key: cord-0759613-z97up0ef authors: Yin, Xiaoxv; Xu, Xing; Li, Hui; Jiang, Nan; Wang, Jing; Lu, Zuxun; Xiong, Nian; Gong, Yanhong title: Evaluation of early antibiotic use in non-severe COVID-19 patients without bacterial infection Early antibiotic use in non-severe COVID-19 date: 2021-10-23 journal: Int J Antimicrob Agents DOI: 10.1016/j.ijantimicag.2021.106462 sha: 89dce82dd607dab9c8a901bfcad47df284e0e1b5 doc_id: 759613 cord_uid: z97up0ef Objectives The use of antibiotics was common in some countries during the early phase of COVID-19 pandemic, but adequate evaluation is so far lacking. This study aimed to evaluate the effect of early antibiotic use in non-severe COVID-19 patients admitted without bacterial infection. Methods This multi-center retrospective cohort study included 1,373 non-severe COVID-19 inpatients admitted without bacterial infection. Patients were divided into two groups according to their exposure to antibiotics within 48 hours after admission. The outcomes were progressing from non-severe type COVID-19 into severe type, length of stay over 15 days, and mortality rate. Mixed-effect Cox model and random effect logistic regression were used to explore the association between early antibiotics use with outcomes. Results During the follow-up of 30 days, the proportion of patients progressed to severe type COVID-19 in the early antibiotic use group was almost 1.4 times that of the comparison group. In the mixed-effect model, the early use of antibiotics was associated with higher probability of developing severe type and staying in the hospital for over 15 days. However, there was no significant association between early use of antibiotics and mortality. Analysis with propensity score-matched cohorts displayed similar results. In subgroup analysis, patients receiving any class of antibiotics were at increased risk for adverse health outcomes. Azithromycin did not improve the disease progression and length of stay in patients with COVID-19. Conclusions It is suggested that antibiotic use should be avoided unless absolutely necessary in non-severe COVID-19 patients, particularly in the early stages. The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly worldwide since December 2019 [1] . This pandemic has brought a major challenge to the current global health systems [2] . However, like Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome, there are no specific medicines for COVID-19 other than supportive and adjunctive therapies [3] . Even though antibiotic therapy is not recommended for patients with virus infections [4] , the use of antibiotics was common in some countries especially in the early phase of the COVID-19. A recent meta-analysis showed in the first 6 months of the global pandemic, the prevalence of antibiotic prescribing for COVID-19 patients was 74.6% [5] . The possible explanation is that the clinical symptoms of COVID-19 are similar to those of bacterial pneumonia, such as coughing, fever, and fatigue [6] . When these disease diagnoses cannot be effectively identified, clinicians usually give empirical or prophylactic antibiotic treatments for patients. In addition, most COVID-19 patients have mild clinical symptoms in the early stages. A report of 72,314 cases by the Chinese Center for Disease Control and Prevention showed that 81% of COVID-19 patients were classified as non-severe patients [7] . When non-severe patients were admitted, their specific symptoms with COVID-19 were not obvious, and laboratory confirmation could not be obtained quickly due to the limited ability of nucleic acid testing. Moreover, antigen and antibody detection reagents based on immunochromatographic techniques were still in the development stage in the early phase of the COVID-19 epidemic [8, 9] . It is anticipated that during the COVID-19 pandemic an increased number of non-severe patients may be prescribed with antibiotics for empirical or prophylactic therapy. Therefore, it is necessary to evaluate the effect of early antibiotic use in patients with COVID-19. Few studies have been conducted to evaluate the efficacy of antibiotics in patients with COVID-19 [10] . Existing research has mainly focused on specific drugs, especially azithromycin [11] [12] [13] [14] . However, the effectiveness of azithromycin remains uncertain. Gautret et al. reported the virological cure rate at day 5-6 post-inclusion among patients treated with a combination of hydroxychloroquine and azithromycin was significantly higher than hydroxychloroquine only and other control groups [15, 16] . However, in some studies, azithromycin showed no effect on lowering in-hospital mortality [11] and was even associated with increased risk of lethal arrhythmias [17] . In these studies, azithromycin was evaluated for therapeutic efficacy as a potential drug to treat SARS-CoV-2 