key: cord-0821931-0niqnhby authors: Davis, Bennett; Rothrock, Ava N.; Swetland, Sarah; Andris, Halle; Davis, Phil; Rothrock, Steven G. title: Viral and atypical respiratory co‐infections in COVID‐19: a systematic review and meta‐analysis date: 2020-06-19 journal: J Am Coll Emerg Physicians Open DOI: 10.1002/emp2.12128 sha: 86e4cc68ed2d2a9afe568422a8bf526f60ff7f54 doc_id: 821931 cord_uid: 0niqnhby OBJECTIVES: Respiratory co‐infections have the potential to affect the diagnosis and treatment of COVID‐19 patients. This meta‐analysis was performed to analyze the prevalence of respiratory pathogens (viruses and atypical bacteria) in COVID‐19 patients. METHODS: This review was consistent with Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA). Searched databases included: PubMed, EMBASE, Web of Science, Google Scholar, and grey literature. Studies with a series of SARS‐CoV‐2‐positive patients with additional respiratory pathogen testing were included. Independently, 2 authors extracted data and assessed quality of evidence across all studies using Cochrane's Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology and within each study using the Newcastle Ottawa scale. Data extraction and quality assessment disagreements were settled by a third author. Pooled prevalence of co‐infections was calculated using a random‐effects model with univariate meta‐regression performed to assess the effect of study subsets on heterogeneity. Publication bias was evaluated using funnel plot inspection, Begg's correlation, and Egger's test. RESULTS: Eighteen retrospective cohorts and 1 prospective study were included. Pooling of data (1880 subjects) showed an 11.6% (95% confidence interval [CI] = 6.9–17.4, I (2) = 0.92) pooled prevalence of respiratory co‐pathogens. Studies with 100% co‐pathogen testing (1210 subjects) found a pooled prevalence of 16.8% (95% CI = 8.1–27.9, I (2) = 0.95) and studies using serum antibody tests (488 subjects) found a pooled prevalence of 26.8% (95%, CI = 7.9–51.9, I (2) = 0.97). Meta‐regression found no moderators affecting heterogeneity. CONCLUSION: Co‐infection with respiratory pathogens is a common and potentially important occurrence in patients with COVID‐19. Knowledge of the prevalence and type of co‐infections may have diagnostic and management implications. A novel coronavirus, now called SARS-CoV-2, was identified as the cause of pneumonia in a cluster of patients in Wuhan, China in December of 2019. 1 Since this initial outbreak, the identified virus has spread across the globe with the World Health Organization (WHO) declaring a global pandemic on March 11, 2020. 2 As of May 3, 2020, there were over 3.3 million cases and 238,000 deaths reported worldwide. 3 Initially, experts including the Centers for Disease Control (CDC) and several state health departments recommended testing individuals with fever and lower respiratory tract infections for other viruses with instructions not to test for SARS-CoV-2 if alternate infections (eg, influenza) were present. [4] [5] [6] [7] [8] [9] Early guidance from the WHO also recommended that clinicians only test for SARS-CoV-2 (formerly 2019-nCoV) once influenza had been ruled out. 10, 11 Subsequent case reports indicate that co-infections may be an important reason for delayed diagnosis of COVID-19. 12, 13 More recently, experts have recommended that individuals who undergo testing for SARS-CoV-2 should additionally be tested for other common respiratory pathogens besides influenza. 14 Information about the type and rate of respiratory co-infections has potential diagnostic and treatment implications in COVID-19. Multiple studies have described typical presenting features for those with COVID-19 with markers that aid in predicting outcome. 14 It is unknown if co-infections alter the presentation, clinical course, or diagnostic markers (eg, laboratory or CT scan findings) used to assess prognosis in COVID-19. It is also possible that treatment of influenza with anti-virals and atypical bacteria with antibiotics might improve the outcome of patients co-infected with COVID-19. Alternately, individuals with co-infections may not respond to treatment in a manner similar to those without COVID-19. For these reasons, knowledge of the prevalence and type of respiratory co-infections has important management and outcome implications in COVID-19 patients. The purpose of this meta-analysis was to determine the prevalence and type of common respiratory co-infections including infections due to respiratory viruses and atypical bacteria in individuals who are SARS-CoV-2-positive. This protocol was consistent with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) methodology (Supporting Information Table S1 ). The protocol was registered with the Center for Open Science's Open Science Framework: citation osf/io/x4q3z. Our study was performed to analyze the prevalence of respiratory virus and atypical bacteria (eg, Chlamydia, Legionella, and Mycoplasma) co-infections in patients infected with