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  • Systematic Review
  • Open Access

25 June 2021

Coinfections with Bacteria, Fungi, and Respiratory Viruses in Patients with SARS-CoV-2: A Systematic Review and Meta-Analysis

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1
Administration of Pharmaceutical Care, Al-Ahsa Health Cluster, Ministry of Health, Al-Ahsa 31982, Saudi Arabia
2
Research Center, Almoosa Specialist Hospital, Al-Ahsa 36342, Saudi Arabia
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College of Nursing, Princess Norah Bint Abdul Rahman University, Riyadh 11564, Saudi Arabia
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School of Nursing, University of Wollongong, Wollongong, NSW 2522, Australia
Pathogens2021, 10(7), 809;https://doi.org/10.3390/pathogens10070809
This article belongs to the Section Viral Pathogens
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Abstract

Background: Coinfection with bacteria, fungi, and respiratory viruses in SARS-CoV-2 is of particular importance due to the possibility of increased morbidity and mortality. In this meta-analysis, we calculated the prevalence of such coinfections. Methods: Electronic databases were searched from 1 December 2019 to 31 March 2021. Effect sizes of prevalence were pooled with 95% confidence intervals (CIs). To minimize heterogeneity, we performed sub-group analyses. Results: Of the 6189 papers that were identified, 72 articles were included in the systematic review (40 case series and 32 cohort studies) and 68 articles (38 case series and 30 cohort studies) were included in the meta-analysis. Of the 31,953 SARS-CoV-2 patients included in the meta-analysis, the overall pooled proportion who had a laboratory-confirmed bacterial infection was 15.9% (95% CI 13.6–18.2, n = 1940, 49 studies, I2 = 99%, p < 0.00001), while 3.7% (95% CI 2.6–4.8, n = 177, 16 studies, I2 = 93%, p < 0.00001) had fungal infections and 6.6% (95% CI 5.5–7.6, n = 737, 44 studies, I2 = 96%, p < 0.00001) had other respiratory viruses. SARS-CoV-2 patients in the ICU had higher co-infections compared to ICU and non-ICU patients as follows: bacterial (22.2%, 95% CI 16.1–28.4, I2 = 88% versus 14.8%, 95% CI 12.4–17.3, I2 = 99%), and fungal (9.6%, 95% CI 6.8–12.4, I2 = 74% versus 2.7%, 95% CI 0.0–3.8, I2 = 95%); however, there was an identical other respiratory viral co-infection proportion between all SARS-CoV-2 patients [(ICU and non-ICU) and the ICU only] (6.6%, 95% CI 0.0–11.3, I2 = 58% versus 6.6%, 95% CI 5.5–7.7, I2 = 96%). Funnel plots for possible publication bias for the pooled effect sizes of the prevalence of coinfections was asymmetrical on visual inspection, and Egger’s tests confirmed asymmetry (p values < 0.05). Conclusion: Bacterial co-infection is relatively high in hospitalized patients with SARS-CoV-2, with little evidence of S. aureus playing a major role. Knowledge of the prevalence and type of co-infections in SARS-CoV-2 patients may have diagnostic and management implications.

1. Introduction

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and was first described in Wuhan, China in 2019. Globally, as of 15 April 2021, there have been 137,866,311 confirmed cases of COVID-19, including 2,965,707 deaths, as reported by the World Health Organization [1]. Coinfection with SARS-CoV-2 and other bacterial, fungal, and respiratory viral pathogens [2,3,4], Gram-positive and Gram-negative bacteria [5,6,7], Middle East respiratory syndrome coronavirus (MERS-CoV) [8], and influenza [9,10,11,12,13] has been described. However, the reported frequency is variable. Such coinfections in patients with SARS-CoV-2 may be a cause of increased morbidity and mortality [2,6,7,14,15,16,17,18,19,20,21,22]. Thus, timely diagnosis is important to initiate appropriate therapy and limit the overuse of antimicrobial agents. Previous studies, including case series [2,5,8,11,14,15,16,19,20,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50], cohort studies [3,4,6,7,9,10,12,13,17,18,21,22,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70], and meta-analyses [71,72,73], have shown variable results. In light of recent studies evaluating coinfections in SARS-CoV-2 patients, we aimed to re-evaluate the prevalence of bacterial, fungal, and respiratory viral coinfections in a comprehensive meta-analysis. Moreover, we aimed to identify the risk-factors, characteristics, and consequences associated with SARS-CoV-2 coinfection.

