No abstract

Objective Respiratory tract infections are among the most common causes of morbidity and mortality worldwide. Acute bronchiolitis (AB) is the leading cause of hospital admission among infants. Clinical scores have proven to be inaccurate in predicting prognosis. Our aim was to build a score based on findings of lung ultrasound (LU) performed at admission, to stratify patients at risk of needing respiratory support (non‐invasive and invasive ventilation). Study design Prospective, multicenter study including infants <6 months of age admitted with AB. Point‐of‐care LU was performed on admission, and a score was calculated based on ultrasound findings (presence and localization of B lines, B line confluence and/or consolidations) and clinical data. Main outcome was need of respiratory support. Results A total of 145 patients were included in the study, with a median age of 1.7 months [IQR: 1.2‐2.8], 47.6% were female. Mean duration of symptoms prior to admission was 3.1 days (SD 1.8). Fifty‐six patients (39%) required non‐invasive ventilation (NIV), 14 (9.7%) were transferred to PICU, and 3 needed invasive ventilation (3/145). Identification of at least one posterior consolidation >1 cm was the main factor associated to NIV (RR 4.4; [CI95%1.8‐10.8]) The LU score built according to the findings on admission showed an AUC: 0.845(CI95%:0.78‐0.91). A score ≥3.5 showed a sensitivity of 89.1% (CI95%:78.2‐94.9%) and specificity of 56% (CI95%: 45.3‐66.1%) Conclusions Among infants below 6 months of age admitted with AB, point‐of‐care LU was a helpful tool to identify patients at risk of needing respiratory support.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).