SARS-CoV-2. We performed a comprehensive literature search of the National Exclusion criteria included the following: • Absence of total number of SARS-CoV-2 patients, • Absence of simultaneous viral pathogen or atypical bacteria testing, • Duplicate studies or studies using the same patient database during the same time period, • Series with <20 patients with SARS-CoV-2, and • Language other than English. Two reviewers independently extracted data from individual articles based on the Meta-analyses of Observational Studies in Epidemiology/MOOSE reporting checklist (Table S3 ). Extracted data from each article included a description of the study population study details (author, publication year, population country and province/state, design), and specific end point data (patient ages, number of SARS-CoV-2 positive patients, number of viral and atypical pathogen positive patients, specific viral and atypical pathogens tested, specific pathogens found, and type of assay used to test). Group consensus was used to resolve any conflicts regarding data extracted. The quality of evidence across studies and risk of bias for individual studies was independently assessed by 2 study authors. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess quality of evidence across studies as high, moderate, low or very low. 18 The risk of bias was assessed for individual studies using the Newcastle-Ottawa Scale for observational studies. With the Newcastle-Ottawa Scale, studies received up to 9 points based on study subjects, study comparability, and outcome of interest assessment. A Newcastle-Ottawa Scale of 0-6 indicates a high risk of bias, and 7-9 indicates a low risk of bias. [19] [20] [21] For GRADE and Newcastle-Ottawa Scale assessments, any disagreement between the 2 independent reviewers was settled by a third reviewer. Initial agreement between the 2 initial Newcastle-Ottawa Scale raters overall total points was assessed using Cohen's kappa. Publication bias was evaluated using funnel plot inspection, Begg's test and Egger's test with a P < 0.10 considered evidence of bias. 22 If publication bias was found, the trim and fill approach was planned to estimate the number of missing studies due to suppression of extreme results to either side of the funnel plot. 23 The overall pooled prevalence and 95% confidence intervals (CIs) were estimated using a Freeman-Tukey (arcsine square root) transformation, random effects model to calculate a weighted summary. Post hoc, univariate meta-regression was performed using a random effects model to assess the effect of subsets on heterogeneity. A multivariate meta-regression was planned using subset variables with P < 0.05 entered into a model. Table 1 ). Eleven included stud- ies were from peer reviewed journals. 28 The Newcastle-Ottawa Scale for individual studies ranged from 6-7 with 12 studies (63%) rated as having high quality (Table 3; Supporting Information Figure S1 ). The interrater agreement for the total Newcastle-Ottawa Scale was substantial for the initial 2 raters (kappa = 0.77; 95% CI = 0.5-1) Based on GRADE, the overall quality of evidence across all studies was low (Table S4) . Pooling of data found an 11.6% co-infection rate (95% CI = 6. (Table 2) Heterogeneity was high (substantial) across all studies and all subsets (I 2 ) ( Table 4 ). Univariate meta-regression found no moderators that had a significant effect on heterogeneity (Supporting Information Table S5 ). Thus, a multivariate meta-regression was not performed. Begg's correlation test (z = 1.2, P = 0.23) and Egger regression (intercept = 0.7; 95% CI = −1.4-2.9) revealed no publication bias ( Figure 3 ). The number of the cases within this meta-analysis, 1880, was small. Despite this finding, the lower limit of the 95% CI, 6.9%, still implies a meaningful rate of co-infections. It is likely that our study underestimated co-infections because many studies only tested for a subset of respiratory viruses and atypical bacteria. We excluded studies with <20 patients. Our cutoff of 20 patients is consistent with other meta-analyses requiring populations with at least 20, 25, or 30 patients. [46] [47] [48] We chose to exclude smaller studies, because they have a higher risk of bias and are less likely than large studies to be published if results are negative. The potential for equal weighting of small and large studies in random effects metaanalyses tends to skew results toward smaller studies. Experts also have noted that underpowered/smaller studies often contribute little information. 49 We compared study size via subgroup analysis and meta-regression and found no effect on heterogeneity or outcome. Author Only those with SARS-CoV-2 plus influenza were described in the series. c Median age unless otherwise specified. Range in parenthesis unless otherwise specified. IQR, interquartile range. Our meta-analysis included 8 unpublished, non-peer-reviewed studies. 