2. Methods

2.1. Design

This is a meta-analysis and was conducted per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA] guidelines [74]. We searched PROQUEST, MEDLINE, EMBASE, PUBMED, CINAHL, WILEY ONLINE LIBRARY, and NATURE for full texts. Search keywords included Coronavirus infection OR SARS coronavirus OR severe acute respiratory syndrome OR COVID OR SARS AND mixed infection OR bacterial pneumonia OR bacteremia OR bacterial infection OR fungal infection OR viral infection OR respiratory infection OR mycosis OR coinfect OR co-infect OR concomitant infect OR concurrent infection OR mixed infect OR coinfection OR co-infection. The search included English language studies from 1 December 2019 to 31 March 2021. Then, articles were kept if the title and abstract contained discussion about bacterial, fungal, and/or respiratory viral co-infection in SARS-CoV-2 patients. In addition, we used manual backward snowballing of the bibliographies of retrieved articles to include additional relevant articles.

2.2. Inclusion and Exclusion Criteria

The included articles were pertinent if these articles included patients with a positive SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) test of any age and a described co-infection on presentation or developed during the course of the disease or during hospital stay. These cases were retained if bacteria, fungi, and/or viruses were detected in the respiratory tract or blood culture samples and were excluded if they were identified from other samples. We aimed to include randomized controlled trials, cohort studies, and case series, and excluded other types of studies.

2.3. Data Extraction

Three authors (S.A., A.A., and J.A.) reviewed the retrieved studies and chose relevant articles. Data were extracted using key headings as indicated in Table 1. The study designs were classified as well. The extracted information included: authors; study location; study design and setting; publication year; number of SARS-CoV-2 patients tested for co-pathogens; number of coinfected patients; age; proportion of male patients; percentage of patients requiring intensive care unit (ICU) and mechanical ventilation; mortality rates; proportion of patients with bacterial, fungal, and/or respiratory viral coinfections; total organisms identified; antimicrobials prescribed; laboratory techniques for co-pathogen detection; assessment of study risk of bias; and remarks on notable findings.
Table 1. Summary of the characteristics of the included studies with evidence on SARS-CoV-2 and bacterial, fungal, and/or respiratory viral co-infections (n = 72), 2020–2021.

2.4. Quality Assessment

The Newcastle–Ottawa Scale [NOS] was the primary tool for examining the quality of included studies, as described previously [75]. The tool provides maximum scores of 4 for selection, 2 for comparability, and 3 for exposure/outcome. High-quality studies have a score of >7, and moderate-quality studies have a score of 5–7. Quality assessment was performed independently by four authors (A.M.A., S.A.A., G.Y.A., and A.R.) and a consensus was used to resolve any disagreement.

2.5. Data Analysis

We examined primarily the proportion of confirmed acute bacterial, fungal and/or respiratory viral infections in patients with SARS-CoV-2. This proportion was further classified based on initial presentation or during the course of the illness. Taking a conservative approach, a random effects with the DerSimoniane–Laird model was used [76], which produces wider confidence intervals [CIs] than a fixed effect model. Results were illustrated using forest plots. The Cochran’s chi-square (χ2) and the I2 statistic provided the tools of examining statistical heterogeneity [77]. An I2 value of >50% suggested significant heterogeneity [78]. Examining the source of heterogeneity, a subgroup analysis was conducted based on ICU and non-ICU admission or only ICU admission. Funnel plots and Egger’s correlation test estimate publication bias and p value < 0.05 indicates statistical significance [79]. R version 4.1.0 with the packages metafor and meta was used for all statistical analyses.

