HHV-8-negative/idiopathic multicentric Castelman disease (iMCD) is a rare lymphoproliferative disorder with limited treatment options and unfavorable outcomes. Although Siltuximab received FDA approval as the sole treatment for this condition in April 2014, its effectiveness and real-world outcomes are unclear outside of clinical trials. Creating a substantial patient cohort for iMCD is challenging due to its low prevalence, absence of a specific ICD code until 2017, and the considerable effort involved in manual chart review.
In this study, researchers developed a machine learning model based on natural language processing to surface patients with iMCD across a nationwide EHR database to examine their clinical features, treatment patterns, and real-world overall survival (rwOS).
Why this matters
This study used advanced machine learning techniques to conduct the most extensive analysis of patients with iMCD to date. By evaluating the utilization and effectiveness of Siltuximab as well as other treatments in real-world practice, the research provides valuable insights into the disease, how it is managed, and how care for patients with iMCD can be improved. These findings carry the potential to significantly impact future treatment strategies, leading to better care for individuals with this rare condition.