UC San Diego

BACKGROUND: Hereditary angioedema (HAE) is a genetic condition characterized by dysregulation of the contact (kallikrein-bradykinin) pathway, leading to recurrent episodes of angioedema. OBJECTIVE: This project sought to determine whether a suspicion index screening tool using electronic health record (EHR) data can identify patients with an increased likelihood of a diagnosis of HAE. METHODS: A suspicion index screening tool for HAE was created and validated by using known patients with HAE from the medical literature as well as positive and negative controls from HAE-focused centers. Through the use of key features of medical and family history, a series of logistic regression models for 5 known genetic causes of HAE were created. Top variables populated the digital suspicion scoring system and were run against deidentified EHR data. Patients at 2 diverse sites were categorized as being at increased, possible, or no increased risk of HAE. RESULTS: Prediction scoring using the strongest 13 variables on the real-world EHR-positive control data identified all but 1 patient with C1 inhibitor deficiency and patient with non-C1 inhibitor deficiency without false-positive results. The 2 missed patients had no documented family history of HAE in their EHR. When the prediction scoring variables were expanded to 25, the screening algorithm approached 100% sensitivity and specificity. The 25-variable algorithm run on general population EHR data identified 26 patients at the medical centers as being at increased risk for HAE. CONCLUSIONS: These results suggest that development, validation, and implementation of suspicion index screening tools can be useful to aid providers in identifying patients with rare genetic conditions.