Methods

Patients and inclusion criteria

Bichat-Claude Bernard hospital is a large 850-bed university reference center for high-risk pathogens in Northern Paris. All consecutive patients presenting with an ILI and admitted to the hospital through the ED were prospectively included from November 18, 2019 to March 30, 2020. All were tested for respiratory viral infection using mPCR assay (see below). The first COVID-19 case at our ED was identified on February 28, and systematic on-site specific SARS-CoV-2 RT-PCR began on March 2, 2020.
An ILI was defined as the association of at least one systemic symptom (fever with temperature higher than 38.5°, malaise, headache and myalgia) with one respiratory symptom (cough, sore throat and dyspnea), according to the eCDC ILI definition 9. Those indications of PCR testing were identical during both periods in our ED despite the constraints of the epidemics.

Study periods

In France, the influenza epidemic period extended from November 18, 2019 to March 2, 2020. During this period, SARS-CoV-2 was not spreading in the community or among patients returning or travelling from foreign countries, notably Northern Italy and China. All suspicions of SARS-CoV-2 based on these exposures were thus admitted from other EDs or hospitals in the dedicated ward of the Infectious Diseases department and were not included in this study. Starting on March 3, all patients with an ILI visiting the ED were systematically suspected of COVID-19 and tested for both human RVs and SARS-CoV-2.
To reflect these two periods, they were analyzed separately: (i) Period 1, hereafter called the respiratory virus (RV) period, with active circulation of non-SARS-CoV-2 RVs, from November 18, 2019 to March 2, 2020, and (ii) Period 2, hereafter called the SARS-CoV-2 period with active circulation of SARS-CoV-2, starting on March 3.

Virological testing

During the RV period, multiplex PCR was performed as point of care using the QIAstat-Dx Respiratory Panel (QIAGEN, Hilden, Germany). This rapid multiplex PCR assay allows for the detection of 22 viral and bacterial respiratory targets, including influenza, parainfluenza, rhinoviruses/enteroviruses, RSV, metapneumovirus, adenovirus, coronaviruses, bocavirus, Mycoplasma pneumoniae ,Legionella pneumophila and Bordetella pertussis10. During the SARS-CoV-2 period, and depending on kit availability, viral investigations were conducted either with the QIAstat-Dx Respiratory SARS-CoV-2 Panel (Qiagen, Hilden, Germany), allowing for the detection of the same respiratory pathogens plus SARS-CoV-2 11, or with a combination of the RT-PCR RealStar SARS-CoV-2 Kit RUO (Altona Diagnostics, Hamburg, Germany)12 and rapid multiplex PCR FilmArray RP2 (BioFire, BioMérieux, Marcy-L’Etoile, France) 13, allowing for the detection of the same viral respiratory pathogens except bocavirus.

