key: cord-0783090-umr8ryje authors: Ma, Xiaolin; Conrad, Tim; Alchikh, Maren; Reiche, Janine; Schweiger, Brunhilde; Rath, Barbara title: Can we distinguish respiratory viral infections based on clinical features? A prospective pediatric cohort compared to systematic literature review date: 2018-07-24 journal: Rev Med Virol DOI: 10.1002/rmv.1997 sha: 235ef978feb59a1d0d829d58dd7838ddcbca8e50 doc_id: 783090 cord_uid: umr8ryje Studies have shown that the predictive value of “clinical diagnoses” of influenza and other respiratory viral infections is low, especially in children. In routine care, pediatricians often resort to clinical diagnoses, even in the absence of robust evidence‐based criteria. We used a dual approach to identify clinical characteristics that may help to differentiate infections with common pathogens including influenza, respiratory syncytial virus, adenovirus, metapneumovirus, rhinovirus, bocavirus‐1, coronaviruses, or parainfluenza virus: (a) systematic review and meta‐analysis of 47 clinical studies published in Medline (June 1996 to March 2017, PROSPERO registration number: CRD42017059557) comprising 49 858 individuals and (b) data‐driven analysis of an inception cohort of 6073 children with ILI (aged 0‐18 years, 56% male, December 2009 to March 2015) examined at the point of care in addition to blinded PCR testing. We determined pooled odds ratios for the literature analysis and compared these to odds ratios based on the clinical cohort dataset. This combined analysis suggested significant associations between influenza and fever or headache, as well as between respiratory syncytial virus infection and cough, dyspnea, and wheezing. Similarly, literature and cohort data agreed on significant associations between HMPV infection and cough, as well as adenovirus infection and fever. Importantly, none of the abovementioned features were unique to any particular pathogen but were also observed in association with other respiratory viruses. In summary, our “real‐world” dataset confirmed published literature trends, but no individual feature allows any particular type of viral infection to be ruled in or ruled out. For the time being, laboratory confirmation remains essential. More research is needed to develop scientifically validated decision models to inform best practice guidelines and targeted diagnostic algorithms. Studies have shown that the predictive value of "clinical diagnoses" of influenza and other respiratory viral infections is low, especially in children. In routine care, pediatricians often resort to clinical diagnoses, even in the absence of robust evidence-based criteria. We used a dual approach to identify clinical characteristics that may help to differen- We determined pooled odds ratios for the literature analysis and compared these to odds ratios based on the clinical cohort dataset. This combined analysis suggested significant associations between influenza and fever or headache, as well as between respiratory syncytial virus infection and cough, dyspnea, and wheezing. Similarly, literature and cohort data agreed on significant associations between HMPV infection and cough, as well as adenovirus infection and fever. Importantly, none of the abovementioned features were unique to any particular pathogen but were also observed in association with other respiratory viruses. In summary, our "real-world" dataset confirmed published literature trends, but no individual feature allows any particular type of viral infection to be ruled in or ruled out. For the time being, laboratory confirmation remains essential. More research is List of Abbreviations: Ab, antibody; AE, asthma exacerbation; altered/LOC, altered or loss of consciousness; anorexia/DF, anorexia/difficulty feeding; ARI, acute respiratory infection; BALF, bronchoalveolar lavage fluids; BCL, bronchiolitis; CAP, community-acquired pneumonia; CC, case-control; CF, cystic fibrosis; CI, confidence interval; COH, (inception) cohort dataset; CS, cross-sectional; DB, difficulty breathing; DFA, direct immunofluorescence assay; EIA, enzyme immunoassay; EIFA, enzyme immunofluorescence assay; ETA, endotracheal aspirates; Flu, influenza; FS, febrile seizure; FRI, febrile respiratory illness; HAdV, human adenovirus; HBoV-1, human bocavirus type 1; HCoV, human coronavirus; HHP-6, human herpesvirus 6; HMPV, human metapneumovirus; HPIV, human parainfluenza virus; HRV, human rhinovirus; IFA, (indirect) immunofluorescence assay; ILI, influenza-like illness; LIT, literature review dataset; LRTI, lower respiratory tract infections; NOS, number of studies; NPA, nasopharyngeal aspirate; NPS, nasopharyngeal swab; NS, nasal swabs/secretions; NW, nasal washing; OP, observational prospective; OPS, oropharyngeal swabs; OR, observational retrospective; PC, prospective cohort; PNA, pneumonia; (p)OR, (pooled) odds ratio; PPV, positive predictive value; PROSPERO, International Prospective Register of Systematic Reviews; PS, pharyngeal swabs; QI, quality improvement; RKI, Robert Koch Institute; RS, respiratory samples; SARI, severe acute respiratory infection; RT, rapid test,; RTI, respiratory tract infection; TA, tracheal aspirates; TS, throat swabs; URTI, upper respiratory tract infections; WHO, World Health Organization Influenza and acute respiratory infections (ARI) are major contributors to disease burden in the pediatric age group 1-4 with highest mortality rates in resource-limited settings. 