27, 30, 32, 33, 39, 42, 43, 44 Prior studies found that most investigators and editors who evaluate meta-analyses believe that unpublished data should be included as long as the information undergoes the same methodological evaluation as published studies. [50] [51] [52] Exclusion of unpublished studies and grey literature potentially can lead to overestimation of treatment effects within meta-analyses. 53 The Cochrane collaborative allows for inclusion of unpublished studies within systematic review and meta-analyses to avoid publication bias with the caveat that unpublished studies may have lower methodological quality and may be sourced from entities with a biased "interest" in the study results. 51, 52 To determine their effect on our meta-analysis, we included a subgroup analysis and meta-regression of published versus unpublished studies and found no effect on heterogeneity or outcome. High variance between studies or heterogeneity in our and other meta-analyses can be due to clinical, methodological, or statistical rea- The overall quality of evidence within our meta-analysis GRADE was designated as low indicating that the true prevalence of respiratory co-infections might be different from our estimation. The majority of Cochrane systematic reviews, WHO guidelines, and many online medical resources of medical interventions also are based on low or very low quality of evidence. [55] [56] [57] As an example, Alexander et al. 57 found that the WHO made strong recommendations in over 56% of instances in which the quality of evidence was rated low or very low. Multiple reasons exist for this disparity including the known benefit of a treatment, the magnitude of benefit, potential (or lack of potential) for catastrophic harm, confidence in similar alternative options, and overall risk related to recommendations. 58 Selection of non-exposed from the same community as exposed Five studies did not specify type of testing and were not included in subset. We found a pooled prevalence of 11.6% for viral and atypical ) with RT-PCR to analyze the additive diagnostic yield with a combined testing approach. In their study, antibody testing increased detection of viral pathogens between 12%-49% depending upon the virus studied. 64 Only a subset of 1 study in our meta-analysis combined serology and RT-PCR in their population. 43 Separate from issues with testing, co-infection with other respiratory pathogens has important implications for diagnosis and prognosis. It is possible that the clinical presentation, laboratory results, radiological findings, and outcome differ between SARS-CoV-2 positive patients with and without co-infections. Burk et al 65 found that coexisting viral and bacterial pathogens increased mortality in community-acquired pneumonia. Other studies conflict on whether or not co-infection with Chlamydia pneumonia in individuals with SARS-CoV-1 is associated with increased disease severity and mortality. 66 Prospective studies detailing presenting historic, physical examination, and laboratory/radiological features will be needed to determine how patients with respiratory pathogens differ from those without co-pathogens. In summary, we found an 11.6% pooled prevalence for co-infection with viruses and atypical bacteria in studies of SARS-CoV-2-positive patients. Pooled prevalence was even higher, 16 .8%, in studies that tested 100% of patients for co-pathogens. These results indicate that clinicians should not rely on positive tests for these co-infections when considering whether or not to test patients for SARS-CoV-2. Further study is needed to determine if co-infections alter clinical features, laboratory and radiological examinations, and outcomes for patients with COVID-19. A novel coronavirus from patients with pneumonia in China World Health Organization. WHO Director-General's opening remarks at the media briefing on COVID-19-11 Coronavirus disease 2019 (COVID-19) Situation Report -104 Centers for Disease Control and Prevention. Updated guidance on evaluating and testing persons for coronavirus disease 2019 Differential diagnosis of illness in patients under investigation for the novel coronavirus Updated criteria for coronavirus disease 2019 (COVID-19) persons under investigation (PUI) reporting and testing in Alaska Definitions of a person under investigation COVID-19) person under investigation (PUI)/case report form cover sheet South Dakota department of health. COVID updates and information Pan American Health Organization. WHO. Epidemiological update novel coronavirus (2019-nCoV) Laboratory testing of human suspected cases of novel coronavirus (nCoV) infection. Interim guidance Co-infection with SARS-CoV-2 and human metapneumovirus Co-infection with SARS-CoV-2 and influenza in patient with pneumonia Coronavirus disease 2019 (COVID-19) Clinical disease severity of respiratory viral co-infection versus single viral infection: a systematic review and meta-analysis The frequency of influenza and bacterial coinfection: a systematic review and meta-analysis Incidence of viral infection detected by PCR and real-time PCR in childhood