Effect of Traditional Chinese Medicine in patients with COVID-19: A multi-center retrospective cohort study

Effect of Traditional Chinese Medicine in patients with COVID-19: A multi-center retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of Traditional Chinese Medicine in patients with COVID-19: A multi-center retrospective cohort study Guozhen Zhao, Shiyan Yan, Bo Li, Yuhong Guo, Shuang Song, Yahui Hu, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2350033/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Traditional Chinese medicine (TCM) has been applied in the treatment of COVID-19 in China, but its effectiveness and safety need evaluation. Methods A multi-center retrospective cohort study was carried out, with cumulative TCM treatment period of ≥ 3 days during hospitalization as exposure. Data came from consecutive inpatients in 4 medical centers in Wuhan, China. After data extraction, verification and cleaning, confounding factors were adjusted by inverse probability of treatment weighting, and the Cox proportional hazards regression model was used for statistical analysis. Results A total of 2272 COVID-19 patients were included, including 1684 in the TCM group and 588 in the control group. Compared with the control group, the hazard ratio for the deterioration rate in the TCM group was 0.52 [95% CI: (0.41, 0.64), P < 0.001]. The results were consistent across patients of varying severity at admission, and two sensitivity analyses confirmed the robustness of the results. In addition, the hazard ratio for all-cause mortality in the TCM group was 0.29 (95% CI = 0.19–0.44, P < 0.001). For safety, the proportion of patients with abnormal liver function or renal function in the TCM group was smaller. Conclusion This real-world study indicates that the addition of a full course of TCM therapy to basic conventional treatment, may reduce the deterioration rate and all-cause mortality of COVID-19 patients with safety. This result can provide evidence to support the current treatment of COVID-19 and new respiratory infectious diseases in the future. Additional prospective clinical trial is needed to evaluate the efficacy and safety of specific TCM interventions. Trial registration: ChiCTR, ChiCTR2200062917. Registered 23 August 2022, http://www.chictr.org.cn/showproj.aspx?proj=171556 . COVID-19 Traditional Chinese medicine Deterioration rate All-cause mortality Cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. As of December 5th, 2022, the world had reported over 641 million confirmed cases and 6.6 million deaths. As COVID-19 was declared a global pandemic, serious impact on human health and social economy followed [2,3]. At present, the guidelines issued by the National Health Commission of the People’s Republic of China and World Health Organization (WHO) recommend oral antiviral drugs for the treatment of COVID-19, including nirmatrelvir, ritonavir, and molnupiravir [4,5]. However, the clinical application of these drugs is limited, and there may be adverse reactions such as dysgeusia and diarrhea[5,6]. As well, their efficacy in COVID-19 patients may be reduced in the future due to the emergence of new mutant strains of SARS-CoV-2 [7]. In view of the definite efficacy of Traditional Chinese medicine (TCM) on acute respiratory infectious diseases such as influenza, TCM has been widely used in the treatment of COVID-19 and played an important role in "China's anti-epidemic" [8–10]. In a national retrospective registration study of 8939 COVID-19 patients, Qingfei Paidu Decoction was found to significantly reduce the mortality rate of patients, indicating that TCM has certain advantages in the treatment of COVID-19 [11]. In the early stage of the epidemic, a variety of TCM interventions, such as Qingfei Paidu Decoction, Jinhua Qinggan Granules, and Lianhua Qingwen Capsules, have been recommended for use in the diagnosis and treatment plan issued by National Health Commission [12]. The results of a number of previous intervention studies have shown that a variety of TCM interventions (such as TCM decoction, Huoxiang Zhengqi Dripping Pills, Lianhua Qingwen Capsules and Xiyanping Injection) in the treatment of COVID-19, are effective in relieving fever, cough, fatigue, nausea, aching limbs and other symptoms. Therefore, shortening the length of hospital stay and improving outcomes. However, due to the small sample size of the study, there is still uncertainty about the TCM impact on the deterioration rate [13–16]. There are also published cohort studies as supplementary evidence. Although the effectiveness of TCM in reducing the deterioration rate has been preliminarily confirmed, the results still have certain limitations due to the small sample size of each study [17–19]. Therefore, it is still necessary to carry out large-scale clinical studies with deterioration rate as the primary outcome indicator, to evaluate the overall effectiveness and safety of TCM and provide high-quality evidence-based medical evidence for the treatment of COVID-19. Based on real-world patient data, this study evaluated the overall effectiveness of integrated traditional Chinese and Western medicine in the treatment of COVID-19, by observing the deterioration and mortality rates of COVID-19 patients with TCM combined with conventional treatment. The main research hypothesis of this study is that for hospitalized adult COVID-19 patients, the addition of TCM intervention to conventional treatment can reduce the deterioration rate of the disease. Materials And Methods Study design and participants This study used a multicenter retrospective cohort study design. The research data came from the electronic medical records (EMRs) of inpatients at Wuhan First Hospital, Wuhan Pulmonary Hospital, Jiangxia Cabin Hospital, and Wuhan Wuchang Hospital from December 19th 2019 to May 16th 2020. These four hospitals were designated hospitals for COVID-19 during this period, and only admitted patients with confirmed and suspected COVID-19 cases. The diagnostic criteria and clinical classification criteria of COVID-19 refers to the Diagnosis and Treatment Guideline for COVID-19 issued by the National Health Commission of People’s Republic of China. The patients can be divided into mild, moderate, severe and critical cases [12]. The inclusion criteria for this study were: (1) meet the diagnosis of COVID-19; (2) age ≥ 18 years; (3) the time from the onset of the initial symptoms to the admission of the patient to be ≤ 14 days, and the initial symptoms included fever, cough, dyspnea, physical pain, fatigue, nausea and diarrhea. The exclusion criteria were: (1) not being infected with COVID-19 for the first time; (2) the patient participated in other interventional clinical trials; (3) the etiological evidence showed that the patient had upper respiratory tract infection and pneumonia caused by infection with other pathogens (non-SARS-CoV-2 virus); (4) death occurred within 48 hours after admission. Data collection Data of consecutive hospitalized patients at four medical centers was extracted from EMRs and put into a standard electronic database based on a pre-defined data extraction form. Among them, demographic characteristics (age and sex), date of admission and date of discharge or death, were extracted from the front page of the medical record. The initial symptoms (fever, cough, dyspnea, fatigue, diarrhea, nausea and vomiting) alongside the date, the time of admission, the clinical classification (mild or moderate, severe, critical), and past medical history (chronic respiratory disease, chronic heart disease, hypertension, and diabetes), were extracted from admission records. Patients vital signs (body temperature, respiratory rate, heart rate, blood pressure, and blood oxygen saturation), liver and kidney function, were extracted from the course records. The patients medication status during hospitalization (herbal medicine, Chinese patent medicine, antiviral drugs, antibiotics, antifungals, glucocorticoids, and immunotherapy) was extracted from the doctors order sheet. After the clinical coordinating staff and data management associates of Peking Union Medical College Hospital, and Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, communicated with clinicians for several rounds to ensure the integrity and accuracy of the data, data verification and cleaning was carried out. Data verification included random sampling of 5% of the data to evaluate the accuracy of data extraction, and analysis of missing, contradictory or extreme values, and outliers of each variable. According to the data verification results, cleaning rules were formulated, and data cleaning was carried out. Data extraction, verification and cleaning were performed using SAS software (version 9.4). Statistical analysis and reporting of results followed the entries of the STORBE statement and the RECORD statement [20,21]. Exposures Patients were divided into TCM group and control group according to whether they were exposed or not. All patients who received TCM treatment related to COVID-19 during hospitalization, with a cumulative treatment period of ≥ 3 days, were defined as exposed. TCM treatment related to COVID-19 was defined as the use of any of the following interventions: (1) Chinese herbal medicine; (2) Oral Chinese patent medicine, including Lianhua Qingwen Capsule, Huoxiang Zhengqi Capsule, Shufeng Jiedu Capsule, and Jinhua Qinggan Granules; (3) Chinese patent medicine injections, including Xuebijing Injection, Tanreqing Injection, Xiyanping Injection, Xingnaojing Injection, Shenfu Injection, and Shenmai Injection. The patients who met the exposure were divided into the TCM group, and the rest of the patients belonged to the control group. Outcomes The primary outcome of this study was the deterioration rate. Referring to the Diagnosis and Treatment Guideline for COVID-19 [12], COVID-19 patients were divided into three clinical states: mild and moderate, severe, or critical. Included in mild and moderate cases are patients exhibiting mild clinical symptoms, with no visible pneumonia on imaging. In severe cases, one of the following conditions needs to be met: ① Shortness of breath, respiratory rate ≥ 30 times/min; ② in resting state, oxygen saturation ≤ 93% when inhaling air; ③ arterial partial pressure of oxygen (PaO 2 )/ fraction of inspiration oxygen (FiO 2 ) ≤ 300 mmHg; ④ clinical symptoms progressively worsened, and lung imaging showing that the lesions progressed significantly within 24 to 48 hours (> 50%). In critical cases, patients should meet any of the following conditions: ① Respiratory failure