Evaluation of effectiveness of NAC on curative effect of COVID-19

Abstract

The invention relates to the technical field of medical evaluation, and particularly discloses a method and a model for evaluating the effectiveness of the curative effect of NAC on COVID-19, and the model can simply and effectively evaluate the effectiveness of the curative effect of NAC on COVID-19, and can be used for clinical guidance.

Description

Technical Field

[0001] The present application relates to the field of medical evaluation technology, and specifically to an evaluation method and evaluation model for the effectiveness of NAC in treating COVID-19. Technical Background

[0002] COVID-19 caused by SARS-CoV-2 infection. Studies have shown that excessive immune activation and cytokine storm are the causes of COVID-19 lung injury. It is currently believed that long-term oxidative stress, increased production of reactive oxygen species (ROS) and decreased glutathione levels in inflammation lead to an imbalance in redox homeostasis, which in turn leads to excessive immune activation and cytokine storm. NAC is an N-acetyl derivative of the natural amino acid L-cysteine. It acts as a direct scavenger of ROS to regulate redox status, regulate inflammatory responses and exhibit indirect antioxidant properties. It has been confirmed by in vitro and in vivo studies and is widely used in clinical practice, such as rescuing liver poisoning caused by excessive acetaminophen and chronic obstructive pulmonary disease. The NAC molecule has an active free -SH group, which can promote the breakage of disulfide bonds (-SS) of acidic glycoprotein polypeptides in sputum, and can directly split and hydrolyze DNA molecular chains and mucins in sputum, thereby decomposing respiratory mucus and keeping the respiratory tract moist to reduce irritation. Based on the above theory, it is believed that NAC can play an important role in the treatment of COVID-19.

[0003] The St. George's Respiratory Questionnaire (SGRQ) is a patient-reported questionnaire that measures the impact of respiratory diseases and their treatments on patients' health-related quality of life (HRQoL). It has been used in patients with asthma, chronic obstructive pulmonary disease, bronchiectasis, interstitial lung disease, and lung transplantation. The total score of the SGRQ ranges from 0 to 100, with lower scores indicating better HRQoL.

[0004] The US FDA defines patient-reported outcomes (PROs) as reports directly from patients’ health status, without intervention from clinicians or others’ responses to patients. PROs cover patients’ subjective and objective feelings, can comprehensively identify and measure patients’ health status before and after diagnosis and treatment, and are part of treatment outcomes that is independent of survival and disease physiological indicators. PROs are of great significance to patients, reflecting patients’ feelings, experiences, and interpretations in disease scenarios and treatment experiences, and providing information about patients’ health status, the extent of symptoms or adverse reactions to treatment, the interference of symptoms with normal life, the ability to comply with treatment and reasons for poor compliance, and difficulties in personal and family life. PROs are the only reports that can reflect the impact of a patient’s disease. They help obtain patients’ permission for treatment, and measure the efficiency of treatment by establishing beneficial communication between patients and health care providers. They also help interpret clinical outcomes and make treatment decisions. PROs have been widely used in a variety of diseases.

[0005] In view of this, this application is proposed. Summary of the invention

[0006] In order to solve the above technical problems, this application conducted a multicenter observational study. The main purpose of this study was to evaluate the effectiveness of NAC for COVID-19 and observe its effect on patient-reported outcomes. A total of 63 confirmed COVID-19 patients admitted to five designated COVID-19 hospitals, including the Fourth People's Hospital of Taiyuan City, from the end of January to the end of March 2020 were selected and divided into a NAC treatment group and a non-NAC treatment group. Patients in the non-NAC treatment group received conventional treatment, and patients in the NAC treatment group received NAC treatment on the basis of conventional treatment. The NAC treatment group continued to take NAC orally after discharge. All patients were followed up at discharge and 1 month, 3 months, and 6 months after discharge. The clinical treatment effects of the two groups of patients were compared, and the St. George's Respiratory Questionnaire (SGRQ) was used to evaluate patient-reported outcomes (PRO). The results showed that there was a strong correlation between the scores of each part of the SGRQ questionnaire for all patients and between the scores of each part and the total score. In the univariate analysis, the difference in the change of disease impact score and SGRQ total score between the NAC treatment group and the non-NAC treatment group one month after discharge was statistically significant, and the difference in the change of activity limitation score, disease impact score and SGRQ total score three months (or six months) after discharge was statistically significant. In the multivariate analysis, NAC treatment, disease severity and follow-up time were all influencing factors of SGRQ total score, among which the interaction term between NAC treatment and follow-up time was statistically significant (β=-0.436, P=0.004). Therefore, the total SGRQ score of COVID-19 patients who received NAC treatment decreased faster than those who did not receive NAC treatment.