Clinical and biological data

Demographic and clinical data were prospectively collected, including age, sex, respiratory symptoms (e.g. cough, dyspnea, expectoration, chest pain and auscultatory abnormalities), time since the onset of symptoms, comorbidities (e.g. diabetes, history of stroke, myocardial infarction, chronic heart failure, chronic kidney failure, chronic bronchitis and asthma) and clinical parameters at ED admission (e.g. temperature, blood pressure, cardiac frequency, respiratory frequency, oxygen saturation and Glasgow coma score). The following biological data were also recorded: blood counts, C-Reactive Protein and NT-pro brain natriuretic peptide (NT pro-BNP). Based on the ongoing acquisition of COVID-19-related symptoms, diarrhea, anosmia and ageusia were added to data recording after the beginning of the SARS-CoV-2 period.
Modelling strategy for assessing the effect of the co-circulation of SARS-CoV-2 and other respiratory viruses
Under the hypothesis of a co-circulation of SARS-CoV-2 and other RVs, we modelled the odds of having a positive PCR for SARS-CoV-2 with respect to other viruses, given that the PCR is positive. For the sake of clarity, we denoted the sample by S , with the convention S= 1 for the SARS-CoV-2 period and S = 2 for the RV period. We considered a mixture SARS-CoV-2 and RV period with parameter\(\ p=P(S=1)\). According to Bayes formula, the probability of a random observation from the mixture actually being drawn from Sample 1 given that the PCR is positive is as follows:
\begin{equation} P\left(S=1\right|PCR+)=\frac{P\left(PCR+\right|S=1)\ p}{P\left(PCR+\right|S=1)p+P\left(PCR+\right|S=2)(1-p)}\nonumber \\ \end{equation}
and the corresponding odds are
\begin{equation} O\left(S=1\right|PCR+)=\frac{P\left(PCR+\right|S=1)\ p}{P\left(PCR+\right|S=2)(1-\ p)}\nonumber \\ \end{equation}
Therefore, if we consider a binary covariate X, the odds ratio for being COVID+ in the strata X = 1 with respect to the strata X = 0 can be written as follows:
\begin{equation} \text{OR}_{S,X|PCR+}=\frac{\text{RR}_{PCR,X|1}}{\text{RR}_{PCR,X|2}}\text{OR}_{S,X}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (1)\nonumber \\ \end{equation}
where \(\text{RR}_{\text{PCR},X|s}\)denotes the relative risk of having a positive PCR in the strata X = 1 with respect to X = 0, given that the observation belongs to sample \(s\). This equality, initially derived in the case of a binary covariate X, can be straight forwardly extended to the case of categorical or quantitative variables and covariate-adjusted quantities.
Equation (1) has a clear interpretation in the sense that it identifies two independent contributions to the quantity\(\ \text{OR}_{S,X|\text{PCR}+}\). The first one is the ratio between two relative risks and reflects the difference of association between the covariate and the PCR diagnosis in both samples. If, for example, the male sex is strongly associated with a higher risk of having a positive PCR for SARS-CoV-2, but no association exists for other RVs, then a positive PCR drawn from the mixture sample tends to more likely be for SARS-CoV-2 if the patient is male. The second contribution comes from the imbalance between both samples for covariate X. If, for example, patients from the SARS-CoV-2 period are significantly younger, then a positive PCR in a young patient is more likely to be a COVID case.

Statistical analysis

The baseline characteristics within each group were described with numbers and percentages for qualitative variables and median and interquartile range (IQR) for quantitative variables.
We assessed factors associated with a positive PCR for SARS-CoV-2 in the SARS-CoV-2 period and for other RVs in the RV period using univariate logistic regression. For each sample and variable, we reported the Odd ratio (OR), the 95% confidence interval using a normal approximation and the p-value corresponding to the Wald statistic. We used a threshold of 0.05 for statistical significance. We then performed a multivariate model resulting from a stepwise selection procedure among variables with a p-value below 0.05 in the univariate analysis and a proportion of missing values below 20%.
Regarding the modelling of the SARS-CoV-2 risk in a period of co-circulation with other RVs, we estimated relative risks on the right-hand side of Equation (1) with a quasi-Poisson regression model to take over-dispersion into account. Since these relative risks vary according to the proportion of positive PCR in both samples, we built different scenarios corresponding to different degrees of prevalence of positive PCR for SARS-CoV-2 and other RVs. Relative risks for these scenarios were estimated with a-priori weights on the observations. The quantity \(\text{OR}_{S,X}\) and its standard error were estimated with a logistic regression from the union of both samples. Finally, the estimate for \(\text{OR}_{S,X|\text{PCR}+}\) was reported, and its 95% confidence interval was derived with a normal approximation under the hypothesis of independence between its components.
We also performed a multivariate model for the quantity\(\text{OR}_{S,X|\text{PCR}+}\) by using multivariate models for the right-hand side of (1) and a stepwise selection procedure based on covariates with p < 0.05 in the univariate analysis and less than 20% of missing data. Moreover, to evaluate the discrimination of the multivariate model, we reported the AUC and its 95% confidence interval, derived from 2,000 bootstrap replicates. We finally proposed a clinical score from the multivariate analysis by rounding estimates, and we assessed its discrimination performances (AUC, sensibility and specificity for a given threshold). All analyses were performed using R v4.0.2.
The study has been approved by our local ethic committee.