requiring mechanical ventilation; ② shock; ③ combined with other organ failure requiring ICU monitoring and treatment. The clinical status of the patient on admission was the baseline status. If a patient developed a more severe clinical state during hospitalization, the patient’s condition is judged to have deteriorated. For example, mild and moderate patients progressed to severe or critical, severe progressed to critical, or a death event occurred. The secondary outcome was all-cause mortality during hospitalization. Statistical analysis Baseline differences between the two groups were estimated by the standard deviation (SD), with an absolute value less than 0.1 indicating a small difference between the two groups. To balance the difference in baseline between the two groups in observational studies, the inverse probability of treatment weighting (IPTW) was used to make the two groups' baselines comparable [22]. The propensity score (PS) is the conditional probability of a patient receiving TCM treatment after identifying a series of covariates that may affect the effectiveness and is calculated by a logistic regression model. According to the known clinical background knowledge and previous research results, the following factors were selected as covariates: sex, age, severity of disease at baseline, underlying diseases (chronic respiratory disease, chronic heart disease, hypertension, diabetes), days from initial symptoms to hospital visit, patient symptoms at baseline (fever, cough, dyspnea, fatigue, diarrhea, nausea and vomiting), and antiviral drugs administered during hospitalization. Baseline balance between the two groups after IPTW was estimated by the SD. Continuous variables were described by means and standard deviations, or medians and quartiles. Dichotomous variables were described by frequencies and percentages. Kaplan-Meier curves were drawn for the deterioration rate and mortality in the two groups, and the Cox proportional hazards regression model was applied estimate the hazard ratio (HR) and 95% confidence interval (CI). The univariate logistic regression model was used to estimate the odds ratio (OR) values and 95% CIs for deterioration rate and mortality between the two groups on days 7, 14, and 28. Two-sided tests were used, and P-values < 0.05 indicated a statistically significant difference. If missing data were missing at random, multiple imputation (MI) was used to handle missing values [23]. Subgroup analyses were performed on the primary outcome measures of the whole patient dataset after IPTW processing. Subgroups were defined by the following factors: sex, age, disease severity at baseline, previous chronic heart disease, previous chronic respiratory disease, previous hypertension, previous diabetes, and days from initial symptom onset to hospital visit. In this study, the main outcome index, deterioration rate in the missing data cohort, and the cohort after using propensity score matching (PSM), were also statistically compared with the main statistical analysis results. As well, a sensitivity analysis was performed to evaluate the robustness of the results. Statistical analysis and imputation of missing values was performed using R software (version 4.2.0). Results Patient Characteristics During the period from December 19th 2019 to May 16th 2020, the four medical centers admitted a total of 4,819 hospitalized patients, of which 3,962 were diagnosed with COVID-19, accounting for 82.22% of all patients. 2424 patients met the inclusion criteria, 152 patients were excluded according to the pre-set criteria, and 2272 patients were finally included in the statistical analysis (Fig. 1 ). The multiple imputation method was used to deal with missing values. Among the 2272 patients, 1684 were in the TCM group, with an average age of 55.13 ± 15.26 years, and 746 (44.3%) were male. Detailed clinical and baseline characteristics of the two groups patients are shown in Table 1 . Table 1 Clinical and relevant baseline characteristics of patients Characteristic Total (n = 2272) TCM group (n = 1684) Control group (n = 588) SD Demographics Age [mean (SD)] 55.52 (15.6) 55.13 (15.3) 56.65 (16.6) 0.095 Male (%) 1051 (46.3) 746 (44.3) 305 (51.9) 0.152 Status at admission (%) Type mild or moderate 1864 (82.0) 1436 (85.3) 428 (72.8) 0.310 Type severe 249 (11.0) 164 (9.7) 85 (14.5) 0.145 Type critical 159 (7.0) 84 (5.0) 75 (12.8) 0.276 Time to admission after onset (mean (SD)) Duration time 7.22 (3.9) 7.21 (3.9) 7.22 (3.9) 0.001 Any comorbidity (%) Chronic respiratory disease 162 (7.1) 93 (5.5) 69 (11.7) 0.223 Chronic cardiac disease 214 (9.4) 138 (8.2) 76 (12.9) 0.154 Hypertension 641 (28.2) 454 (27.0) 187 (31.8) 0.106 Diabetes 329 (14.5) 213 (12.6) 116 (19.7) 0.193 Any symptoms (%) Fever 2059 (90.6) 1516 (90.0) 543 (92.3) 0.082 Cough 1993 (87.7) 1507 (89.5) 486 (82.7) 0.198 Dyspnea 1537 (67.6) 1143 (67.9) 393 (66.8) 0.022 Fatigue 1733 (76.3) 1334 (79.2) 400 (68.0) 0.256 Diarrhea 1079 (47.5) 866 (51.4) 214 (36.4) 0.306 Nausea or Vomit 778 (34.2) 616 (36.6) 162 (27.6) 0.194 Vital signs [mean (SD)] Temperature, ℃ 37.0 (0.8) 37.0 (0.8) 36.9 (0.8) 0.126 Respiratory rate/min 20.7 (2.8) 20.5 (2.6) 21.2 (3.3) 0.245 Heart rate/min 86.1 (14.1) 85.1 (13.5) 89.0 (15.1) 0.273 Systolic pressure, mmHg 125.6 (15.0) 125.1(14.2) 127.1 (17.0) 0.129 Diastolic pressure, mmHg 77.5 (10.21) 77.5 (9.8) 78.6 (11.2) 0.106 SPO 2 , % 94.9 (5.76) 95.1 (5.2) 94.3 (7.1) 0.134 Treatment during hospitalization (%) Antiviral treatment 1710 (75.3) 1328 (78.9) 382 (65.0) 0.313 Arbidol 1080 (47.5) 918 (54.5) 162 (27.6) 0.570 Oseltamivir 406 (17.9) 319 (18.9) 87 (14.8) 0.111 Ribavirin 296 (13.0) 280 (16.6) 16 (2.7) 0.484 Lopinavir/ritonavir 282 (12.4) 70 (4.2) 212 (36.1) 0.868 Interferon-α 842 (37.1) 583 (34.6) 259 (44.0) 0.194 Antibiotic treatment 1278 (56.2) 1017 (60.4) 261 (44.4) 0.325 Moxifloxacin 1029 (45.3) 874 (51.9) 155 (26.4) 0.542 Cephalosporins 498 (21.9) 342 (20.3) 156 (26.5) 0.147 Antifungal treatment 48 (2.1) 26 (1.5) 22 (3.7) 0.137 Voriconazole 30 (1.3) 15 (0.9) 15 (2.6) 0.128 Fluconazole 14 (0.6) 10 (0.6) 4 (0.7) 0.011 Caspofungin 14 (0.6) 9 (0.5) 5 (0.9) 0.038 Glucocorticoids 791 (34.8) 513 (30.5) 278 (47.3) 0.350 Human immunoglobulin 507 (22.3) 358 (21.3) 149 (25.3) 0.097 In the TCM group, the most commonly used TCM was Chinese herbal medicine, and the proportions used in mild and moderate, severe and critical patients were 89.83%, 89.02%, and 70.24%, respectively. Lianhua Qingwen Capsule had the highest proportion of use in oral Chinese patent medicine. For Chinese patent medicine injection, Xiyanping Injection was used in the highest proportion in mild and moderate patients, and Xuebijing Injection was used in the highest proportion in in severe and critical patients (Supplementary Table 1). The proportion of patients who used Chinese herbal medicine in combination with one Chinese patent medicine during hospitalization was the highest amongst all the different Chinese medicine combinations (Supplementary Table 2). The control group had 588 COVID-19 patients who did not receive TCM treatment, or who received TCM ≤ 2 days in total, and the medication condition was similar to the TCM group (Supplementary Table 3). There were certain differences in the characteristics of the two groups of patients at baseline (Table 1 ), and the comparability of the two groups at baseline was achieved by the IPTW method. After IPTW adjustment, the main baseline characteristics between the TCM group and the control group were balanced (Supplementary Table 4 and Supplementary Fig. 1). Outcomes During the median hospital stay of 17 days (10, 24), a total of 500 (22.0%) patients had deterioration events, including 343 (20.4%) in the TCM group and 157 (26.6%) in the control group (Supplementary Table 5). After IPTW adjusted for all confounding factors, the results showed that the deterioration rate of the TCM group was lower than that of the control group [HR = 0.52, 95% CI: (0.41, 0.64), P < 0.001] (Fig. 2 ). This result was consistent across patients of varying severity at admission (Fig. 3 ). Furthermore, in IPTW models adjusted for demographic confounders only [HR = 0.52, 95% CI: (0.42, 0.64), P < 0.001], and unadjusted models [HR = 0.52, 95% CI: (0.42, 0.63), P < 0.001], TCM treatment showed the same advantage. In a sensitivity analysis of the primary outcome, all confounding factors were adjusted by using PSM (2:1 nearest neighbor matching, no replacement, caliper = 0.2). The results still suggest that the full-course of TCM treatment reduces the HR of deterioration rates [HR = 0.50, 95% CI: (0.40, 0.63), P < 0.001]. Another sensitivity analysis with direct deletion of missing data followed by IPTW yielded the same result [HR = 0.52, 95% CI: (0.42, 0.63), P < 0.001]. Two sensitivity analyses confirmed the robustness of the results (Table 2 ). Logistic regression analysis showed that the deterioration rate of the TCM group on days 7, 14 and was lower than that of the control group (Table 3 ). Table 2 Multivariable cox regression analysis of deterioration rate of patients receiving TCM Outcomes Unadjusted model Demographic-adjusted model 1 Fully adjusted model 2 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value IPTW-MI model 0.52 (0.42, 0.63) < 0.001 0.52 (0.42, 0.64) < 0.001 0.52 (0.41, 0.64) < 0.001 PSM-MI model 0.52 (0.42, 0.63) < 0.001 0.44 (0.35, 0.55) < 0.001 0.50 (0.40, 0.63) < 0.001 Non-MI model 0.50 (0.41, 0.60) < 0.001 0.53 (0.43, 0.64) < 0.001 0.52 (0.42, 0.63) < 0.001 1 Demographic-adjusted: controlled for age and sex as covariates 2 Fully adjusted: controlled for age, sex, status at admission, time to admission after onset, comorbidity, symptoms, and antiviral treatment during hospitalization as covariates Table 3 Comparison of the clinical outcomes in day 7, 14 and 28 before the propensity score adjustment Outcomes TCM group (n = 1684) Control group (n = 588) P -value OR (95% CI) Deterioration rate (%) Through day 7 159 99 < 0.001 0.51 (0.39, 0.68) Through day 14 300 145 < 0.001 0.66 (0.53, 0.83) Through day 28 337 154 0.002 0.71 (0.57,0.88) All-cause mortality (%) Through day 7 10 35 < 0.001 0.09 (0.05, 0.19) Through day 14 26 60 < 0.001 0.14 (0.09, 0.22) Through day 28 54 73 < 0.001 0.23 (0.16, 0.34) In the evaluation of all-cause mortality as a secondary outcome indicator, a total of 135 patients (5.94%) died, including 60 (3.56%) in the TCM group and 75 (12.80%) in the control group. After IPTW adjusted for all confounding factors, the results showed that the TCM group had lower all-cause mortality than the control group [HR = 0.29, 95% CI: (0.19, 0.44), P < 