[0007] Based on this research result, this application specifically proposes the following technical solutions:

[0008] This application first provides a method for evaluating the effectiveness of NAC for COVID-19, the method comprising the following steps:

[0009] 1. Data collection: used to collect relevant information of COVID-19 patients;

[0010] The inclusion criteria for the patients were: (a) age > 18 years; (b) confirmed positive for SARS-CoV2 by real-time reverse transcription polymerase chain reaction test of nasal swab or lower respiratory tract specimens, and pneumonia confirmed by chest CT; the patients were divided into NAC treatment group and NAC non-treatment group;

[0011] The collection specifically includes: collecting demographic data, clinical symptoms and signs, comorbidities, laboratory tests, chest imaging data and treatment plans of patients in the NAC treatment group and the NAC non-treatment group at the time of admission; collecting clinical symptoms and signs, laboratory tests, chest imaging data of patients in the NAC treatment group and the NAC non-treatment group at the time of discharge, and collecting baseline SGRQ questionnaire information; collecting follow-up data of patients in the NAC treatment group and the NAC non-treatment group 1 month, 3 months, and 6 months after discharge, and collecting SGRQ questionnaire information at each follow-up time point; preferably, the patients in the NAC treatment group continue to take NAC orally after discharge.

[0012] 2. Data statistics and analysis:

[0013] 1) Statistical description and correlation analysis

[0014] a. Adoption To describe the quantitative data, the composition ratio n (%) is used to describe the enumeration data;

[0015] b. All statistical analyses are set to be two-sided tests; preferably, the test level α is 0.05;

[0016] c. The Pearson correlation coefficient was used to describe the correlation between the respiratory symptom score, activity limitation score, disease impact score, and total score of the SGRQ scale, and a statistical test of the correlation coefficient was performed.

[0017] 2) Univariate analysis

[0018] a. Data preprocessing: Calculate the difference between each type of SGRQ score at all follow-up times and the corresponding baseline;

[0019] b. Univariate analysis of NAC treatment and change in SGRQ score:

[0020] According to whether or not NAC treatment was received, the patients were divided into two groups: "NAC treatment" and "no NAC treatment". At each follow-up time dimension, the Mann-Whitney U test was used to analyze whether there were statistical differences in the changes in various SGRQ scores (respiratory symptom score, activity limitation score, disease impact score, and total score) between the two groups.

[0021] c. Univariate analysis of follow-up time and SGRQ score change:

[0022] According to the follow-up time, the patients were divided into three groups: "one month after discharge", "three months after discharge", and "six months after discharge". The Kruskal-Wallis H test was used to comprehensively analyze whether there were statistical differences in the changes in various SGRQ scores (respiratory symptom score, activity limitation score, disease impact score, and total score) among the three groups in different dimensions with or without NAC treatment. The Bonferroni method was used to adjust the test level α, and the differences in the changes in various SGRQ scores between the three groups were analyzed pairwise.

[0023] 3) Multifactor analysis

[0024] a. Data preprocessing: During the modeling process, a new indicator, the unit value of difference (UVD) of the corresponding variable, was constructed based on the minimal clinically important difference (MCID). For the total SGRQ score, the UVD formula is:

[0025]

[0026] Among them, SGRQ i is the total SGRQ score at follow-up time i, MCID SFRQ is the MCID of the total SGRQ score; UVD SGRQ As the dependent variable, it was included in the generalized estimating equation for analysis. Its meaning in the generalized estimating model is: when other variables remain unchanged, the total score of SGRQ increases or decreases by a certain MCID due to a certain factor; when UVD in the model SGRQ When the absolute value of the change is greater than 1, it means that the change in the total SGRQ score has clinical significance;