infection [12, 13] . Few studies have evaluated the impact of empirical antibiotic use on clinical outcomes in COVID-19 patients without bacterial infection. Thus, proper evaluation of the effect of the use of antibiotics among COVID-19 patients is still needed. Based on the data of patients admitted with non-severe COVID-19, this study used a retrospective cohort design to analyze the effects of antibiotic use within 48 hours of admission on disease progression, length of stay, and mortality rate, to provide clinical evidence for the formulation of prescription and management strategies of antibiotic therapy for COVID-19 patients. This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of central institution, the Medical Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (Number: 2020IECA252). The requirement for informed consent was waived by the Ethics Committee. Only pseudonymized data with no risk of identification were used for our analyses. All authors only had access to anonymized data and had no interaction with patients or patient samples. This multi-center retrospective cohort study analyzed information on hospitalized patients with COVID-19 admitted to four hospitals in Hubei Province, China. (2) moderate cases: fever and respiratory symptoms and the chest radiology suggestive of pneumonia; (3) severe cases: cases meeting any of the following criteria: respiratory rate of at least 30 breaths per min; oxygen saturation of 93% or lower in a resting state; ratio of arterial partial pressure of oxygen and oxygen concentration no greater than 300 mm Hg; more than 50% lesion progression in lung imaging within 24 to 48h; (4) critical cases: cases meeting any of the following criteria: respiratory failure and the mechanical ventilation is needed; other organ failure; shock; or death. For this study, we grouped patients into two categories: severe (severe and critical cases) and non-severe (mild and moderate cases). Bacterial infection was defined using both clinical and laboratory data [20] [21] [22] ; three physicians March, 2020. Exclusion criteria were: 1) patients discharged within 24 hours of admission; 2) cases that became severe within 48h of admission; 3) patients with bacterial infection within 48h of admission; 4) patients who received antibiotics within 48 hours of admission, but whose medication course was <3 days. The demographic information, clinical symptoms, medical history, in-hospital medication, and clinical outcomes were obtained from the electronic medical system. Laboratory data (white blood cell count, PCT, CRP, aspartate aminotransferase, alanine aminotransferase, albumin, albumin/globulin ration, serum creatinine, blood urea, uric acid, D-Dimer) were collected from the laboratory information system. The personal identification information including name and ID was anonymized and a new study ID was generated for each patient. [12, 14] , the effect of azithromycin was also analyzed in a subgroup analysis. The study outcomes were: 1) progression from non-severe type COVID-19 into severe type; 2) length of stay >15 days; 3) all-cause death during 30 days of in-hospital follow-up. Data are presented as the medians and interquartile ranges (IQRs), or numbers and percentages (%), as appropriate. Comparison of parameters between two groups were conducted with the Wilcoxon-Mann-Whitney-Test for continuous variables. For categorical variables, Pearson's χ 2 test or Fisher's exact tests were used. The risk of outcomes of interest was calculated by the Cox proportional hazard model if hazard curves for the EAU and NEAU groups were proportional (determined by the Kaplain-Meier curve) or Logistic regression. Site was modeled as a random effect in the mixed-effect Cox model and random effect logistic regression. Factors associated with disease severity were adjusted in the multivariate analysis, including basic demographic characteristics (age and gender), symptoms (cough and fever), comorbidities (hypertension, diabetes, malignancy, COPD, coronary heart disease), and treatments (antiviral drugs, NSAID, steroids, immunomodulators and biologics). The following variables were included for propensity score matching: basic demographic characteristics (age and gender), symptoms (cough and fever), vital signs (DBP and respiratory rate), comorbidities (hypertension, diabetes, malignancy, COPD), treatments (antiviral drugs, NSAID, steroids, immunomodulators and biologics), lab data on admission (white blood cell count, lymphocyte percentage, and C-reaction protein). Two cohorts were matched at a ratio of 1:1 with a caliper width of 0.15. The balance among covariates was evaluated by estimating the standardized differences between two groups before and after matching. Only those with absolute value < 0.1 were considered as qualified matching. A