0.001] (Fig. 4 ). The all-cause mortality in the TCM group was lower than that in the control group on the days 7, 14 and 28 (Table 3 ). Subgroup analyses In a subgroup analysis stratified by different factors (Fig. 5 ), we observed that there may be a stronger positive association between the use of TCM and the risk of deterioration events for patients visiting hospital on days 7–14 after the onset of initial symptoms ( P for interaction = 0.036). In addition, compared with the control group, the TCM treatment had effects on reducing the deterioration rate in the subgroups of different sex, different severity of the disease, with or without hypertension, with or without diabetes, with or without chronic heart disease, at different times from initial symptoms to hospital visit, and the difference was statistically significant. However, for patients aged < 40 years [HR = 0.58, 95% CI: (0.23, 1.43), P = 0.236 ], and patients with chronic respiratory disease [HR = 0.61, 95% CI: (0.31, 1.18), P = 0.141], TCM treatment did reduce the deterioration rate but it was not statistically significant. In addition, in the subgroups with different severities of disease, it can be found that the more severe the patient's disease is at baseline, the greater the effect of TCM on reducing the deterioration rate. Safety During the treatment of integrated traditional Chinese and western medicine, 6 patients (0.36%) in the TCM group and 5 patients (0.85%) in the control group developed abnormal liver function; 1 patient (0.06%) in the TCM group and 3 patients (0.51%) in the control group had abnormal renal function. No other adverse events occurred during the study. Since SARS-CoV-2 itself can also cause liver and kidney damage, the correlation between the observed adverse events and therapeutic drugs cannot be determined. However, because the overall proportion of abnormal liver function and renal function in the TCM group was not high, and was lower than that in the control group, it highlights the safety of TCM. Discussion The results of this multicenter retrospective cohort study of 2272 patients showed that full-course of TCM treatment can reduce the risk of deterioration events (HR = 0.52), and death events (HR = 0.29), in patients with COVID-19. Two sensitivity analyses confirmed the robustness of the findings. In addition, the results of subgroup analysis can preliminarily indicate that TCM treatment can reduce the deterioration rate of patients in most cases, and the more severe the patient's condition, the greater the effect of TCM may be. In previous clinical trials, the relative risk (RR) of TCM intervention on deterioration rate was in the range of 0.00-0.50 [13–16]. Although TCM tends to reduce the deterioration rate, some research results are not statistically significant due to the small sample size. Meanwhile, due to the achievements of China's epidemic prevention and control, as well as medical ethics considerations, some RCTs cannot be implemented according to the established plan[24]. Therefore, the results of the previous study have certain limitations. However, based on real-world data from a large sample, this study showed that patients in the TCM group had 0.52 times the chance of disease deterioration at the next time point compared with the control group. Considering that the control group of this study included patients who received TCM for 1 or 2 days, rather than patients who did not use TCM at all, the effectiveness of the control group could be overestimated, and the effectiveness of the TCM group could be underestimated. Therefore, the actual effectiveness of the TCM group may be better. The results of this study illustrate that TCM can be used in the clinical treatment of COVID-19 at various stages, it reduces the risk of deterioration, and is safe. The reduction in the deterioration rate also means that TCM treatment can reduce the risk of mild and moderate cases progressing to severe, and severe progressing to critical or even death. In a previous national retrospective registry study, compared with patients who did not take Qingfei Paidu Decoction, the application of Qingfei Paidu Decoction significantly reduced mortality [1.2% vs. 4.8%, HR = 0.50, 95% CI: (0.37, 0.66)]. This effect size is smaller than the results of this study [11]. We believe that the control group of this study (patients who did not take Qingfei Paidu Decoction) may have used other TCM interventions, thus overestimating the effectiveness of the control group and reducing the effect size of the study. But in general, the effect of TCM intervention on reducing mortality is worthy of recognition. As an acute infectious disease, COVID-19 is epidemic and contagious. It belongs to the category of "plague" in TCM. Most of the COVID-19 patients in Wuhan have symptoms of fever, cough, fatigue, poor appetite, and thick and greasy tongue coating. The syndrome differentiation of TCM should focus on “dampness toxin” [25]. In clinical treatment, Chinese herbal medicines for resolving dampness, clearing heat, dispelling wind and removing toxins can be applied. Oral Chinese patent medicines such as Lianhua Qingwen Capsules can be used for clearing heat and detoxifying. In severe cases, Xuebijing Injection and other Chinese patent medicine injections can also be used to assist Western medicine treatment [26]. Previous studies have found that TCM mainly plays the roles of antiviral, anti-inflammatory, immune regulation and organ protection in the treatment of COVID-19 [27–29]. In view of the definite effectiveness of TCM on a variety of acute infectious diseases [8], TCM interventions should still have a certain effect on patients infected with new variant strains of COVID-19, but this needs further research and verification. This study provides new evidence for TCM treatment of COVID-19. Firstly, this study took deterioration rate and mortality as outcomes. Deterioration rate and mortality are the most important and objective outcomes in the evaluation of COVID-19. Since there is low incidence of death, preventing further deterioration is more important for most COVID-19 patients [30]. Secondly, the sample size of this study is three times the largest sample size of previous observational studies evaluating deterioration rates, which means this study also has good statistical power in subgroup analysis. Thirdly, the data for this study was derived from consecutive patients from four medical centers in Wuhan. Therefore, this study can be used as a supplement to RCTs in providing new and reliable medical evidence for TCM in the treatment of COVID-19 in a real-world setting. The results of this study still have some limitations. Firstly, since this study is an observational study, the influence of confounding factors on the results cannot be completely ruled out. However, we performed IPTW for confounders typically considered in comparative effects studies, making baseline characteristics comparable between the two groups, and the IPTW-adjusted model had a similar hazard ratio in reducing the rate of deterioration as the unadjusted model. Thus, suggesting that the reduction in the deterioration rate is mainly related to the application of TCM. Secondly, the SARS-CoV-2 strain of the patients included in this study is partially different from the currently prevalent SARS-CoV-2 Omicron variant in clinical features (such as infectivity and immune escape) [31]. However, considering the wide applicability of TCM and the exact curative effect on various variant strains, the results of this study can still provide evidence to support the current treatment of COVID-19 and new respiratory infectious diseases in the future [32]. Lastly, there is a certain clinical heterogeneity in the specific TCM interventions in the TCM group in this study. Although we describe the specific use of TCM in patients with different states, there may be differences in the effectiveness of different interventions on patients. Therefore, we can only evaluate the overall effectiveness of TCM on COVID-19. The efficacy of each specific intervention still needs further research. Conclusions This retrospective study based on real-world data shows adding a full course of TCM treatment to basic conventional treatment may reduce the deterioration rate and all-cause mortality of COVID-19 patients with good safety. This result can provide evidence to support the current treatment of COVID-19 and new respiratory infectious diseases in the future. However, rigorously designed multicenter, large sample randomized controlled trials are still required in the future to evaluate the efficacy and safety of specific TCM interventions. Abbreviations CI, confidence interval; COVID-19, coronavirus disease 2019; EMR, electronic medical record; HR, hazard ratio; IPTW, inverse probability of treatment weighting; MI, multiple imputation; OR, odds ratio; PS, propensity score; PSM, propensity score matching; RR, relative risk; SARS-CoV-2, syndrome coronavirus 2; SD, standard deviation; TCM, traditional Chinese medicine; WHO, World Health Organization. Declarations Acknowledgements We acknowledge clinical coordinating staff and data management associates of Peking Union Medical College Hospital and Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, and we have great thanks to all volunteers and health-care workers in the Wuhan, Hubei province. Author’s contributions Guozhen Zhao, Shiyan Yan, Bo Li, Yuhong Guo and Qingquan Liu designed the research and wrote the manuscript. Shuang Song, Yahui Hu, Yuan Du, Haitian Lu, Haoran Ye, Zhiying Ren and Lingfei Zhu governed the data. Shiqi Guo, Jing Hu, Xiaolong Xu and Rui Su analyzed the data. All authors read and approved the final manuscript. Qingquan Liu has primary responsibility for the final content. Funding This work was supported by the Innovation Team Project of the State Administration of traditional Chinese medicine (No. ZYYCXTD-D-202201) and the National Key Research and Development Plan of China (No. 2020YFC0861000 and No. 2021YFC1712901). Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was ethically approved by the Medical Ethics Committee of Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University (2022-BL02-033-01) and has been registered with the Chinese Clinical Research Registry (ChiCTR) (ChiCTR2200062917). Since this study was a retrospective study, it did not involve contact with subjects and would not pose a significant risk to the patients who provided the data, so an application was submitted to the Medical Ethics Committee for exemption of informed consent. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing, China 2 Beijing University of Chinese Medicine, Beijing, China 3 Tianjin University of Traditional Chinese Medicine, Tianjin, China References World Health Organization. Therapeutics and COVID-19: living guideline. Available from: https://www.who.int/publications/i/item/WHO-2019-nCoV-therapeutics-2022.4. Accessed on 5 Dec 2022. World Health Organization. WHO coronavirus disease (COVID-19) dashboard. Available from: https://covid19.who.int/. Accessed on 5 Dec 2022. World Health Organization. WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020. Accessed on 5 Dec 2022. Agarwal A, Rochwerg B, Lamontagne F, Siemieniuk RA, Agoritsas T, Askie L, et al. A living WHO guideline on drugs for covid-19. BMJ. 2020;370:m3379. General Office of the National Health and Health Commission, Office of the State Administration of Traditional Chinese Medicine. Notice on Issuing a New Coronary Virus Pneumonia Diagnosis and Treatment guidelines (Trial Version 9). Available from: http://www.nhc.gov.cn/yzygj/s7653p/202203/b74ade1ba4494583805a3d2e40093d88.shtml. Accessed on 5 Dec 2022. Hammond J, Leister-Tebbe H, Gardner A, Abreu P, Bao W, Wisemandle W, et al. Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19. N Engl J Med. 2022;386(15):1397-1408. Cao Y, Wang J, Jian F, Xiao T, Song W, Yisimayi A, et al. Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature. 2022;602(7898):657-663. Wang C, Cao B, Liu QQ, Zou ZQ, Liang ZA, Gu L, et al. Oseltamivir compared with the Chinese traditional therapy maxingshigan-yinqiaosan in the treatment of H1N1 influenza: a randomized trial. Ann Intern Med. 2011;155(4):217-225. World Health Organization. WHO expert meeting on evaluation of traditional Chinese medicine in the treatment of COVID-19. Available from: https://cdn.who.int/media/docs/default-source/traditional-medicine/meeting-report---who-expert-meeting-on-evaluation-of-tcm-in-the-treatment-of-covid-192f7d2ba2-cfb8-4b00-90e3-441740cdbacb.pdf. Accessed on 5 Dec 2022. Shi SF, Liu QQ. Discussion on the value of traditional Chinese medicine in the treatment of COVID-19 from the "Jiangxia Cabin TCM Model". Jiangsu J Tradit Chin Med. 2020;52(4):11-14. Zhang L, Zheng X, Bai X, Wang Q, Chen B, Wang H, et al. Association between use of Qingfei Paidu Tang and mortality in hospitalized patients with COVID-19: A national retrospective registry study. Phytomedicine. 2021;85:153531. General Office of the National Health and Health Commission, Office of the State Administration of Traditional Chinese Medicine. Notice on Issuing a New Coronary Virus Pneumonia Diagnosis and Treatment guidelines (Trial Version 6). Available from: http://www.nhc.gov.cn/yzygj/s7653p/202002/8334a8326dd94d329df351d7da8aefc2.shtml. Accessed on 5 Dec 2022. Xia WG, Zheng CJ, Zhang JX, Huang M, Li QL, Duan C, et al. Randomized controlled study of a diagnosis and treatment plan for moderate coronavirus disease 2019 that integrates Traditional Chinese and Western Medicine. J Tradit Chin Med. 2022;42(2):234-241. Xiao M, Tian J, Zhou Y, Xu X, Min X, Lv Y, et al. Efficacy of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules in treatment of COVID-19: A randomized controlled trial. Pharmacol Res. 2020;161:105126. Hu K, Guan WJ, Bi Y, Zhang W, Li L, Zhang B, et al. Efficacy and safety of Lianhuaqingwen capsules, a repurposed Chinese herb, in patients with coronavirus disease 2019: A multicenter, prospective, randomized controlled trial. Phytomedicine. 2021;85:153242. Zhang XY, Lv L, Zhou YL, Xie LD, Xu Q, Zou XF, et al. Efficacy and safety of Xiyanping injection in the treatment of COVID-19: A multicenter, prospective, open-label and randomized controlled trial. Phytother Res. 2021;35(8):4401-4410. Feng J, Fang B, Zhou D, Wang J, Zou D, Yu G, et al. Clinical Effect of Traditional Chinese Medicine Shenhuang Granule in Critically Ill Patients with COVID-19: A Single-Centered, Retrospective, Observational Study. J Microbiol Biotechnol. 2021;31(3):380-386. Tian J, Yan S, Wang H, Zhang Y, Zheng Y, Wu H, et al. Hanshiyi Formula, a medicine for Sars-CoV2 infection in China, reduced the proportion of mild and moderate COVID-19 patients turning to severe status: A cohort study. Pharmacol Res. 2020;161:105127. Zhang Y, Liu Y, Li M, Feng S, Li X, Gao Z, et al. Timely treatment and higher compliance to traditional Chinese medicine: New influencing factors for reducing severe COVID-19 based on retrospective cohorts in 2020 and 2021. Pharmacol Res. 2022;178:106174. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495-1499. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The Reporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PloS Med. 2015;12(10):e1001885. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661-3679. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. Zhao MZ, Zhao C, Tu SS, Wei XX, Shang HC. Evaluating the methodology of studies conducted during the global COVID-19 pandemic: A systematic review of randomized controlled trials. J Integr Med. 2021;19(4):317-326. Wang YG, Qi WS, Ma JJ, Ruan LG, Lu YR, Li XC, et al. A preliminary study on the clinical characteristics and syndrome differentiation of COVID-19 in traditional Chinese medicine. J Tradi Chin Med. 2020;61(4):281-285. Zhao Z, Li Y, Zhou L, Zhou X, Xie B, Zhang W, et al. Prevention and treatment of COVID-19 using Traditional Chinese Medicine: A review. Phytomedicine. 2021;85:153308. Chen J, Wang YK, Gao Y, Hu LS, Yang JW, Wang JR, et al. Protection against COVID-19 injury by qingfei paidu decoction via anti-viral, anti-inflammatory activity and metabolic programming. Biomed Pharmacother. 2020;129:110281. Huang K, Zhang P, Zhang Z, Youn JY, Wang C, Zhang H, et al. Traditional Chinese Medicine (TCM) in the treatment of COVID-19 and other viral infections: Efficacies and mechanisms. Pharmacol Ther. 2021;225:107843. An X, Zhang Y, Duan L, Jin D, Zhao S, Zhou R, et al. The direct evidence and mechanism of traditional Chinese medicine treatment of COVID-19. Biomed Pharmacother. 2021;137:111267. Jin X, Pang B, Zhang J, Liu Q, Yang Z, Feng J, et al. Core Outcome Set for Clinical Trials on Coronavirus Disease 2019 (COS-COVID). Engineering (Beijing). 2020;6(10):1147-1152. Fan Y, Li X, Zhang L, Wan S, Zhang L, Zhou F. SARS-CoV-2 Omicron variant: recent progress and future perspectives. Signal Transduct Target Ther. 2022;7(1):141. Shah MR, Fatima S, Khan SN, Ullah S, Himani G, Wan K, et al. Jinhua Qinggan granules for non-hospitalized COVID-19 patients: A double-blind, placebo-controlled, and randomized controlled trial. Front Med (Lausanne). 2022;9:928468. Supplementary Files 3SupplementaryMaterial.docx 4STROBEchecklist.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. As of December 5th, 2022, the world had reported over 641\u0026nbsp;million confirmed cases and 6.6\u0026nbsp;million deaths. As COVID-19 was declared a global pandemic, serious impact on human health and social economy followed [2,3].\u003c/p\u003e \u003cp\u003e At present, the guidelines issued by the National Health Commission of the People\u0026rsquo;s Republic of China and World Health Organization (WHO) recommend oral antiviral drugs for the treatment of COVID-19, including nirmatrelvir, ritonavir, and molnupiravir [4,5]. However, the clinical application of these drugs is limited, and there may be adverse reactions such as dysgeusia and diarrhea[5,6]. As well, their efficacy in COVID-19 patients may be reduced in the future due to the emergence of new mutant strains of SARS-CoV-2 [7].\u003c/p\u003e \u003cp\u003eIn view of the definite efficacy of Traditional Chinese medicine (TCM) on acute respiratory infectious diseases such as influenza, TCM has been widely used in the treatment of COVID-19 and played an important role in \"China's anti-epidemic\" [8\u0026ndash;10]. In a national retrospective registration study of 8939 COVID-19 patients, Qingfei Paidu Decoction was found to significantly reduce the mortality rate of patients, indicating that TCM has certain advantages in the treatment of COVID-19 [11]. In the early stage of the epidemic, a variety of TCM interventions, such as Qingfei Paidu Decoction, Jinhua Qinggan Granules, and Lianhua Qingwen Capsules, have been recommended for use in the diagnosis and treatment plan issued by National Health Commission [12]. The results of a number of previous intervention studies have shown that a variety of TCM interventions (such as TCM decoction, Huoxiang Zhengqi Dripping Pills, Lianhua Qingwen Capsules and Xiyanping Injection) in the treatment of COVID-19, are effective in relieving fever, cough, fatigue, nausea, aching limbs and other symptoms. Therefore, shortening the length of hospital stay and improving outcomes. However, due to the small sample size of the study, there is still uncertainty about the TCM impact on the deterioration rate [13\u0026ndash;16]. There are also published cohort studies as supplementary evidence. Although the effectiveness of TCM in reducing the deterioration rate has been preliminarily confirmed, the results still have certain limitations due to the small sample size of each study [17\u0026ndash;19]. Therefore, it is still necessary to carry out large-scale clinical studies with deterioration rate as the primary outcome indicator, to evaluate the overall effectiveness and safety of TCM and provide high-quality evidence-based medical evidence for the treatment of COVID-19.\u003c/p\u003e \u003cp\u003eBased on real-world patient data, this study evaluated the overall effectiveness of integrated traditional Chinese and Western medicine in the treatment of COVID-19, by observing the deterioration and mortality rates of COVID-19 patients with TCM combined with conventional treatment. The main research hypothesis of this study is that for hospitalized adult COVID-19 patients, the addition of TCM intervention to conventional treatment can reduce the deterioration rate of the disease.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study used a multicenter retrospective cohort study design. The research data came from the electronic medical records (EMRs) of inpatients at Wuhan First Hospital, Wuhan Pulmonary Hospital, Jiangxia Cabin Hospital, and Wuhan Wuchang Hospital from December 19th 2019 to May 16th 2020. These four hospitals were designated hospitals for COVID-19 during this period, and only admitted patients with confirmed and suspected COVID-19 cases.