[0027] The presence or absence of NAC treatment, disease severity, and follow-up time were set as independent variables, and UVD was set as the dependent variable. Generalized estimating equations were used to conduct multivariate analysis of the factors affecting SGRQ scores. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 , Flowchart of the efficacy of NAC on COVID-19 patients;

[0029] Figure 2 , contour plot of marginal mean of SGRQ total score in COVID-19 patients. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in detail below in conjunction with the examples, but it will be appreciated by those skilled in the art that the following examples are only used to illustrate the present application and should not be considered as limiting the scope of the present application. In the examples, if no specific conditions are specified, the conditions are carried out according to normal conditions or manufacturer recommendations. If the manufacturer is not specified for the reagents or instruments used, they are all conventional products that can be purchased from the market.

[0031] Definition of some terms

[0032] Unless otherwise defined below, the meaning of all technical terms and scientific terms used in the specific embodiments of the present application is intended to be the same as those generally understood by those skilled in the art. Although it is believed that the following terms are well understood by those skilled in the art, the following definitions are still set forth to better explain the present application.

[0033] As used in this application, an indefinite or definite article used when referring to a singular noun eg "a" or "an", "the" includes a plural of that noun.

[0034] As used in this application, the terms "comprises", "comprising", "having", "containing" or "involving" are inclusive or open-ended and do not exclude other unrecited elements or method steps. The term "consisting of" is considered a preferred embodiment of the term "comprising". If a group is defined below as comprising at least a certain number of embodiments, this should also be understood to disclose a group that preferably consists of only these embodiments.

[0035] The term "approximately" in this application indicates an accuracy range that can be understood by those skilled in the art and still ensures the technical effect of the characteristic in question. The term generally indicates ±10%, preferably ±5%, of the indicated value.

[0036] In addition, the terms first, second, third, (a), (b), (c), and the like in the specification and claims are used to distinguish similar elements and are not necessarily required to describe a sequential or chronological order. It should be understood that the terms so used are interchangeable under appropriate circumstances, and the embodiments described in this application can be implemented in other sequences than those described or illustrated in this application.

[0037] The above terms or definitions are provided only to help understand the present application. These definitions should not be construed as having a scope less than that understood by those skilled in the art.

[0038] The present application is described below in conjunction with specific embodiments.

[0039] Example 1 Experimental Design

[0040] 1. Design and Data

[0041] This application adopts a prospective observational study design to study the effectiveness of NAC against COVID-19. The flow chart is as follows Figure 1 As shown. In order to improve the accuracy and completeness of this study report, this application uses STROBE to standardize the work and report writing at each stage. Data were collected by researchers in the form of a case report form, which was developed by the Shanxi Key Laboratory of Major Disease Risk Assessment of Shanxi Medical University. Demographic data, clinical symptoms and signs, comorbidities, laboratory tests, chest imaging data and treatment regimens were collected at admission. 32 patients using NAC were included in the NAC treatment group, and the dosage of NAC was 600 mg / time, 2 times / day, orally. 31 COVID-19 patients who did not use NAC were set as the non-NAC treatment group. Clinical symptoms and signs, laboratory tests, chest imaging data and SGRQ were collected when the two groups of patients were discharged. After discharge, the treatment group continued to take NAC orally. Both groups of patients received face-to-face or telephone follow-up at 1 month, 3 months, and 6 months after discharge and filled in the SGRQ. All data were analyzed anonymously.

[0042] 2. Patients

[0043] The study was conducted in five hospitals, including the Taiyuan Fourth People's Hospital, a designated hospital for COVID-19 treatment in Shanxi Province. Patients admitted from February to March 2020 were screened for eligibility and followed up in person or by telephone at discharge and 1, 3, and 6 months after discharge. The number of patients in the region during the study period determined the sample size.

[0044] Inclusion criteria: (a) age > 18 years; (b) confirmed as SARS-CoV2 positive by real-time reverse transcription polymerase chain reaction test of nasal swab or lower respiratory tract specimens and pneumonia confirmed by chest CT; (c) voluntary participation in this study.

[0045] Exclusion criteria: (a) pregnant women and children; (b) unable to obtain informed consent.

[0046] Ethics and informed consent

[0047] This study was approved by the institutional review board of the principal investigator's hospital (Shanxi Medical University Second Hospital, Approval No. (2020)YX(017)) and the institutional review boards of participating hospitals. Informed consent was collected during hospitalization according to the approval of the local ethics committee.