3. Results

3.1. Characteristics and Quality of Included Studies

Of the initial 7317 retrieved publications, there were 4609 duplicate articles, and 2080 articles were found to be irrelevant based on their titles and abstracts and were excluded. An additional 1065 articles were excluded after review, meaning that we included 72 articles in the systematic review [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,80,81,82], while 68 articles were included in the meta-analysis [2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,39,40,41,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,59,60,61,62,63,64,65,66,67,68,69,70,80,81,82] (Figure 1).
Figure 1. Flow diagram of literature search and data extraction from studies included in the systematic review and meta-analysis.
The included studies had a total of 31,953 SARS-CoV-2 infected patients as detailed in Table 1. Of those patients, 25,302 (79.2%) were from 32 cohort studies and 20.8% were from 40 case series. The geographical distribution of these studies was as follows: Asia (n = 36), Europe (n = 22), and North America (n = 14). The majority of the studies were single center and only 24 studies were multi-center. Laboratory techniques for co-pathogen detection within studies included 19 that used respiratory samples and RT-PCR tests [4,5,8,11,12,13,29,33,37,38,53,55,58,59,62,63,66,70,80], 17 that used serologic tests (antibodies) [6,10,14,19,24,31,32,35,36,43,44,45,50,52,60,64,68], 15 that used RT-PCR tests with respiratory and/or blood cultures [7,9,17,18,23,26,28,34,39,42,51,56,57,65,67], 12 that did not specify their testing methods [3,15,16,22,25,30,40,41,46,47,49,81], five that only used respiratory and/or blood cultures [2,21,48,54,61], and three that tested both serology and RT-PCR [27,69,82] (Table 1). Seven studies examined patients for influenza A and B only [10,11,19,41,60,68,70]; while five studies evaluated patients for the presence of Chlamydia or Mycoplasma [6,24,35,52,82]; and four studies only evaluated for the presence of fungi [17,23,39,42]. The proportion of patients receiving antibiotic agents was reported in 34 studies [2,6,7,14,16,17,18,19,20,21,23,24,31,34,35,36,37,39,40,42,43,45,46,48,49,51,52,56,57,60,64,70,80,82]. The most commonly used antimicrobials were macrolides (n = 355), 2nd/3rd/5th generation cephalosporins (n = 157), fluoroquinolones, (n = 150), antifungals (n = 62), beta-lactams/beta-lactam inhibitors (n = 26), beta-lactams (n = 21), tetracyclines (n = 17), linezolid (n = 13), carbapenems (n = 4), and glycopeptides (n = 2). The median NOS score was 6 with a range from 5 to 8. The NOS quality was moderate for 66 studies, and high quality for 6 studies. The majority (60/72, 83.3%) of the studies included only adult patients. The proportion of male patients had a median of 55.9% [interquartile range (IQR) 48.9–71.9%]. The majority (n = 58) of the studies included any hospitalized patient, and 14 studies included only critically ill. Sixteen, thirteen, and four studies exclusively reported on respiratory viral, bacterial, and fungal co-infections, respectively; and the remaining 39 studies reported on bacterial, fungal, and respiratory viral co-infections; Table 1.

3.2. Meta-Analysis of Bacterial, Fungal, and Respiratory Viral Co-Infections in Patients with SARS-CoV-2

The overall pooled proportions of SARS-CoV-2 patients who had laboratory-confirmed bacterial, fungal, and respiratory viral coinfections were 15.9% (95% CI 13.6 to 18.2, n = 1940, 49 studies, I2 99%, p < 0.00001), 3.7% (95% CI 2.6 to 4.8, n = 177, 16 studies, I2 93%, p < 0.00001), and 6.6% (95% CI 5.5 to 7.6, n = 737, 44 studies, I2 96%, p < 0.00001), respectively; (Figure 2,Figure 3,Figure 4).
Figure 2. Forest plot of proportion of SARS-CoV-2 patients with bacterial co-infections (all patients in the upper panel and only ICU patients in the lower panel).
Figure 3. Forest plot of proportion of SARS-CoV-2 patients with fungal co-infections (all patients in the upper panel and only ICU patients in the lower panel).
Figure 4. Forest plot of proportion of SARS-CoV-2 patients with respiratory viral co-infections (all patients in the upper panel and only ICU patients in the lower panel).
In bacterial coinfected SARS-CoV-2 patients, subgroup analysis showed some difference in the rates between all patients (ICU and non-ICU group); and the ICU only group (14.8% (95% CI 12.4 to 17.3, n = 1802, 41 studies, I2 = 99%); and 22.2% (95% CI 16.1 to 28.4, n = 137, 8 studies, I2 = 88%), respectively); Figure 2. In the fungal co-infected SARS-CoV-2 patients, subgroup analysis showed a significant difference in the rates between all patients (ICU and non-ICU); and ICU only patients [2.7% (95% CI 0.0 to 3.8, n = 155, 8 studies, I2 = 95%); and 9.6% (95% CI 6.8 to 12.4, n = 62, 8 studies, I2 = 74%), respectively]; Figure 3.
However, in the respiratory viral co-infected SARS-CoV-2 patients, subgroup analysis showed an identical proportion between all patients (ICU and non-ICU) and the ICU only patients [6.6% (95% CI 5.5 to 7.7, n = 723, 40 studies, I2 = 96%); and 6.6% (95% CI 0.0 to 11.3, n = 14, 4 studies, I2 = 58%), respectively]; Figure 4.
Funnel plots for possible publication bias for the pooled effect size to determine the prevalence of coinfections in SARS-Cov-2 patients appeared asymmetrical on visual inspection, and Egger’s tests confirmed asymmetry with p values < 0.05; Figure 5, Figure 6 and Figure 7.
Figure 5. Funnel plots evaluating publication bias for the pooled effect size determining the prevalence of bacterial co-infections in SARS-Cov-2 patients.
Figure 6. Funnel plots evaluating publication bias for the pooled effect size to determine the prevalence of fungal co-infections in SARS-Cov-2 patients.
Figure 7. Funnel plots to evaluate publication bias for the pooled effect size to determine the prevalence of other respiratory viral co-infections in SARS-Cov-2 patients.