\u003c/p\u003e \u003cp\u003eThe diagnostic criteria and clinical classification criteria of COVID-19 refers to \u003cem\u003ethe Diagnosis and Treatment Guideline for COVID-19\u003c/em\u003e issued by the National Health Commission of People\u0026rsquo;s Republic of China. The patients can be divided into mild, moderate, severe and critical cases [12]. The inclusion criteria for this study were: (1) meet the diagnosis of COVID-19; (2) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (3) the time from the onset of the initial symptoms to the admission of the patient to be \u0026le;\u0026thinsp;14 days, and the initial symptoms included fever, cough, dyspnea, physical pain, fatigue, nausea and diarrhea. The exclusion criteria were: (1) not being infected with COVID-19 for the first time; (2) the patient participated in other interventional clinical trials; (3) the etiological evidence showed that the patient had upper respiratory tract infection and pneumonia caused by infection with other pathogens (non-SARS-CoV-2 virus); (4) death occurred within 48 hours after admission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData of consecutive hospitalized patients at four medical centers was extracted from EMRs and put into a standard electronic database based on a pre-defined data extraction form. Among them, demographic characteristics (age and sex), date of admission and date of discharge or death, were extracted from the front page of the medical record. The initial symptoms (fever, cough, dyspnea, fatigue, diarrhea, nausea and vomiting) alongside the date, the time of admission, the clinical classification (mild or moderate, severe, critical), and past medical history (chronic respiratory disease, chronic heart disease, hypertension, and diabetes), were extracted from admission records. Patients vital signs (body temperature, respiratory rate, heart rate, blood pressure, and blood oxygen saturation), liver and kidney function, were extracted from the course records. The patients medication status during hospitalization (herbal medicine, Chinese patent medicine, antiviral drugs, antibiotics, antifungals, glucocorticoids, and immunotherapy) was extracted from the doctors order sheet.\u003c/p\u003e \u003cp\u003eAfter the clinical coordinating staff and data management associates of Peking Union Medical College Hospital, and Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, communicated with clinicians for several rounds to ensure the integrity and accuracy of the data, data verification and cleaning was carried out. Data verification included random sampling of 5% of the data to evaluate the accuracy of data extraction, and analysis of missing, contradictory or extreme values, and outliers of each variable. According to the data verification results, cleaning rules were formulated, and data cleaning was carried out. Data extraction, verification and cleaning were performed using SAS software (version 9.4). Statistical analysis and reporting of results followed the entries of the STORBE statement and the RECORD statement [20,21].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExposures\u003c/h2\u003e \u003cp\u003ePatients were divided into TCM group and control group according to whether they were exposed or not. All patients who received TCM treatment related to COVID-19 during hospitalization, with a cumulative treatment period of \u0026ge;\u0026thinsp;3 days, were defined as exposed. TCM treatment related to COVID-19 was defined as the use of any of the following interventions: (1) Chinese herbal medicine; (2) Oral Chinese patent medicine, including Lianhua Qingwen Capsule, Huoxiang Zhengqi Capsule, Shufeng Jiedu Capsule, and Jinhua Qinggan Granules; (3) Chinese patent medicine injections, including Xuebijing Injection, Tanreqing Injection, Xiyanping Injection, Xingnaojing Injection, Shenfu Injection, and Shenmai Injection. The patients who met the exposure were divided into the TCM group, and the rest of the patients belonged to the control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome of this study was the deterioration rate. Referring to \u003cem\u003ethe Diagnosis and Treatment Guideline for COVID-19\u003c/em\u003e [12], COVID-19 patients were divided into three clinical states: mild and moderate, severe, or critical.\u003c/p\u003e \u003cp\u003eIncluded in mild and moderate cases are patients exhibiting mild clinical symptoms, with no visible pneumonia on imaging.\u003c/p\u003e \u003cp\u003eIn severe cases, one of the following conditions needs to be met: ① Shortness of breath, respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;30 times/min; ② in resting state, oxygen saturation\u0026thinsp;\u0026le;\u0026thinsp;93% when inhaling air; ③ arterial partial pressure of oxygen (PaO\u003csub\u003e2\u003c/sub\u003e)/ fraction of inspiration oxygen (FiO\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;\u0026le;\u0026thinsp;300 mmHg; ④ clinical symptoms progressively worsened, and lung imaging showing that the lesions progressed significantly within 24 to 48 hours (\u0026gt;\u0026thinsp;50%).\u003c/p\u003e \u003cp\u003eIn critical cases, patients should meet any of the following conditions: ① Respiratory failure requiring mechanical ventilation; ② shock; ③ combined with other organ failure requiring ICU monitoring and treatment.\u003c/p\u003e \u003cp\u003eThe clinical status of the patient on admission was the baseline status. If a patient developed a more severe clinical state during hospitalization, the patient\u0026rsquo;s condition is judged to have deteriorated. For example, mild and moderate patients progressed to severe or critical, severe progressed to critical, or a death event occurred. The secondary outcome was all-cause mortality during hospitalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline differences between the two groups were estimated by the standard deviation (SD), with an absolute value less than 0.1 indicating a small difference between the two groups. To balance the difference in baseline between the two groups in observational studies, the inverse probability of treatment weighting (IPTW) was used to make the two groups' baselines comparable [22]. The propensity score (PS) is the conditional probability of a patient receiving TCM treatment after identifying a series of covariates that may affect the effectiveness and is calculated by a logistic regression model. According to the known clinical background knowledge and previous research results, the following factors were selected as covariates: sex, age, severity of disease at baseline, underlying diseases (chronic respiratory disease, chronic heart disease, hypertension, diabetes), days from initial symptoms to hospital visit, patient symptoms at baseline (fever, cough, dyspnea, fatigue, diarrhea, nausea and vomiting), and antiviral drugs administered during hospitalization. Baseline balance between the two groups after IPTW was estimated by the SD.\u003c/p\u003e \u003cp\u003eContinuous variables were described by means and standard deviations, or medians and quartiles. Dichotomous variables were described by frequencies and percentages. Kaplan-Meier curves were drawn for the deterioration rate and mortality in the two groups, and the Cox proportional hazards regression model was applied estimate the hazard ratio (HR) and 95% confidence interval (CI). The univariate logistic regression model was used to estimate the odds ratio (OR) values and 95% CIs for deterioration rate and mortality between the two groups on days 7, 14, and 28. Two-sided tests were used, and P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a statistically significant difference.\u003c/p\u003e \u003cp\u003eIf missing data were missing at random, multiple imputation (MI) was used to handle missing values [23]. Subgroup analyses were performed on the primary outcome measures of the whole patient dataset after IPTW processing. Subgroups were defined by the following factors: sex, age, disease severity at baseline, previous chronic heart disease, previous chronic respiratory disease, previous hypertension, previous diabetes, and days from initial symptom onset to hospital visit. In this study, the main outcome index, deterioration rate in the missing data cohort, and the cohort after using propensity score matching (PSM), were also statistically compared with the main statistical analysis results. As well, a sensitivity analysis was performed to evaluate the robustness of the results. Statistical analysis and imputation of missing values was performed using R software (version 4.2.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eDuring the period from December 19th 2019 to May 16th 2020, the four medical centers admitted a total of 4,819 hospitalized patients, of which 3,962 were diagnosed with COVID-19, accounting for 82.22% of all patients. 2424 patients met the inclusion criteria, 152 patients were excluded according to the pre-set criteria, and 2272 patients were finally included in the statistical analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The multiple imputation method was used to deal with missing values.\u003c/p\u003e \u003cp\u003eAmong the 2272 patients, 1684 were in the TCM group, with an average age of 55.13\u0026thinsp;\u0026plusmn;\u0026thinsp;15.26 years, and 746 (44.3%) were male. Detailed clinical and baseline characteristics of the two groups patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Clinical and relevant baseline characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2272)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCM group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1684)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;588)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge [mean (SD)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.52 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.13 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.65 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1051 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e746 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatus at admission (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType mild or moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1864 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1436 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428 (72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType critical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime to admission after onset (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.22 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.21 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.22 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny comorbidity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic respiratory disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic cardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny symptoms (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2059 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1516 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e543 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1507 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1537 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1143 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e393 (66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1733 (76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1334 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1079 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e866 (51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea or Vomit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e778 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e616 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs [mean (SD)]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, ℃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.9 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.7 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.1 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.1 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.0 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.6 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.1(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127.1 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.5 (10.