white blood cell count (WBC), neutrophils, lymphocytes, monocytes, platelet count (PLT), red blood cell distribution width - coefficient of variation (RDW-CV), hemoglobin (HGB), C-reactive protein (CRP), albumin (ALB), COVID-19 nucleic acid results, and chest spiral CT reports.

[0031] The following multiple derived inflammatory indices were calculated, including lymphocyte to monocyte ratio (LMR), neutrophil to platelet ratio (NPR), platelet to albumin ratio (PAR), C-reactive protein to albumin ratio (CAR), C-reactive protein to lymphocyte ratio (CLR), and red blood cell distribution width - coefficient of variation to albumin ratio (RAR). The calculation methods for each index were as follows:

[0032] 1. LMR calculation method: Lymphocyte count (10^9 / L) divided by monocyte count (10^9 / L).

[0033] 2. NPR calculation method: Neutrophil count (10^9 / L) multiplied by 1000, and then divided by platelet count (10^9 / L).

[0034] 3. PAR calculation method: Platelet count (10^9 / L) divided by albumin level (g / L).

[0035] 4. CAR calculation method: C-reactive protein level (mg / L) divided by albumin level (g / L).

[0036] 5. CLR calculation method: C-reactive protein level (mg / L) divided by lymphocyte count (10^9 / L).

[0037] 6. RAR calculation method: Red cell distribution width - coefficient of variation (%) divided by albumin level (g / dL).

[0038] III. Statistical analysis

[0039] All statistical analyses were performed using Python (version 3.x) and SPSS (version 27) software. Categorical data were presented as proportions and frequencies, and the chi-square test was used to compare categorical data. Numerical data were tested for normal distribution by visual methods (histogram) and analytical methods (Kolmogorov-Smirnov test / Shapiro-Wilk test). Normally distributed data were expressed as mean ± standard deviation; non-normally distributed data were expressed as median and interquartile range (IQR). The t-test was used for comparison between two groups of normally distributed data; the Mann–Whitney U test was used for comparison between groups of non-normally distributed data. Binary logister regression analysis was used to evaluate the risk factors for COVID-19 infection in COVID-19 infected patients. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of a series of novel inflammation-derived markers such as RDW-CV, RAR, LMR, CLR, NAR, CRP, CAR, etc. for COVID-19 infection. Restricted cubic spline (RCS) regression was used to visualize the non-linear relationship between RAR and COVID-19 infection. All relevant variables were standardized using z-score. A P value < 0.05 was considered statistically significant.

[0040] Effect:

[0041] Among the total 2,205 COVID-19 infected patients, 444 patients had novel coronavirus infection (pneumonia group), with a median age of 65 years; 1,761 patients had no pneumonia (non-pneumonia group), with a median age of 44 years. Among all patients, 1,253 (56.8%) were female and 952 (43.2%) were male. The comparison between the pneumonia group and the non-pneumonia group in demographics and laboratory results is shown in Table 1. It was found that the median age of patients in the pneumonia group was significantly higher (p<0.001), while there was no significant difference in terms of gender (p = 0.400). CRP, LMR, CLR, CAR, and RAR were all significantly higher in the pneumonia group than in the non-pneumonia group (p<0.001), and NPR was also higher in the pneumonia group than in the non-pneumonia group (p = 0.029). In addition, the ALB level in the pneumonia group was significantly lower than that in the non-pneumonia group (p<0.001). As shown in Table 2, univariate logister regression analysis showed that high CRP, LMR, CLR, CAR, RAR, age, and low ALB were significantly associated with novel coronavirus infection. In the subgroups stratified by age and gender, the correlation between RAR and the risk of novel coronavirus infection remained consistent. This indicates that stratification by age and gender did not affect the diagnostic value of RAR for the risk of novel coronavirus infection (as Figure 1 shown).

[0042] Multivariate-adjusted RCS analysis showed that RAR was closely associated with the risk of novel coronavirus infection (p<0.001), and showed a significant non-linear dose-response relationship (non-linear p<0.001) (as Figure 2 shown). In addition, a gender-stratified analysis of the non-linear relationship between RAR and the risk of novel coronavirus infection was also performed, and the results also indicated a significant non-linear dose-response relationship (overall p<0.001, non-linear p<0.001) (as Figure 3 shown).

[0043] Table 3 and Figure 4 showed the ROC analysis results of RAR, CRP, LMR, CLR, NAR, CAR, and RDW-CV in evaluating the risk of novel coronavirus infection. According to the analysis, the AUC value of RAR was the highest (0.769), while the AUC value of RDW-CV was the lowest (0.488); the AUC values of CRP, LMR, CLR, NAR, and CAR were 0.603, 0.556, 0.591, 0.538, and 0.631, respectively. In addition, the cut-off value of RAR was 0.154, at which the sensitivity of RAR was 76.8%, the specificity was 65.3%, and the negative predictive value (NPV) was 91.7%.

[0044] In summary, through the above data analysis, the results suggest that RAR may be a hematological marker for the early diagnosis of novel coronavirus infection in COVID-19 patients.

[0045] Table 1. Comparison of demographic characteristics and laboratory test results between COVID-19 patients with pneumonia and those without pneumonia

[0046]

[0047] Abbreviations: HGB, hemoglobin; RDW-CV, red blood cell distribution width - coefficient of variation; CRP, C-reactive protein; LMR, lymphocyte to monocyte ratio; NPR, neutrophil to platelet ratio; CLR, C-reactive protein to lymphocyte ratio; ALB, albumin; PAR, platelet to albumin ratio; CAR, C-reactive protein to albumin ratio; RAR, red blood cell distribution width - coefficient of variation to albumin ratio.

[0048] Table 2. Univariate logistic regression analysis of the risk of novel coronavirus infection

[0049]

[0050]

[0051] Table 3. Receiver operating characteristic (ROC) analysis for predicting the risk of novel coronavirus infection

[0052]

Claims

1. Use of a hematological marker in the preparation of an early diagnostic marker for novel coronavirus infection, characterized in that, The hematological markers are selected from one or more of RAR, CRP, LMR, CLR, CAR, and ALB.

2. Use of a hematological marker in the preparation of an early diagnostic marker for novel coronavirus infection in COVID-19 virus-infected patients.

3. Use of a hematological marker in the preparation of a marker for early risk stratification and / or prognosis prediction of novel coronavirus infection.

4. The application according to any one of claims 1 to 3, characterized in that, The levels of RAR, CRP, LMR, CLR, and CAR in patients with novel coronavirus infection are higher than those in the non-pneumonia group.

5. The application according to any one of claims 1 to 3, characterized in that, The level of ALB in patients with novel coronavirus infection is lower than that in the non-pneumonia group.

6. The application according to any one of claims 1 to 3, characterized in that The hematological marker is RAR.

7. Use of a reagent for detecting a hematological marker in the preparation of a kit for early diagnosis of novel coronavirus infection, characterized in that, The hematological markers include one or more of RAR, CRP, LMR, CLR, CAR, and ALB.

8. Use of a reagent for detecting a hematological marker in the preparation of a reagent kit for early risk stratification and / or prognosis prediction of coronavirus disease 2019 (COVID-19) infection, characterized in that, The hematological markers include one or more of RAR, CRP, LMR, CLR, CAR, and ALB.

9. The application according to any one of claims 7-8, characterized in that, The hematological marker is RAR.

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