3.3. Bacterial, Fungal and Respiratory Viral Co-Pathogens

Specific bacterial co-pathogens were reported in 49/72 (68%) studies, which is about 57.3% of the reported co-infections. The most common bacteria were S. aureus (n = 1095), M. catarrhalis (n = 352), M. pneumoniae (n = 338), S. pneumoniae (n = 316), C. pneumoniae (n = 261), K. pneumoniae (n = 259), and H. influenzae (n = 197) (Table 2).
Table 2. Proportion of all identified SARS-CoV-2 bacterial co-infections (N = 3468).
Fungal co-pathogens were reported in 16/72 (22.2%) studies, which is equal to only 3.2% of the reported co-infections. The most common fungal organisms were Aspergillus spp. (n = 68), Aspergillus fumigatus (n = 43), Other Candida spp. (n = 29), Candida albicans (n = 25) and Aspergillus flavus (n = 10) (Table 3).
Table 3. Proportion of all identified SARS-CoV-2 fungal co-infections (N = 192).
Respiratory viral co-pathogens were reported in 44/72 (61.1%) studies, representing about 39.5% of the reported co-infections. The most common respiratory viruses were EBV (n = 644), HHV6 (n = 574), Influenza A virus (n = 355), HMPV (n = 328), and Adenovirus (n = 144) (Table 4).
Table 4. Proportion of all identified SARS-CoV-2 respiratory viral co-infections (N = 2392).