[0048] 3. Quality Control

[0049] COVID-19 patients come from a wide range of sources, are less likely to be recalled after discharge, and have poor compliance with regular follow-up visits, resulting in a very large proportion of missing patient follow-up data. In this case, in order to obtain relevant information on the outcomes reported by COVID-19 patients, the SGRQ questionnaire was used to follow up COVID-19 patients regularly to measure the damage to patients' health caused by COVID-19. At the same time, in order to ensure a high response rate to the questionnaire during regular follow-up visits, free medication was delivered during the follow-up period to improve patient compliance. It has been proven that the above measures have significantly improved patient compliance, with a questionnaire response rate of 100%. All follow-up personnel have been trained.

[0050] Example 2: Data Statistics and Analysis

[0051] 1. Statistical description and correlation analysis

[0052] a. Adoption Describe measurement data and use composition ratio to describe count data;

[0053] b. All statistical analyses were set to be two-sided tests, with the test level α being 0.05;

[0054] c. The Pearson correlation coefficient was used to describe the correlation between the respiratory symptom score, activity limitation score, disease impact score, and total score of the SGRQ scale, and a statistical test of the correlation coefficient was performed.

[0055] 2. Univariate analysis

[0056] a. Data preprocessing: Calculate the difference between each type of SGRQ score at all follow-up times and the corresponding baseline;

[0057] b. Univariate analysis of NAC treatment and change in SGRQ score:

[0058] According to whether or not NAC treatment was received, the patients were divided into two groups: "NAC treatment" and "no NAC treatment". At each follow-up time dimension, the Mann-Whitney U test was used to analyze whether there were statistical differences in the changes in various SGRQ scores (respiratory symptom score, activity limitation score, disease impact score, and total score) between the two groups.

[0059] c. Univariate analysis of follow-up time and SGRQ score change:

[0060] According to the follow-up time, the patients were divided into three groups: "one month after discharge", "three months after discharge", and "six months after discharge". The Kruskal-Wallis H test was used to comprehensively analyze whether there were statistical differences in the changes in various SGRQ scores (respiratory symptom score, activity limitation score, disease impact score, and total score) among the three groups in different dimensions with or without NAC treatment. The Bonferroni method was used to adjust the test level α, and the differences in the changes in various SGRQ scores between the three groups were analyzed pairwise.

[0061] 3. Multi-factor analysis

[0062] a. Data preprocessing: During the modeling process, a new indicator, the unit value of difference (UVD) of the corresponding variable, was constructed based on the minimal clinically important difference (MCID). For the total SGRQ score, the UVD formula is:

[0063]

[0064] Among them, SGRQ i is the total SGRQ score at follow-up time i, MCID SGRQ is the MCID of the total SGRQ score; UVD SGRQ As the dependent variable, it was included in the generalized estimating equation for analysis. Its meaning in the generalized estimating model is: when other variables remain unchanged, the total score of SGRQ increases or decreases by a certain MCID due to a certain factor; when UVD in the model SGRQ When the absolute value of the change is greater than 1, it means that the change in the total SGRQ score has clinical significance;

[0065] The presence or absence of NAC treatment, disease severity, and follow-up time were set as independent variables, and UVD was set as the dependent variable. Generalized estimating equations were used to conduct multivariate analysis of the factors affecting SGRQ scores.

[0066] Epidata 3.1 software was used for double entry and verification of all patient data. All statistical analyses were performed using SPSS 25.0, with an α test level of 0.05.

[0067] Experimental results:

[0068] 1. Baseline conditions of COVID-19 patients

[0069] As shown in Table 1, the average age of COVID-19 patients was 44.65 years, and the average hospital stay was 16.24 days. Among the patients, there was a higher proportion of males, a higher proportion of overweight, a lower proportion of smokers, a lower proportion of regular or occasional drinkers, a higher proportion of occasional physical exercisers, a lower proportion of patients with a history of respiratory diseases, a lower proportion of patients with other systemic complications, and a higher proportion of patients with mild symptoms.