sensitivity analyses was conducted to evaluate the robustness of propensity score-matched cohort analyses. This additional analysis of the full datasets included the propensity score as a continuous variable in the model to adjust for differences in patient characteristics. For all comparisons, differences were tested using two-tailed tests and p-values less than 0.05 were considered statistically significant. The difference-in-difference (DID) methodology was employed to evaluate the impact of antibiotic use on liver function, kidney function, and fibrinolytic activity, while controlling for confounding factors in linear regression analysis. The model was also adjusted for basic demographic characteristics (age and gender), symptoms (cough and fever), comorbidities (hypertension, diabetes, malignancy, COPD, coronary heart disease), and treatments (antiviral drugs, NSAID, steroids, immunomodulators and biologics). All analyses were performed using SAS 9.4 (by SAS Institute Inc., Cary, NC, USA). Compared with the NEAU group, the EAU group had higher prevalences of cough and fever, but lower prevalences of hypertension, diabetes and coronary heart diseases. A higher proportion of the EAU group received antiviral therapies (95.35% vs. 81.13%; P < 0.0001). After propensity score matching, the two cohorts were balanced with no significant difference existed ( Table 1 ). The standard mean differences shrank within the range of ± 0.1, indicating good balance (Supplementary Figure 1) . Among the patients in EAU group, a total of eight antibiotic classes were received. The most frequently prescribed class of antibiotics was fluoroquinolones (n=573, 76.10%), followed by cephalosporins (n=191, 25.37%) and penicillins (n=89, 11.82%) (Supplementary Table 1 During the 30-day follow-up, 375 of the 1,373 patients admitted with non-severe type COVID-19, 375 patients progressed into severe type. The proportion of patients progressing to severe type of COVID-19 in the EAU group was higher than that of the NEAU group (31.74% vs 21.94%; P<0.0001) ( Table 1 ). In the mixed-effect Cox model treating site as a random effect, the early use of antibiotics was associated with higher probability of developing into severe type (adjusted HR=1.555, 95% CI: 1.227-1.970) (Figure 2A, Table2) . Table 3) . The average length of stay in EAU group was 17.8 days (SD=7.8) and NEAU group 14.6 days (SD=7.2) (P<0.0001) ( Table 1 ). In the mixed-effect model, antibiotic use was associated with higher risk of staying in the hospital for over 15 days (adjusted OR=1.827, 95% CI: 1.389-2.403). In propensity score-matched cohorts analysis, higher risk was also observed for patients administered with antibiotics within 48 hours after admission (adjusted OR=1.784, 95% CI: 1.279-2.487) (Table2). The sensitivity analysis of the full datasets showed consistent results with the results of propensity score-matched cohorts (adjusted OR= 1.664, 95% CI: 1.244-2.227) ( Table 3 ). There was no evidence of improved health outcomes in patients receiving any Table 4 ). The case number was too low to allow assessment of azithromycin on survival. After adjusting for covariates, the DID estimator was negative and statistically significant at the 1% level in albumin, and it was positive and statistically significant in D-Dimer. The albumin decreased by 2.5g/L in EAU group and by 1.4g/L while in the NEAU group, respectively. The D-Dimer increased by 1.7μg/mL FEU in EAU group and decreased by 0.1μg/mL FEU in NEAU group (Supplementary Table 5A ). However, the effect of early antibiotic use on blood examination indicators was not significant in propensity score-matched cohorts (Supplementary Table 5B ). Our study found that early use of antibiotics in non-severe COVID-19 patients without bacterial infection was associated with progression of COVID-19 from non-severe into severe and longer length of stay, and with no effect on 30-day survival. Subgroup analysis showed that this held for all antibiotic types, including azithromycin. Antibiotic use was common among COVID-19 patients in the early phase of the pandemic [5] . One possible explanation for the adverse impact of antibiotics would be dysbiosis that occurs within four to seven days of antibiotic use [23, 24] . Considering that the length of stay of patients with early use of antibiotics in this study was 17.8 days, dysbiosis is certainly temporally feasible. Microbial balance has a positive impact on lung immunity. The intestine microbiome can activate G protein-coupled receptors to inhibit the inflammation in the lung via its metabolisms like short-chain fatty acids to reduce lung injury [25] [26] [27] . Antibiotic use, by affecting the composition and function of microbiome, has been proven to undermine the lung's ability to clear pathogens and make lung more vulnerable to