Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 26, 2024
Date Accepted: Aug 6, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Early Diagnosis of Hereditary Angioedema Based on US Medical Dataset: Algorithm Development and Validation in Japan
ABSTRACT
Background:
The rare genetic disease hereditary angioedema (HAE) induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified.
Objective:
We aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and EMR) in the US and validate the detection performance of HAE cases. We also aimed to verify whether the model was applicable to Japanese data.
Methods:
The HAE patient and control groups were identified from the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability in Japanese data.
Results:
Precision and sensitivity were measured to validate the model performance. The precision score was 2% in the initial model-development step using the comprehensive US dataset. This means that while the prevalence of HAE is 1/50,000, our model can screen out suspected patients, where 1 in 50 of these patients actually have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved 61.5% sensitivity in the US and 37.6% in the validation in a single Japanese hospital. Overall, our model predicted patients with typical HAE symptoms.
Conclusions:
This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.
Citation