[0070] Table 1 Baseline conditions of COVID-19 patients

[0071]

[0072]

[0073] 2. Correlation analysis between SGRQ series scores of all patients

[0074] Pearson correlation coefficient is used to describe the correlation between the scores. et al. found that under the condition of α = 0.05 test level, there was a strong correlation between the scores of each part of SGRQ and between each part score and the total score. Among them, the total score of SGRQ had the greatest correlation with the activity limitation score, followed by the disease impact score, and the least with the respiratory symptom score.

[0075] Table 2 Correlation of SGRQ series scores in all COVID-19 patients

[0076]

[0077]

[0078] 3. Univariate analysis of factors affecting the changes in SGRQ scores

[0079] 1) Differences in the changes in scores between the two groups of patients at each follow-up time

[0080] As can be seen from Table 3, according to the test level of α=0.05, for the scores at discharge, the median differences in the changes in the disease impact score and the SGRQ total score of the two groups of patients one month after discharge were statistically significant; the median differences in the changes in the activity limitation score, the disease impact score and the SGRQ total score of the two groups of patients three months after discharge were statistically significant; the median differences in the changes in the activity limitation score, the disease impact score and the SGRQ total score of the two groups of patients six months after discharge were also statistically significant.

[0081] Table 3 Changes in SGRQ scores of the two groups of patients at each follow-up time

[0082]

[0083]

[0084] Note: The Mann-Whitney U test was used to compare the differences in the scores between groups.

[0085] 2) Differences in changes in patient scores at each follow-up time between the two groups

[0086] As shown in Table 4, according to the test level of α=0.05, the overall differences in the median changes of each score between the two groups of patients at different follow-up times were not statistically significant, and it cannot be concluded that the scores of the two groups of patients at different follow-up times are not completely the same.

[0087] Table 4 Changes in SGRQ scores of COVID-19 patients at different follow-up times in the two groups

[0088]

[0089] Note: The Kruskal-Wallis H test was used to compare the differences in the changes of scores at different follow-up times.

[0090] 3) Multivariate analysis of factors affecting the total SGRQ score UVD in COVID-19 patients

[0091] A generalized estimating equation was constructed to analyze the factors affecting the UVDSGRQ of patients. The analysis results are shown in Table 5: It was found that NAC treatment, severe illness, and follow-up time were all statistically significant for UVDSGRQ, and it was also found that the interaction term between NAC treatment and follow-up time was also statistically significant.

[0092] Without follow-up, patients who received NAC treatment had a lower risk of UVD than those who did not receive NAC treatment, assuming that the severity of the disease remained unchanged. SGRQ The increase was 1.954, that is, the SGRQ total score of patients receiving NAC treatment increased by an average of 1.954 units MCID compared with those not receiving NAC treatment, which contradicts the expected results. SGRQ When the disease is severe, the average increase is 3.179 compared with mild cases. Under the premise that the severity of the disease remains unchanged, for patients who did not receive NAC treatment, the UVD SGRQ The average reduction was 0.232, which did not reach the minimum clinically significant change value.

[0093] Table 5. Generalized Estimating Equation Parameter Estimation Results

[0094]

[0095] 5) Contour plot of marginal mean of SGRQ scores in COVID-19 patients

[0096] According to the results of the interaction term parameter estimation of the model, it can be obtained that the total SGRQ score of patients receiving NAC treatment decreased faster than that of patients not receiving NAC treatment, that is, the total SGRQ score of patients receiving NAC treatment decreased by an average of 0.436 MCID units on the basis of an average decrease of 0.232 MCID units in the follow-up time increased by one month. Figure 1 It can be observed that the slope of the mean change in the SGRQ total score of patients who received NAC treatment during the same period of time was greater than that of patients who did not receive NAC treatment.

[0097] It can be seen that the total SGRQ score of COVID-19 patients decreased faster after NAC treatment than without NAC treatment. Long-term use of NAC can improve the quality of life of COVID-19 patients.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A model for evaluating the effectiveness of NAC for COVID-19, characterized in that: The model includes the following modules: 1) Data collection module: used to collect relevant information of COVID-19 patients; 2) Data statistics module: used for statistical description and correlation analysis; 3) Data analysis module: used for univariate and multivariate analysis of data.