5, 6 It has been shown that the positive predictive value of a "clinical" influenza diagnosis in children is as low as 32%. 7 In children in particular, influenza symptoms are often nonspecific, making it difficult to distinguish influenza infection from infection because of other respiratory viruses. 8 The ability to make accurate "clinical diagnoses" is further hampered by the frequent succession of different respiratorys infection during the winter months. 7 For pediatricians in acute care settings, however, it may not always be possible to perform virus diagnostics. Even if diagnostic tests are widely available, presumptive clinical diagnoses will still be influencing clinical decision-making, such as the use of diagnostics, antivirals, and antibiotics. Clinical bias in the use of diagnostic testing may thus impair epidemiological surveillance and disease burden estimates. 9 To address this question further, we explored which clinical features, according to the published literature, may be associated with ARI due to influenza, respiratory syncytial virus (RSV), human adenovirus (HAdV), human rhinovirus (HRV), human metapneumovirus (HMPV), human bocavirus-1 (HBoV-1), human parainfluenza virus (HPIV), and human coronavirus (HCoV). We then addressed the same question through analysis of a "real-world" dataset based on a prospective surveillance of 6073 children aged 0 to 18 years, where detailed clinical presentations and virus diagnoses were assessed and documented in all patients, independent from routine care. 10 The objectives of this analysis are as follows: In the first round of review, the following data were extracted independently: (1) study location (country), (2) study design, (3) age range, (4) cohort size/number of subjects, (5) sampling and laboratory method, and (6) presenting symptoms including respiratory and extrarespiratory symptoms. Full-text publications were accessed for a second round of review. XM and BR independently reviewed studies against the predefined inclusion and exclusion criteria, and any eligible discrepancy was resolved by discussion among the reviewer team (3 researchers). The clinical symptoms were grouped into the following 19 distinct symptom categories: altered or loss of consciousness (altered/LOC), anorexia/difficulty feeding, apnea, conjunctivitis, cough, hypoxia, diarrhea, dyspnea, fever, headache, malaise, myalgia, rash, rhinitis, seizures, sore throat, signs of upper respiratory tract infection, vomiting, and wheezing/bronchoconstriction/signs of lower respiratory tract infection (henceforth labeled "wheezing"). The literature review was compared to a well-described clinical inception cohort [11] [12] [13] [14] [15] : From December 2009 to April 2015, a specifically trained quality improvement (QI) team performed predefined clinical assessments of 6073 influenza-like illness (ILI) patients aged 0 to 18 years at the point of care. [11] [12] [13] [14] [15] Influenza-like illness case criteria were defined as evidence of fever with a body temperature ≥38°C and ≥1 respiratory symptom (including cough, rhinitis/coryza, red/sore throat, ear ache, dyspnea, tachypnea, labored breathing, wheezing) or a documented clinician diagnosis of ILI. Clinical assessments were as described previously. 10 Nasopharyngeal swabs were collected in universal transport medium Specimens were analyzed for influenza A and B, RSV, HMPV, HAdV, and HRV by real-time PCR as published previously. 10, 11, [16] [17] [18] [19] Investigation of HCoV (NL63, 229E, OC43, and HKU1), HPIV1-4, The comparative statistical analysis was performed using R with the Metaphor Package software. 20 Clinical features associated with viral pathogens were determined independently using pooled odds ratios (pOR), with 95% confidence intervals (CI) for the literature review Random effect models for meta-analysis were applied. 20 Heterogeneity testing was done using I 2 statistics. I 2 values <25% were considered low, 25% to 75% as moderate, and values >75% indicated high levels of heterogeneity. 21 Publication bias was assessed using funnel plots. A symmetrical plot indicates a lack of publication bias. 22 For each used OR calculation, we estimated the exact CI using the mid-p method. 