community-acquired pneumonia: a meta-analysis GRADE: grading quality of evidence and strength of recommendations for diagnostic tests and strategies Comparison of femoral tunnel length and obliquity of anatomic versus nonanatomic anterior cruciate ligament reconstruction: a meta-analysis Navigated versus conventional technique in high tibial osteotomy: a meta-analysis focusing on weight bearing effect Male partner involvement in increasing the uptake of infant antiretroviral prophylaxis/treatment in sub Saharan Africa: a systematic review and meta-analysis Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomized controlled trials Adjusting for publication bias in the presence of heterogeneity Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated The GRADE Working Group. Chapter 5.2.2 Inconsistency of Results. In GRADE Handbook for Grading Quality of Evidence and Strength of Recommendations. Available from gdt.guidelinedevelopment.org/app/handbook/handbook.html Updated Introduction, comparison, and validation of Meta-Essentials: a free and simple tool for meta-analysis Optimizing diagnostic strategy for novel coronavirus pneumonia, a multi-center study in Eastern China COVID-19 in critically ill patients in the Seattle Region-case series Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Epidemiological and clinical features of 291 cases with coronavirus disease 2019 in areas adjacent to Hubei, China: a double center observational study The clinical characteristics of pneumonia patients co-infected with 2019 novel coronavirus and influenza virus I Wuhan Sex differences in clinical findings among patients with coronavirus disease 2019 (COVID-19) and severe condition A simple laboratory parameter facilitates early identification of COVID-19 patients Co-infections of SARS-CoV-2 with multiple common respiratory pathogens in infected patients Clinical and CT imaging features of the COVID-19 pneumonia: focus on pregnancy women and children Epidemiological and clinical characteristic of COVID-19 patients in Shiyan city of Hubei province, China Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China Rates of co-infection between SARS-CoV-2 and other respiratory pathogens Clinical diagnosis of 274 samples with 2019-novel coronavirus in Wuhan Clinical features of 69 cases with coronavirus disease Clinical characteristics of imported cases of COVID-19 in Jiangsu Province: a multicenter descriptive study Epidemiological and clinical characteristics of children with coronavirus disease Precautions are needed for COVID-19 patients with coinfection of common respiratory pathogens Clinical features and outcome of 221 patients with COVID-19 in Wuhan Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China Clinical outcomes and failure rates of osteochondral allograft transplantation in the knee: a systematic review Oral paracetamol (acetaminophen) for cancer pain Antidepressants for the treatment of abdominal pian-related functional gastrointestinal disorders in children and adolescents The impact of study size on metaanalyses: examination of underpowered studies in Cochrane reviews Should unpublished data be included in meta-analyses? Current convictions and controversies Cochrane Handbook for Systematic Reviews of Interventions Version 6 Cochrane Handbook for Systematic Reviews of Interventions Version 6 Systematic review finds that study data not published in full text articles have unclear impact on meta-analyses results in medical research Commentary in meta-analyses should be expected and appropriately quantified High quality of the evidence for medical and other health-related interventions was uncommon in Cochrane Systematic Reviews Nutritional recommendations for gout: an update from clinical epidemiology World Health Organization recommendations are often strong based on low confidence in effect estimates World Health Organization strong recommendations based on low-quality evidence (study quality) are frequent and inconsistent with GRADE guidance A number of factors explain why WHO guideline developers make strong recommendations inconsistent with GRADE guidance GRADE guidelines: 3. rating the quality of evidence Community surveillance of respiratory viruses among families n the Utah Better Identification of Germs-Longital Viral Epidemiology (BIG-LoVE) study Point-of-care diagnostics for respiratory viral infections Serology enhances molecular diagnosis of respiratory virus infections other than influenza in children and adults hospitalized with community-acquired pneumonia Viral infection in community-acquired pneumonia: a systematic review and meta-analysis Risk of ruling out severe acute respiratory syndrome by ruling in another diagnosis: variable incidence of atypical bacterial co-infection based on diagnostic assays The authors declare no conflicts of interest. Steven G. Rothrock MD https://orcid.org/0000-0003-3528-662X Additional supporting information may be found online in the Supporting Information section at the end of the article.