2. The model according to claim 1, characterized in that In the step 1), the COVID-19 patients are divided into a NAC treatment group and a NAC non-treatment group; Preferably, the patients in the NAC treatment group continue to take NAC orally after discharge; More preferably, the inclusion criteria for the patients are: (a) age > 18 years old; (b) confirmed to be SARS-CoV2 positive by real-time reverse transcription polymerase chain reaction test of nasal swab or lower respiratory tract specimen, and pneumonia confirmed by chest CT.

3. The model according to any one of claims 1-2, characterized in that: In the step 1), the collection specifically includes: collecting demographic data, clinical symptoms and signs, comorbidities, laboratory tests, chest imaging data and treatment plans of patients in the NAC treatment group and the NAC non-treatment group at the time of admission; collecting clinical symptoms and signs, laboratory tests, and chest imaging data of patients in the NAC treatment group and the NAC non-treatment group at the time of discharge, and filling in the SGRQ; collecting follow-up data of patients in the NAC treatment group and the NAC non-treatment group 1 month, 3 months, and 6 months after discharge, and filling in the SGRQ.

4. The model according to any one of claims 1 to 3, characterized in that: In the step 2), the statistical description and correlation analysis includes the following steps: a. Adoption Describe measurement data and use composition ratio to describe count data; b. All statistical analyses are set to be two-sided tests; preferably, the test level α is 0.05; c. The Pearson correlation coefficient was used to describe the correlation between the respiratory symptom score, activity limitation score, disease impact score, and total score of the SGRQ scale, and a statistical test of the correlation coefficient was performed.

5. The model according to any one of claims 1 to 4, characterized in that: In the step 3), the univariate analysis includes: a. Data preprocessing: Calculate the difference between each type of SGRQ score at all follow-up times and the corresponding baseline; b. Univariate analysis of NAC treatment and change in SGRQ score; c. Univariate analysis of follow-up time and change in SGRQ score.

6. The model according to claim 5, characterized in that Univariate analysis of the relationship between NAC treatment and change in SGRQ score included: According to whether or not NAC treatment was received, the patients were divided into two groups: "NAC treatment" and "no NAC treatment". The Mann-Whitney U test was used to analyze whether there were statistical differences in the changes in various SGRQ scores (respiratory symptom score, activity limitation score, disease impact score, and total score) between the two groups at each follow-up time dimension.

7. The model according to claim 5, characterized in that The univariate analysis of the follow-up time and the change in SGRQ score included: According to the follow-up time, the patients were divided into three groups: "one month after discharge", "three months after discharge", and "six months after discharge". The Kruskal-Wallis H test was used to comprehensively analyze whether there were statistical differences in the changes in various SGRQ scores (respiratory symptom score, activity limitation score, disease impact score, and total score) among the three groups in different dimensions with or without NAC treatment. The Bonferroni method was used to adjust the test level α, and the differences in the changes in various SGRQ scores between the three groups were analyzed pairwise.

8. The model according to any one of claims 1 to 7, characterized in that: In step 3), the multi-factor analysis includes the following steps: a. Data preprocessing: During the modeling process, a new indicator is constructed based on the minimum clinically significant change value MCID: the change unit value UVD of the corresponding variable; for the total SGRQ score, the formula for UVD is: Among them, SGRQ0 is the total SGRQ score at follow-up time i, MCID '()* is the MCID of the total SGRQ score; UVD SGRQ As the dependent variable, it was included in the generalized estimating equation for analysis. Its meaning in the generalized estimating model is: when other variables remain unchanged, the total score of SGRQ increases or decreases by a certain MCID due to a certain factor; when UVD in the model '()* When the absolute value of the change is greater than 1, it means that the change in the total SGRQ score has clinical practical significance; The presence or absence of NAC treatment, disease severity, and follow-up time were set as independent variables, and UVD was set as the dependent variable. Generalized estimating equations were used to conduct multivariate analysis of the factors affecting SGRQ scores.

9. Use of the model described in any one of claims 1 to 8 in evaluating the effectiveness of NAC for COVID-19.

10. A method for evaluating the effectiveness of NAC for COVID-19, characterized by comprising the following steps: 1) Data collection: Collect relevant information of COVID-19 patients; 2) Data statistics: statistical description and correlation analysis; 3) Data analysis: univariate and multivariate analysis of data.

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