Proposals with the following project features will be responsive:
A subset of asthma patients using inhaled corticosteroids experience frequent ER visits and hospitalizations, demonstrating an apparent refractoriness to the classical therapy for this problem. Using the RPDR and Natural Language Processing (NLP) tools developed by i2b2, 96,000 patients were identified as diagnosed with asthma. They were subsequently phenotyped for age, weight, gender, smoking status (via NLP), medical history (via NLP), pulmonary function, co-morbidities and confirmation of the diagnosis of asthma, which left 40,000 that met age and smoking criteria. A subset of the asthma patients (n=10,000) with frequent ER visits and hospitalizations (non-responders) and an appropriate control group of asthma patients with no ER visits or hospitalizations had DNA samples collected via the Crimson Biospecimen Core and were genotyped to predict if there was a unique genomic signature (or outcome predictor) associated with the steroid non-response phenotype. The total cost of the project was $200,000.