Khatri, Purvesh

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.

Abstract Many patients in the emergency department present with signs and symptoms that arouse concern for sepsis; however, other explanations are also possible. There are currently no rapid tests used in clinical practice that reliably distinguish the presence of a bacterial or viral infection vs. a non-infectious etiology and can predict a patient’s likelihood to decompensate. The diagnostic and prognostic uncertainty in “gray zone” patients complicates the decision to begin therapy as clinicians need to balance the risk of withholding therapy vs. the risk of the therapy itself (e.g., overtreatment with antibiotics and hospitalization, which is costly, potentially harmful, and contributes to antibiotic resistance). The TriVerity™ Test uses isothermal amplification and machine-learning algorithms to quantify and interpret mRNA expression levels to determine both likelihood of bacterial infection, viral infection, or no infection, and whether the patient will likely require one or more critical interventions within 7 days. The three scores each fall into one of five interpretation bands ranging from Very high to Very low. Testing takes approximately 30 minutes using the proprietary Myrna™ Instrument with an operator hands-on-time of under one minute. We enrolled 1,222 patients from 22 emergency departments (ED) to validate the performance of the TriVerity Test. Patients were treated as per local standard of care and were followed for 28 days. Bacterial and viral TriVerity results were validated against clinically adjudicated infection status; the illness severity TriVerity result was validated against the need for at least one critical interventions within 7 days. The bacterial TriVerity result had high AUROC for the diagnosis of bacterial infection (0.83; 80% CI 0.81–0.85) and divided bacterial infection likelihood scores into five interpretation bands with increasing likelihood ratios of infection ranging from Very low (LR- 0.08, 80% CI 0.06–0.11) to Very high (LR + 8.04, 80% CI 5.72–11.78). The AUROC for the bacterial TriVerity result was significantly higher compared to AUROCs for C-reactive protein, procalcitonin or white blood cell count. Similarly, the viral TriVerity score showed high AUROC for the diagnosis of viral infection (0.91; 80% CI 0.90–0.93) and likelihood ratios from Very low (LR- 0.09, 80% CI 0.05–0.14) to Very high (LR + 40.93; 80% CI 29.11–79.23). The TriVerity Illness Severity score showed a high AUROC for the prediction of illness severity (0.77; 80% CI 0.77–0.81) with scores divided into five interpretation bands with increasing likelihood ratios ranging from Very low (LR- 0.22; 80% CI 0.14–0.33) to Very high (LR + 11.33; 80% CI 7.31–17.00). TriVerity illness severity results allowed marked re-classification of the risk for “ICU-level care” as compared to clinical assessment (qSOFA scores) alone. In conclusion, TriVerity provides rapid, highly accurate and actionable results for the diagnosis and prognosis of patients with suspected acute infection and/or sepsis, supporting a major unmet medical need. TriVerity may improve personalized management of patients with suspected acute infections and suspected sepsis for improved overall healthcare outcomes.