Design
this is a new work from our PREVISE study, which prospectively recruited patients presenting with respiratory infection at a Community Health Centre in Spain (Servicio de Urgencias de Atención Primaria, SUAP, Salamanca) from December 2018 to January 2019. While in a previous article from this study we evaluated transcriptomic signatures for detecting viral infection in these patients (11), in the present manuscript we test other totally different gene expression signatures to predict hospitalization.
Patients and setting: recruited patients met the following criteria: age >45 years, clinical signs of upper or lower respiratory infection according to the physician in charge, and at least one of the following items of the National Early Warning Score (NEWS score) (12): Respiration rate < 11 rpm or ≥ 21 rpm, oxygen saturation < 95%, any supplemental oxygen, temperature < 36.0 or > 38.1o C, systolic blood pressure < 110 mmHg or ≥ 220 mmHg, heart rate < 50 or > 91 or altered level of consciousness. A total of 129 patients were enrolled, of which 29 were finally excluded from the study (14 of them had incomplete follow up data and 15 showed infections of other source than respiratory one).
Ethics declarations
informed consent was obtained from all individuals. The research complied with all the relevant national regulations and institutional policies and was developed in accordance to the tenets of the Helsinki Declaration, and was approved by the “Comité Ético de Investigación con Medicamentos” of the Instituto de Investigación Biomédica de Salamanca (IBSAL) (code PI 2018 11 138).
Gene expression profiling: 2.5 mL of blood were collected by using PaxGene (BD) venous blood vacuum collection tubes. Total RNA was extracted from blood samples using the PAXgene Blood RNA System (PreAnalytix, Hombrechtikon, Switzerland). The evaluation of concentration and quality was performed by spectrometry (Nano-Drop ND1000, NanoDrop Technologies,Wilmington, DE). Gene expression was quantified by ddPCR (BioRad). Granulocyte genes assessed were MMP8 (reference Hs01029057_m1); LCN2 (reference Hs01008571_m1); LTF (Hs00914334_m1); PRTN3 (Hs01597752_m1) and FCER1A (Hs00758600_m1). Predesigned TaqMan Assay Primer/Probe Sets were employed [FAM labeled MGB probes (MMP8, LCN2 and PRTN3), or VIC labeled MGB probes. (FCER1A, LTF) from Thermo Fisher/Scientific-Life Technologies, Waltham, MA, USA]. cDNA was generated from each sample on a Techne TC-512 thermal cycler (Bibby-Scientific, Staffordshire, OSA, UK) starting from 1000 ng of mRNA by using iScript Advanced cDNA Synthesis Kit (BioRad, cat:1725038). The obtained volume of cDNA (20mL) was further diluted (1/25), and 2.5mL (5 ng of total mRNA) were employed for quantification of target gene expression according to the manufacturer instructions. Briefly, ddPCR was performed using the BioRad QX200 ddPCR system, ddPCR Supermix for Probes (no dUTP), and BioRad standard reagents for droplet generation and reading. End-point PCR with 40 cycles was performed by usingC1000Touch Thermal Cycler (BioRad) after splitting each sample into approximately 20,000 droplets. Next, the droplet reader used at least 10,000 droplets to determine the percentage of positive droplets and calculation of copy number of cDNA per nanogram of initial mRNA.
Viral diagnosis
Nasopharyngeal aspirates were tested for influenza A H1, influenza H1N1 2009, influenza H3, influenza B, adenovirus, respiratory syncytial virus, rhinovirus, metapneumovirus, and parainfluenza 1,2 and 3 using the FILMARRAY® Respiratory 2 Panel, Biofire, USA) (13)..
Statistical analysis
For the descriptive analysis of the patients ‘characteristics, the differences between groups were assessed using the Chi-square test or Fisher's Exact Test for categorical variables. For continuous variables, differences between groups were assessed with the Mann-Whitney U test. A multivariate logistic regression analysis was employed to evaluate the association between gene expression levels and the risk of hospitalization in the next 48 hours following first contact with the family doctor. The potential confounding factors were identified by using a univariate analysis followed by multiple testing correction (false discovery ratio FDR- Benjamini–Hochberg) of the p-values (a complete list of the variables considered for this analysis is showed in the supplementary material 1). Those variables yielding FDR/q-values < 0⋅200 were further introduced in the multivariate analysis as adjusting variables for gene expression levels. In the case of the categorical variables, only those with a frequency > 5% were considered for the analysis. The predicted values from the multivariate model were used to calculate the area under the receiver operating curve (AUC), which summarizes its predictive power. Statistical analysis was performed using IBM SPSS Statistics 26.0 (SPSS INC, Armonk, NY, U.S.A). The level of significance was set at 0·05 (2-tailed).