virus infection [28] [29] [30] [31] . Previous studies have suggested that antibiotic treatment without bacterial infection could cause a cytokine storm and a septic shock-like picture [32] [33] [34] [35] [36] . This release of pro-inflammatory cytokines may promote immune dysfunction, tissue damage, and increase susceptibility to infection [37] . In the context of COVID-19, antibiotic exposure by adding to release of pro-inflammatory cytokines, could feasibly be associated with higher risk of progression from non-severe to severe type. Our finding that azithromycin was not associated with either lower risk of progression to severe types or reduced length of stay is consistent with a multicenter retrospective cohort study in New York [11] . A Spanish observational study also found no significant association between azithromycin and length of stay in patients with COVID-19 [38] . Moreover, a recent meta-analysis of randomized clinical trials also showed no benefit from azithromycin use on length of stay [39] . We also observed average changes in the decrease of albumin and increase of D-Dimer were greater in the EAU than NEAU group. This may again be an impact of increased release of pro-inflammatory factors by antibiotics [33] [34] [35] . Several pro-inflammatory factors such as IL-6 and TNF-α can inhibit synthesis of albumin in the liver [40] [41] [42] . Decreased level of albumin is involved in the regulation of coagulation function [43] . Besides, pro-inflammatory factors can also lead to systemic inflammatory response syndrome (SIRS) and subsequent damage to the vascular system and extensive microthrombosis [44] [45] [46] . Elevation of D-Dimer indicates a hypercoagulable state in patient with COVID-19 [47] . However, the differences in the changes of albumin and D-Dimer were not significant in the propensity score-matched cohorts. Further investigation in large clinical trials is needed to assess the influence of antibiotics on liver/renal function and coagulation among COVID-19 patients. Our study does have some limitations. First, we were unable to retrieve details of pre-admission self-medication with antibiotics. Second, due to limited test availability during the early stages of the pandemic, not all patients in this study had their diagnosis of COVID-19 confirmed virologically. Third, the sample size was too small to reliably determine whether there was any association between antibiotic use and survival. Fourth, as a single-locality study we cannot exclude the possibility of regional or ethnic differences. Fifth, obesity is recognized to be associated with increased risks of unfavorable clinical outcomes, including death, in COVID-19 patients [48] [49] [50] [51] . Although BMI was not addressed directly in our study, obesity-related comorbidities like diabetes were adjusted in the analysis to partially mitigate the effect of obesity on outcome. Sixth, even though a commonly used official definition of severe cases was adopted by this study, some previous studies used admission to ICU as a standard [52, 53] . Seventh, although we adopted propensity score matching and additional propensity score matching analysis to balance the disease severity between EAU and NEAU group, we could only adjust for known and measurable confounders. Thus, it was not possible to completely eliminate selective bias in the antibiotics given to patients by physicians caused by severity of disease. Further randomized clinical trials are needed to verify the impact of early antibiotic use in non-severe patients with COVID-19. This study found that early use of antibiotics among non-severe COVID-19 patients was significantly associated with the risk of progression from non-severe to severe disease, and with prolonged hospitalization. We also found that azithromycin use was not associated with better clinical outcomes. We suggest that antibiotic use should be NEAU=Non-early antibiotic use; EAU=Early antibiotic use; OR=odds ratio; CI=confidence interval; HR=hazard ratio a Propensity score matched was conducted to adjust for basic demographic characteristics (age and gender), symptoms (cough and fever), vital signs (DBP and respiratory rate), comorbidities (hypertension, diabetes, malignancy, COPD), treatments (antiviral drugs, NSAID, steroids, immunomodulators and biologics), lab data on admission (white blood cell count, lymphocyte percentage, and C-reaction protein). b Crude estimations were adjusted for basic demographic characteristics (age and gender), symptoms (cough and fever), comorbidities (hypertension, diabetes, and coronary heart disease, malignancy, COPD), treatments (antiviral drugs, NSAID, steroids, immunomodulators and biologics). c Site (hospital) was modeled as a random effect in the multivariate analyses. 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