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.5 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.6 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.9 (5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.1 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.3 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment during hospitalization (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiviral treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1710 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1328 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArbidol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1080 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e918 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOseltamivir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRibavirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLopinavir/ritonavir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterferon-α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e842 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e583 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e259 (44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1278 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1017 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoxifloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1029 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e874 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCephalosporins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e498 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e342 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntifungal treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoriconazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluconazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaspofungin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucocorticoids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e791 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e513 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e278 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman immunoglobulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the TCM group, the most commonly used TCM was Chinese herbal medicine, and the proportions used in mild and moderate, severe and critical patients were 89.83%, 89.02%, and 70.24%, respectively. Lianhua Qingwen Capsule had the highest proportion of use in oral Chinese patent medicine. For Chinese patent medicine injection, Xiyanping Injection was used in the highest proportion in mild and moderate patients, and Xuebijing Injection was used in the highest proportion in in severe and critical patients (Supplementary Table\u0026nbsp;1). The proportion of patients who used Chinese herbal medicine in combination with one Chinese patent medicine during hospitalization was the highest amongst all the different Chinese medicine combinations (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eThe control group had 588 COVID-19 patients who did not receive TCM treatment, or who received TCM\u0026thinsp;\u0026le;\u0026thinsp;2 days in total, and the medication condition was similar to the TCM group (Supplementary Table\u0026nbsp;3). There were certain differences in the characteristics of the two groups of patients at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the comparability of the two groups at baseline was achieved by the IPTW method. After IPTW adjustment, the main baseline characteristics between the TCM group and the control group were balanced (Supplementary Table\u0026nbsp;4 and Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eDuring the median hospital stay of 17 days (10, 24), a total of 500 (22.0%) patients had deterioration events, including 343 (20.4%) in the TCM group and 157 (26.6%) in the control group (Supplementary Table\u0026nbsp;5). After IPTW adjusted for all confounding factors, the results showed that the deterioration rate of the TCM group was lower than that of the control group [HR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: (0.41, 0.64), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This result was consistent across patients of varying severity at admission (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, in IPTW models adjusted for demographic confounders only [HR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: (0.42, 0.64), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and unadjusted models [HR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: (0.42, 0.63), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], TCM treatment showed the same advantage. In a sensitivity analysis of the primary outcome, all confounding factors were adjusted by using PSM (2:1 nearest neighbor matching, no replacement, caliper\u0026thinsp;=\u0026thinsp;0.2). The results still suggest that the full-course of TCM treatment reduces the HR of deterioration rates [HR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: (0.40, 0.63), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Another sensitivity analysis with direct deletion of missing data followed by IPTW yielded the same result [HR\u0026thinsp;=\u0026thinsp;0.52, 95% CI: (0.42, 0.63), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. Two sensitivity analyses confirmed the robustness of the results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Logistic regression analysis showed that the deterioration rate of the TCM group on days 7, 14 and was lower than that of the control group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Multivariable cox regression analysis of deterioration rate of patients receiving TCM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDemographic-adjusted model\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFully adjusted model\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIPTW-MI model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.42, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.42, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52 (0.41, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePSM-MI model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.42, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44 (0.35, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (0.40, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-MI model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.41, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.43, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52 (0.42, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Demographic-adjusted: controlled for age and sex as covariates\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Fully adjusted: controlled for age, sex, status at admission, time to admission after onset, comorbidity, symptoms, and antiviral treatment during hospitalization as covariates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Comparison of the clinical outcomes in day 7, 14 and 28 before the propensity score adjustment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCM group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1684)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;588)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeterioration rate (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrough day 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.39, 0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrough day 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66 (0.53, 0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrough day 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 (0.57,0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrough day 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09 (0.05, 0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrough day 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14 (0.09, 0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrough day 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23 (0.16, 0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the evaluation of all-cause mortality as a secondary outcome indicator, a total of 135 patients (5.94%) died, including 60 (3.56%) in the TCM group and 75 (12.80%) in the control group. After IPTW adjusted for all confounding factors, the results showed that the TCM group had lower all-cause mortality than the control group [HR\u0026thinsp;=\u0026thinsp;0.29, 95% CI: (0.19, 0.44), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The all-cause mortality in the TCM group was lower than that in the control group on the days 7, 14 and 28 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eIn a subgroup analysis stratified by different factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), we observed that there may be a stronger positive association between the use of TCM and the risk of deterioration events for patients visiting hospital on days 7\u0026ndash;14 after the onset of initial symptoms (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.036). In addition, compared with the control group, the TCM treatment had effects on reducing the deterioration rate in the subgroups of different sex, different severity of the disease, with or without hypertension, with or without diabetes, with or without chronic heart disease, at different times from initial symptoms to hospital visit, and the difference was statistically significant. However, for patients aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years [HR\u0026thinsp;=\u0026thinsp;0.58, 95% CI: (0.23, 1.43), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.236 ], and patients with chronic respiratory disease [HR\u0026thinsp;=\u0026thinsp;0.61, 95% CI: (0.31, 1.18), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.141], TCM treatment did reduce the deterioration rate but it was not statistically significant. In addition, in the subgroups with different severities of disease, it can be found that the more severe the patient's disease is at baseline, the greater the effect of TCM on reducing the deterioration rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSafety\u003c/h2\u003e \u003cp\u003eDuring the treatment of integrated traditional Chinese and western medicine, 6 patients (0.36%) in the TCM group and 5 patients (0.85%) in the control group developed abnormal liver function; 1 patient (0.06%) in the TCM group and 3 patients (0.51%) in the control group had abnormal renal function. No other adverse events occurred during the study. Since SARS-CoV-2 itself can also cause liver and kidney damage, the correlation between the observed adverse events and therapeutic drugs cannot be determined. However, because the overall proportion of abnormal liver function and renal function in the TCM group was not high, and was lower than that in the control group, it highlights the safety of TCM.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this multicenter retrospective cohort study of 2272 patients showed that full-course of TCM treatment can reduce the risk of deterioration events (HR\u0026thinsp;=\u0026thinsp;0.52), and death events (HR\u0026thinsp;=\u0026thinsp;0.29), in patients with COVID-19. Two sensitivity analyses confirmed the robustness of the findings. In addition, the results of subgroup analysis can preliminarily indicate that TCM treatment can reduce the deterioration rate of patients in most cases, and the more severe the patient's condition, the greater the effect of TCM may be.\u003c/p\u003e \u003cp\u003eIn previous clinical trials, the relative risk (RR) of TCM intervention on deterioration rate was in the range of 0.00-0.50 [13\u0026ndash;16]. Although TCM tends to reduce the deterioration rate, some research results are not statistically significant due to the small sample size. Meanwhile, due to the achievements of China's epidemic prevention and control, as well as medical ethics considerations, some RCTs cannot be implemented according to the established plan[24]. Therefore, the results of the previous study have certain limitations.\u003c/p\u003e \u003cp\u003eHowever, based on real-world data from a large sample, this study showed that patients in the TCM group had 0.52 times the chance of disease deterioration at the next time point compared with the control group. Considering that the control group of this study included patients who received TCM for 1 or 2 days, rather than patients who did not use TCM at all, the effectiveness of the control group could be overestimated, and the effectiveness of the TCM group could be underestimated. Therefore, the actual effectiveness of the TCM group may be better. The results of this study illustrate that TCM can be used in the clinical treatment of COVID-19 at various stages, it reduces the risk of deterioration, and is safe. The reduction in the deterioration rate also means that TCM treatment can reduce the risk of mild and moderate cases progressing to severe, and severe progressing to critical or even death.\u003c/p\u003e \u003cp\u003eIn a previous national retrospective registry study, compared with patients who did not take Qingfei Paidu Decoction, the application of Qingfei Paidu Decoction significantly reduced mortality [1.2% vs. 4.8%, HR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: (0.37, 0.66)]. This effect size is smaller than the results of this study [11]. We believe that the control group of this study (patients who did not take Qingfei Paidu Decoction) may have used other TCM interventions, thus overestimating the effectiveness of the control group and reducing the effect size of the study. But in general, the effect of TCM intervention on reducing mortality is worthy of recognition.\u003c/p\u003e \u003cp\u003eAs an acute infectious disease, COVID-19 is epidemic and contagious. It belongs to the category of \"plague\" in TCM. Most of the COVID-19 patients in Wuhan have symptoms of fever, cough, fatigue, poor appetite, and thick and greasy tongue coating. The syndrome differentiation of TCM should focus on \u0026ldquo;dampness toxin\u0026rdquo; [25]. In clinical treatment, Chinese herbal medicines for resolving dampness, clearing heat, dispelling wind and removing toxins can be applied. Oral Chinese patent medicines such as Lianhua Qingwen Capsules can be used for clearing heat and detoxifying. In severe cases, Xuebijing Injection and other Chinese patent medicine injections can also be used to assist Western medicine treatment [26]. Previous studies have found that TCM mainly plays the roles of antiviral, anti-inflammatory, immune regulation and organ protection in the treatment of COVID-19 [27\u0026ndash;29]. In view of the definite effectiveness of TCM on a variety of acute infectious diseases [8], TCM interventions should still have a certain effect on patients infected with new variant strains of COVID-19, but this needs further research and verification.\u003c/p\u003e \u003cp\u003eThis study provides new evidence for TCM treatment of COVID-19. Firstly, this study took deterioration rate and mortality as outcomes. Deterioration rate and mortality are the most important and objective outcomes in the evaluation of COVID-19. Since there is low incidence of death, preventing further deterioration is more important for most COVID-19 patients [30]. Secondly, the sample size of this study is three times the largest sample size of previous observational studies evaluating deterioration rates, which means this study also has good statistical power in subgroup analysis. Thirdly, the data for this study was derived from consecutive patients from four medical centers in Wuhan. Therefore, this study can be used as a supplement to RCTs in providing new and reliable medical evidence for TCM in the treatment of COVID-19 in a real-world setting.\u003c/p\u003e \u003cp\u003eThe results of this study still have some limitations. Firstly, since this study is an observational study, the influence of confounding factors on the results cannot be completely ruled out. However, we performed IPTW for confounders typically considered in comparative effects studies, making baseline characteristics comparable between the two groups, and the IPTW-adjusted model had a similar hazard ratio in reducing the rate of deterioration as the unadjusted model. Thus, suggesting that the reduction in the deterioration rate is mainly related to the application of TCM. Secondly, the SARS-CoV-2 strain of the patients included in this study is partially different from the currently prevalent SARS-CoV-2 Omicron variant in clinical features (such as infectivity and immune escape) [31]. However, considering the wide applicability of TCM and the exact curative effect on various variant strains, the results of this study can still provide evidence to support the current treatment of COVID-19 and new respiratory infectious diseases in the future [32]. Lastly, there is a certain clinical heterogeneity in the specific TCM interventions in the TCM group in this study. Although we describe the specific use of TCM in patients with different states, there may be differences in the effectiveness of different interventions on patients. Therefore, we can only evaluate the overall effectiveness of TCM on COVID-19. The efficacy of each specific intervention still needs further research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis retrospective study based on real-world data shows adding a full course of TCM treatment to basic conventional treatment may reduce the deterioration rate and all-cause mortality of COVID-19 patients with good safety. This result can provide evidence to support the current treatment of COVID-19 and new respiratory infectious diseases in the future. However, rigorously designed multicenter, large sample randomized controlled trials are still required in the future to evaluate the efficacy and safety of specific TCM interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI, confidence interval; COVID-19, coronavirus disease 2019; EMR, electronic medical record; HR, hazard ratio; IPTW, inverse probability of treatment weighting; MI, multiple imputation; OR, odds ratio; PS, propensity score; PSM, propensity score matching; RR, relative risk; SARS-CoV-2, syndrome coronavirus 2; SD, standard deviation; TCM, traditional Chinese medicine; WHO, World Health Organization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge clinical coordinating staff and data management associates of Peking Union Medical College Hospital and Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, and we have great thanks to all volunteers and health-care workers in the Wuhan, Hubei province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuozhen Zhao, Shiyan Yan, Bo Li, Yuhong Guo and Qingquan Liu designed the research and wrote the manuscript. Shuang Song, Yahui Hu, Yuan Du, Haitian Lu, Haoran Ye, Zhiying Ren and Lingfei Zhu governed the data. Shiqi Guo, Jing Hu, Xiaolong Xu and Rui Su analyzed the data. All authors read and approved the final manuscript. Qingquan Liu has primary responsibility for the final content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Innovation Team Project of the State Administration of traditional Chinese medicine (No. ZYYCXTD-D-202201) and the National Key Research and Development Plan of China (No. 2020YFC0861000 and No. 2021YFC1712901).