Citations

Abstract Asthma is a heterogeneous disease with variable presentation and characteristics. There is a critical need to identify underlying molecular endotypes of asthma. We performed the largest transcriptomic analysis of 808 bronchial epithelial cell samples across 11 independent cohorts, including 3 cohorts from the Severe Asthma Research Program. Using seven datasets (218 patients with asthma, 148 healthy control subjects) as discovery cohorts, we identified 505 differentially expressed genes, which we validated in the remaining four datasets. Unsupervised clustering using the 505 differentially expressed genes identified four reproducible clusters of patients with asthma across all datasets, corresponding to healthy control subjects, patients with mild/moderate asthma, and patients with severe asthma with significant differences in several clinical markers of severity, including pulmonary function, Type 2 inflammation, fractional exhaled nitric oxide, and maximum bronchodilator reversibility. Importantly, we found the same clusters in pediatric patients using nasal lavage fluid cells, demonstrating the gene signature and clusters are not confounded by age and are conserved in both lower and upper airways. The four asthma clusters may represent a unifying framework for understanding the molecular heterogeneity of asthma. Further study could potentially enable a precision medicine approach of matching therapies with patients with asthma most likely to benefit.

PURPOSE Improving care transitions for patients with cancer discharged from the hospital is considered an important component of quality care. Digital monitoring has the potential to better the delivery of transitional care through improved patient-provider communication and enhanced symptom management. However, remote patient monitoring (RPM) interventions have not been widely implemented for oncology patients after discharge, an innovative setting in which to apply this technology. METHODS We implemented a RPM intervention which identifies medical oncology patients at discharge, monitors their symptoms for 10 days, and intervenes as necessary to manage symptoms. We evaluated the feasibility (>50% patient engagement with symptom assessment), appropriateness (symptom alerts), and acceptability (net promoter score >0.7) of the intervention and the initial effect on acute care visits and return on investment. RESULTS During the study period, January 1, 2021, to December 31, 2022, we evaluated 2,257 medical oncology discharges representing 1,857 unique patients. We found that 65.9% of patients discharged (N = 1,489) completed at least one symptom assessment postdischarge and of them, 45.5% (n = 678) generated a severe symptom alert that we helped to manage. Patients expressed high satisfaction with the intervention with a net promoter score of 84%. In preliminary analysis of patients with GI malignancies (n = 449), we found a nonsignificant decrease in 30-day readmissions for the intervention cohort (n = 269) by 5.8% as compared with the control (n = 180; from 33.3% to 27.5%; P = .22). CONCLUSION Digital transitional care management was feasible and demonstrated that patients transitioning from the hospital to home have a substantial symptom burden. The intervention was associated with high patient satisfaction but will require further refinement and evaluation to increase its impact on 30-day readmission.

AbstractIntroductionPrevious bronchoalveolar lavage fluid (BALF) proteomic analysis has evaluated limited numbers of subjects for only a few proteins of interest, which may differ between asthma and normal controls. Our objective was to examine a more comprehensive inflammatory biomarker panel in quantitative proteomic analysis for a large asthma cohort to identify molecular phenotypes distinguishing severe from nonsevere asthma.MethodsBronchoalveolar lavage fluid from 48 severe and 77 nonsevere adult asthma subjects were assessed for 75 inflammatory proteins, normalized to BALF total protein concentration. Validation of BALF differences was sought through equivalent protein analysis of autologous sputum. Subjects' data, stratified by asthma severity, were analysed by standard statistical tests, principal component analysis and 5 machine learning algorithms.ResultsThe severe group had lower lung function and greater health care utilization. Significantly increased BALF proteins for severe asthma compared to nonsevere asthma were fibroblast growth factor 2 (FGF2), TGFα, IL1Ra, IL2, IL4, CCL8, CCL13 and CXCL7 and significantly decreased were platelet‐derived growth factor a‐a dimer (PDGFaa), vascular endothelial growth factor (VEGF), interleukin 5 (IL5), CCL17, CCL22, CXCL9 and CXCL10. Four protein differences were replicated in sputum. FGF2, PDGFaa and CXCL7 were independently identified by 5 machine learning algorithms as the most important variables for discriminating severe and nonsevere asthma. Increased and decreased proteins identified for the severe cluster showed significant protein–protein interactions for chemokine and cytokine signalling, growth factor activity, and eosinophil and neutrophil chemotaxis differing between subjects with severe and nonsevere asthma.ConclusionThese inflammatory protein results confirm altered airway remodelling and cytokine/chemokine activity recruiting leukocytes into the airways of severe compared to nonsevere asthma as important processes even in stable status.