23 3 | RESULTS In clinical practice, fever is often considered a hallmark of influenza disease, whereas wheezing is viewed as "typical" for RSV infections. Therefore, we studied these clinical associations in detail in the published literature (Figures 2 and 3 I 2 = 66%) (Figures 2A and 4A ). As evident from detailed literature analysis, most studies agreed on a positive correlation, with 1 exception. 66 No evidence of publication bias was observed. There was no significant association in the meta-analysis between fever and RSV (pOR I 2 = 35%) ( Figure 3A ). Associations with fever or wheezing however are neither unique to influenza nor to RSV. When we studied the meta-analyses across all 8 types of respiratory viral infection, multiple overlapping associations were easily identified for different types of respiratory viral infections ( Figure 4A ). In fact, most associations were shared across multiple types of viral infection, and no clinical feature stood out as unique to any specific type of infection. In addition to influenza, fever was also significantly associated The same clinical features were now tested in the clinical cohort ( Figure 4B ). The most striking difference was that associations in the COH dataset yielded narrower CI compared to the LIT dataset. Several new (positive and negative) associations were revealed in the COH dataset that were not previously observed in the meta-anal- The individual studies in our meta-analysis showed high levels of heterogeneity, especially with regards to inclusion criteria and/or cut- Publication bias was also of concern in literature studies as funnel plots indicate that negative associations may have been missed in the published literature. To avoid observer bias in the inception cohort, a trained QI team elicited these symptoms accurately in all patients, regardless of age. In clinical routine care in most settings, it will not be feasible to obtain virus diagnostics on the 8 most common respiratory viral pathogens in all patients with ILI, as was the case in this inception cohort. PubMed and the English language were chosen as they represent the most commonly accessed publications by clinicians. A total of 205 literature studies had to be excluded because of lack of a "virus-negative" control groups ( Figure 1 ). In the inception cohort, each patient was simultaneously tested (+/−) for the same viral pathogens using highly sensitive and specific RT-PCR assays at the National Reference Centre for Influenza at the Robert-Koch Institute. Even though sample sizes were usually smaller in the inception cohort, prospective data collection resulted in higher confidence levels, because of a comprehensive dataset with predefined variables Future prospective studies or QI programs in different settings (for example in countries with universal influenza vaccination and treatment recommendations) may allow for the analysis of medical interventions. 80 We showed that point-of-care clinical assessments via mobile application represent a powerful mechanism to identify "typical clinical features" likely to be associated with a specific viral infection. Many clinical features are shared across different types of respiratory viral infection. This means that even though significant associations between individual clinical features and viral infections have been identified, clinical symptoms alone cannot be used to predict specific respiratory viral infections in a particular patient. Clinicians should be aware that clinical features alone will not "rule-in" or "rule-out" any specific type of viral infection. Diagnostic testing for respiratory viruses will remain the cornerstone of accurate diagnoses. Testing should be encouraged to prevent unnecessary prescriptions of antivirals in "similar-looking" noninfluenza cases, where neuraminidase inhibitors would be ineffective. 81, 82 Methodologically, prospective data collection may be more effective in identifying clinical associations than large-scale meta-analyses of the medical literature. While some trends in the literature are confirmed, additional features were identified through the inception cohort. In the future, complex decision models considering combinations of symptoms rather than individual features may be more useful to inform best practice. Machine-learning algorithms may show the way toward "smart" decision software and the targeted use of diagnostics and antivirals. 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A prospective pediatric cohort compared to systematic literature review The authors kindly thank the members of the QI team, namely Katharina Karsch, Franziska Tief, Susann Muehlhans, Patrick Obermeier, Xi Chen, and Lea Seeber for their contribution to the clinical dataset. The authors also express their gratitude to the laboratory team at the Robert Koch Institute including Barbara Biere, Eleni Adamou, as well as the colleagues at the National Reference Centre for their helpful comments and review of the manuscript. The authors have no conflicts of interest to declare with regards to the work presented.