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was ethically approved by the Medical Ethics Committee of Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University (2022-BL02-033-01) and has been registered with the Chinese Clinical Research Registry (ChiCTR) (ChiCTR2200062917). Since this study was a retrospective study, it did not involve contact with subjects and would not pose a significant risk to the patients who provided the data, so an application was submitted to the Medical Ethics Committee for exemption of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eBeijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Institute of Traditional Chinese Medicine, Beijing, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eBeijing University of Chinese Medicine, Beijing, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eTianjin University of Traditional Chinese Medicine, Tianjin, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. 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BMJ. 2020;370:m3379.\u003c/li\u003e\n\u003cli\u003eGeneral Office of the National Health and Health Commission, Office of the State Administration of Traditional Chinese Medicine. Notice on Issuing a New Coronary Virus Pneumonia Diagnosis and Treatment guidelines (Trial Version 9). Available from: http://www.nhc.gov.cn/yzygj/s7653p/202203/b74ade1ba4494583805a3d2e40093d88.shtml. Accessed on 5 Dec 2022.\u003c/li\u003e\n\u003cli\u003eHammond J, Leister-Tebbe H, Gardner A, Abreu P, Bao W, Wisemandle W, et al. Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19. N Engl J Med. 2022;386(15):1397-1408. \u003c/li\u003e\n\u003cli\u003eCao Y, Wang J, Jian F, Xiao T, Song W, Yisimayi A, et al. Omicron escapes the majority of existing SARS-CoV-2 neutralizing antibodies. Nature. 2022;602(7898):657-663. \u003c/li\u003e\n\u003cli\u003eWang C, Cao B, Liu QQ, Zou ZQ, Liang ZA, Gu L, et al. Oseltamivir compared with the Chinese traditional therapy maxingshigan-yinqiaosan in the treatment of H1N1 influenza: a randomized trial. Ann Intern Med. 2011;155(4):217-225.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO expert meeting on evaluation of traditional Chinese medicine in the treatment of COVID-19. Available from: https://cdn.who.int/media/docs/default-source/traditional-medicine/meeting-report---who-expert-meeting-on-evaluation-of-tcm-in-the-treatment-of-covid-192f7d2ba2-cfb8-4b00-90e3-441740cdbacb.pdf. Accessed on 5 Dec 2022.\u003c/li\u003e\n\u003cli\u003eShi SF, Liu QQ. Discussion on the value of traditional Chinese medicine in the treatment of COVID-19 from the \u0026quot;Jiangxia Cabin TCM Model\u0026quot;. Jiangsu J Tradit Chin Med. 2020;52(4):11-14.\u003c/li\u003e\n\u003cli\u003eZhang L, Zheng X, Bai X, Wang Q, Chen B, Wang H, et al. Association between use of Qingfei Paidu Tang and mortality in hospitalized patients with COVID-19: A national retrospective registry study. Phytomedicine. 2021;85:153531. \u003c/li\u003e\n\u003cli\u003eGeneral Office of the National Health and Health Commission, Office of the State Administration of Traditional Chinese Medicine. Notice on Issuing a New Coronary Virus Pneumonia Diagnosis and Treatment guidelines (Trial Version 6). Available from: http://www.nhc.gov.cn/yzygj/s7653p/202002/8334a8326dd94d329df351d7da8aefc2.shtml. Accessed on 5 Dec 2022.\u003c/li\u003e\n\u003cli\u003eXia WG, Zheng CJ, Zhang JX, Huang M, Li QL, Duan C, et al. Randomized controlled study of a diagnosis and treatment plan for moderate coronavirus disease 2019 that integrates Traditional Chinese and Western Medicine. J Tradit Chin Med. 2022;42(2):234-241.\u003c/li\u003e\n\u003cli\u003eXiao M, Tian J, Zhou Y, Xu X, Min X, Lv Y, et al. Efficacy of Huoxiang Zhengqi dropping pills and Lianhua Qingwen granules in treatment of COVID-19: A randomized controlled trial. Pharmacol Res. 2020;161:105126.\u003c/li\u003e\n\u003cli\u003eHu K, Guan WJ, Bi Y, Zhang W, Li L, Zhang B, et al. Efficacy and safety of Lianhuaqingwen capsules, a repurposed Chinese herb, in patients with coronavirus disease 2019: A multicenter, prospective, randomized controlled trial. Phytomedicine. 2021;85:153242. \u003c/li\u003e\n\u003cli\u003eZhang XY, Lv L, Zhou YL, Xie LD, Xu Q, Zou XF, et al. Efficacy and safety of Xiyanping injection in the treatment of COVID-19: A multicenter, prospective, open-label and randomized controlled trial. Phytother Res. 2021;35(8):4401-4410.\u003c/li\u003e\n\u003cli\u003eFeng J, Fang B, Zhou D, Wang J, Zou D, Yu G, et al. Clinical Effect of Traditional Chinese Medicine Shenhuang Granule in Critically Ill Patients with COVID-19: A Single-Centered, Retrospective, Observational Study. J Microbiol Biotechnol. 2021;31(3):380-386. \u003c/li\u003e\n\u003cli\u003eTian J, Yan S, Wang H, Zhang Y, Zheng Y, Wu H, et al. Hanshiyi Formula, a medicine for Sars-CoV2 infection in China, reduced the proportion of mild and moderate COVID-19 patients turning to severe status: A cohort study. Pharmacol Res. 2020;161:105127. \u003c/li\u003e\n\u003cli\u003eZhang Y, Liu Y, Li M, Feng S, Li X, Gao Z, et al. Timely treatment and higher compliance to traditional Chinese medicine: New influencing factors for reducing severe COVID-19 based on retrospective cohorts in 2020 and 2021. Pharmacol Res. 2022;178:106174. \u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495-1499. \u003c/li\u003e\n\u003cli\u003eBenchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The Reporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PloS Med. 2015;12(10):e1001885. \u003c/li\u003e\n\u003cli\u003eAustin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661-3679. \u003c/li\u003e\n\u003cli\u003eSterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.\u003c/li\u003e\n\u003cli\u003eZhao MZ, Zhao C, Tu SS, Wei XX, Shang HC. Evaluating the methodology of studies conducted during the global COVID-19 pandemic: A systematic review of randomized controlled trials. J Integr Med. 2021;19(4):317-326. \u003c/li\u003e\n\u003cli\u003eWang YG, Qi WS, Ma JJ, Ruan LG, Lu YR, Li XC, et al. A preliminary study on the clinical characteristics and syndrome differentiation of COVID-19 in traditional Chinese medicine. J Tradi Chin Med. 2020;61(4):281-285.\u003c/li\u003e\n\u003cli\u003eZhao Z, Li Y, Zhou L, Zhou X, Xie B, Zhang W, et al. Prevention and treatment of COVID-19 using Traditional Chinese Medicine: A review. Phytomedicine. 2021;85:153308. \u003c/li\u003e\n\u003cli\u003eChen J, Wang YK, Gao Y, Hu LS, Yang JW, Wang JR, et al. Protection against COVID-19 injury by qingfei paidu decoction via anti-viral, anti-inflammatory activity and metabolic programming. Biomed Pharmacother. 2020;129:110281. \u003c/li\u003e\n\u003cli\u003eHuang K, Zhang P, Zhang Z, Youn JY, Wang C, Zhang H, et al. Traditional Chinese Medicine (TCM) in the treatment of COVID-19 and other viral infections: Efficacies and mechanisms. Pharmacol Ther. 2021;225:107843. \u003c/li\u003e\n\u003cli\u003eAn X, Zhang Y, Duan L, Jin D, Zhao S, Zhou R, et al. The direct evidence and mechanism of traditional Chinese medicine treatment of COVID-19. Biomed Pharmacother. 2021;137:111267.\u003c/li\u003e\n\u003cli\u003eJin X, Pang B, Zhang J, Liu Q, Yang Z, Feng J, et al. Core Outcome Set for Clinical Trials on Coronavirus Disease 2019 (COS-COVID). Engineering (Beijing). 2020;6(10):1147-1152.\u003c/li\u003e\n\u003cli\u003eFan Y, Li X, Zhang L, Wan S, Zhang L, Zhou F. SARS-CoV-2 Omicron variant: recent progress and future perspectives. Signal Transduct Target Ther. 2022;7(1):141. \u003c/li\u003e\n\u003cli\u003eShah MR, Fatima S, Khan SN, Ullah S, Himani G, Wan K, et al. Jinhua Qinggan granules for non-hospitalized COVID-19 patients: A double-blind, placebo-controlled, and randomized controlled trial. Front Med (Lausanne). 2022;9:928468. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Traditional Chinese medicine, Deterioration rate, All-cause mortality, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-2350033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2350033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTraditional Chinese medicine (TCM) has been applied in the treatment of COVID-19 in China, but its effectiveness and safety need evaluation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multi-center retrospective cohort study was carried out, with cumulative TCM treatment period of \u0026ge;\u0026thinsp;3 days during hospitalization as exposure. Data came from consecutive inpatients in 4 medical centers in Wuhan, China. After data extraction, verification and cleaning, confounding factors were adjusted by inverse probability of treatment weighting, and the Cox proportional hazards regression model was used for statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2272 COVID-19 patients were included, including 1684 in the TCM group and 588 in the control group. Compared with the control group, the hazard ratio for the deterioration rate in the TCM group was 0.52 [95% CI: (0.41, 0.64), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. The results were consistent across patients of varying severity at admission, and two sensitivity analyses confirmed the robustness of the results. In addition, the hazard ratio for all-cause mortality in the TCM group was 0.29 (95% CI\u0026thinsp;=\u0026thinsp;0.19\u0026ndash;0.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For safety, the proportion of patients with abnormal liver function or renal function in the TCM group was smaller.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis real-world study indicates that the addition of a full course of TCM therapy to basic conventional treatment, may reduce the deterioration rate and all-cause mortality of COVID-19 patients with safety. This result can provide evidence to support the current treatment of COVID-19 and new respiratory infectious diseases in the future. Additional prospective clinical trial is needed to evaluate the efficacy and safety of specific TCM interventions.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eChiCTR, ChiCTR2200062917. Registered 23 August 2022, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chictr.org.cn/showproj.aspx?proj=171556\u003c/span\u003e\u003cspan address=\"http://www.chictr.org.cn/showproj.aspx?proj=171556\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Effect of Traditional Chinese Medicine in patients with COVID-19: A multi-center retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-15 16:27:44","doi":"10.21203/rs.3.rs-2350033/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"979d752c-0777-4c58-adda-dd900485cd81","owner":[],"postedDate":"December 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-02T08:01:24+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-15 16:27:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2350033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2350033","identity":"rs-2350033","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}