4. Discussion

In this large systematic review and meta-analysis, we included 31,953 patients with laboratory-confirmed SARS-CoV-2 from 72 observational studies in order to estimate the prevalence of coinfections with bacterial, fungal, and respiratory viral pathogens. This study showed the following microbial coinfection prevalences: bacterial (15.9%, 95% CI 13.6–18.2); fungal (3.7%, 95% CI 2.6–4.8); and respiratory viral (6.6%, 95% CI 5.5–7.6) coinfections. Bacterial and fungal coinfections were more common in ICU patients ((22.2%%, 95% CI 16.1–28.4) and (9.6%, 95% CI 6.8–12.4), respectively) than mixed ICU and non-ICU patients, as expected. However, respiratory viral co-infection rate in SARS-CoV-2 patients was identical in both groups (6.6%, 95% CI 0.0–11.3). Nevertheless, the included studies in this meta-analysis are case series and cohort studies and we did not identify any randomized controlled trials addressing this issue. In addition, the included studies comprised only admitted patients, which may skew the findings and should not be generalized to all SARS-COV-2 patients. Non-admitted COVID-19 patients were not represented in these studies and thus the exact prevalence of coinfections could not be calculated for all SARS-CoV-2 infected patients [83,84,85]. The findings in this meta-analysis showed different results from previous systematic meta-analyses that evaluated coinfections among COVID-19 patients [71,72,73]. We reported a higher prevalence of coinfections in hospitalized SARS-CoV-2 patients. The current meta-analysis is more comprehensive and included a total of 71 studies [2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,80] and one abstract [3], including a total of 31,953 patients. The inclusion of 18 recently published studies [2,3,5,6,7,8,9,10,12,13,14,22,24,27,41,62,64,65] contributed to the refinement of the estimate of the pooled prevalence of pathogens contributing to coinfections in SARS-CoV-2 patients.
In this meta-analysis, bacterial coinfection was more prevalent than fungal and other respiratory viruses. This finding may reflect high rates of antimicrobial use for admitted patients with SARS-CoV-2 infection to treat documented or presumed bacterial co-infections. Thus, it is important to study the occurrence, type, and intended antimicrobial agent use in SARS-COV-2 patients in order to develop additional strategies for the optimal use of antimicrobial agents in this population. As expected, bacterial, fungal, and other respiratory viral co-infections in SARS-CoV-2 patients were more frequent in ICUs compared with non-ICU locations [2,20,28,57], a finding which has previously been described in systematic reviews [71,72] and may reflect the epicenter role of ICUs in both infections and antimicrobial resistance. One of the reasons for the increase in infection rate in ICUs could be due to the simultaneous infection of the virus and bacterium. Viruses can facilitate the attachment and colonization of the bacteria in the respiratory tract, which is certainly no exception for SARS-CoV-2 [86]. Nevertheless, other factors such as ICU type, used equipment rate, admission or discharge criteria, high workload or nurse ratio, etc. can also affect the quality of care and the rate of ICU-acquired, healthcare-associated infections [87,88]. With observed strains currently being placed on healthcare systems during the upstroke of the SARS-CoV-2 pandemic, guidelines must focus on the maintenance of good knowledge and compliance of infection prevention and control [89], antimicrobial stewardship [90], and robust surveillance for healthcare-associated infections and antimicrobial resistance [91,92].
The most common method used to detect co-infections in the studies included in this review was RT-PCR tests for respiratory samples. The choice of diagnostic test for pathogens depends in part upon test availability and how soon the results are needed. If available, molecular assays (RT-PCR or, alternatively, a rapid molecular assay) are preferred over antigen detection tests (e.g., direct and indirect immunofluorescence assays) because molecular tests are the most sensitive [93]. Nevertheless, positive RT-PCR tests might indicate recently resolved infection or colonization [94,95]. In addition, many studies evaluated serological (antibodies) tests with this method detecting co-infections in SARS-CoV-2 patients. Application of serologic laboratory technique for co-pathogens detection across all studies was likely to reveal an even higher overall co-infection proportion than found in our study. Consecutively, it is possible that positive serology indicated recent and not acute infection in included patients [96]. Serologic testing is useful primarily for research purposes and antibody-based tests might produce false negative results during the window period. It is worthwhile to mention that administration of broad-spectrum antimicrobials to a large percentage of the patients included in this review might relatively have lowered the sensitivity of microbial culture methods, which could have resulted in underestimation of the true numbers of co-infections.
Specific co-infecting pathogens in SARS-CoV-2 patients were identified in this study from the 72 included studies. In line with the previous systematic reviews and meta-analyses [71,72], M. pneumoniae, K. pneumoniae, and H. influenzae were among the predominant co-pathogens. However, in this meta-analysis, S. aureus was the most common bacterial pathogens co-infecting SARS-CoV-2 patients. However, this finding needs to be carefully interpreted, as 85.6% of all S. aureus co-pathogens in our review were reported by one study [58]. S. aureus infections are a known complication of other viral pandemics, such as the Spanish flu and the H1N1 influenza pandemic [97,98]. S. aureus is known to act synergistically in SARS-CoV-2 patients, increasing mortality and severity of disease [38,99]. The proposed mechanisms of viral-induced S. aureus co-infections include viral modification of airway structures and increased adherence of the organism to respiratory mucosa, as well as initiation of immune-suppressive responses [22,100,101]. Further investigations are necessary to confirm an association between SARS-CoV-2 infection and susceptibility to S. aureus coinfections.
It was noted that male patients with SARS-CoV-2 were more likely to have coinfections than female [13]. However, patients with pneumococcal pneumoniae and SARS-CoV-2 were mostly females [24]. Older age appears to be the major risk factor associated with coinfections with bacteria and respiratory viruses [12,38,43,58,62] and fungi [39]. This might be attributed mainly to the differences in the inclusion criteria and the population age groups included in the studies, or it could be explained by the gender-based biological differences in the host immune response to COVID-19 infection [102]. The age-dependent defects in T-cell and B-cell function and the excess production of type 2 cytokines could lead to a deficiency in control of viral replication and more prolonged proinflammatory responses, potentially leading to poorer outcomes [103]. Yet, SARS-CoV-2 patients of any age may develop such coinfections and experience severe disease, especially in those with comorbidities, even in young people [4,53], children [27,49], and infants [40].
A few underlying comorbidities were associated with increased risk of coinfections, and these included obesity [8,12,38], cancer, hepatitis, and kidney disease [12,43]. Laboratory abnormalities that have been described in SARS-CoV-2 patients with bacterial and respiratory viral coinfections were high procalcitonin [47,50,64,80], d-dimer [9], and monocytes [31]; and low neutrophils [31]. Some conclusions could be drawn from available data as to whether patients who have a concurrent bacterial, fungal, and/or respiratory viral infection have a worse prognosis than those in whom SARS-CoV-2 is the only detected pathogen. Mortality in SARS-CoV-2 patients was increased due to bacterial [2,6,14,21], fungal [2,17,20,21], or respiratory viral [20] co-infections compared to SARS-CoV-2 patients with no co-infections. Few studies observed no increase in mortality in COVID-19 patients compared to those who did not have bacterial [3,22,24,35,66], fungal [3,22], or other respiratory viral [66] coinfections. Clinical presentation, laboratory results, radiological findings, and outcome are likely to differ between SARS-CoV-2 positive patients with and without co-infections. Bacterial coinfection increased SARS-CoV-2 patients’ hospital length of stay [18,50], need for ventilatory support [6,28], ARDS [28], shock [28], multi-organ injury [23,32], and caused more severe COVID-19 disease [2,21,28,33,34,53,68]. Two studies reported conflicting results on the role of bacterial [24,36] or respiratory viral [36] coinfection in relation to increasing length of hospital stay or ICU admission [22,24,35]. It was shown that the patterns of SARS-CoV-2 symptoms and clinical outcomes were not different in the bacterial [27] and respiratory viral [10,11,27,66,70] co-infected patients. The severity and time of SARS-CoV-2 disease clearance were not different in patients with respiratory viral co-infections [19,36].
The data on the timing of the occurrence of co-infection was variable. The occurrence of co-infections has a median time of 4–11.5 days (IQR 2–42) of ICU admission [2,17,42]. Bacterial co-infection was infrequent within 2–4 days of hospital admission [22,26]. Nonetheless, considering the high number and severity of bacterial co-infections previously reported in patients with SARS-CoV-2, initiation of antibiotic therapy for all hospitalized patients with COVID-19 is recommended [7]. The approach of administering empiric antibiotic therapy solely to patients who were admitted for SARS-CoV-2 and who presented with a chest X-ray suggestive of bacterial infection, have a need for direct ICU admission, or are severely immunocompromised should be reconsidered. When bacterial co-infection in SARS-CoV-2 patients is suspected, an antibiotic approach with optimal S. aureus coverage, such as ceftaroline, ceftriaxone, or cefazolin plus levofloxacin, is recommended in areas with methicillin-sensitive S. aureus prevalence [104].

Limitations

The main limitation of this meta-analysis is that included studies were observational with no randomized controlled trials; and there was no standardized microbiologic testing at specified intervals. In interpreting funnel plots, the different possible reasons for funnel plot asymmetry should be distinguished. Possible sources of asymmetry in funnel plots might be the wide differences between the included populations in the different studies, publication bias and selective outcome and/or analysis reporting, poor methodological design and inadequate analysis, or asymmetry might have occurred by chance. Furthermore, the analysis was limited to the English literature and thus may miss other studies published in other languages.

5. Conclusions

Bacterial co-infection is relatively high in hospitalized patients with SARS-CoV-2, with little evidence of S. aureus having a major role. Empiric antibiotic therapy should be considered in SARS-CoV-2 patients who present with a chest X-ray suggestive of bacterial infection, the need for direct ICU admission, or a severely immunocompromised condition. Knowledge of the prevalence and type of co-infections in SARS-CoV-2 patients may have diagnostic and management implications.

Author Contributions

S.A., A.A.M., A.A.R., Z.A.A. and J.A.A.-T. contributed equally to this study. S.A., A.A.M., Z.A.A., and A.A.R. were the core team leading the systematic review. S.A., A.A.M., and J.A.A.-T. identified and selected the studies. A.M.A. (Abeer M. Alshawi), S.A.A., G.Y.A., and A.A.R. did the quality assessment of the studies. S.A., A.A.M., M.S.A., and A.A.A. collected the data. S.A., A.H.B.S., A.M.A. (Abdullah M. Alotaibi), and A.A.-O. analyzed the data. S.A., A.A.M., Z.A.A., A.A.R., and J.A.A.-T. drafted the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data are available upon request. Please contact author for data requests.

Acknowledgments

We would like to thank Hani N. Mufti for precious guidance and support to create the forest and funnel plots using RStudio. We would also like to thank the reviewers for very helpful and valuable comments and suggestions for improving the paper.

Conflicts of Interest

The authors declare that they have no competing interests.

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