Real-time tool to display the predicted disease course and treatment response for children with Crohn's disease

✍ Scribed by Corey A. Siegel; Lori S. Siegel; Jeffrey S. Hyams; Subra Kugathasan; James Markowitz; Joel R. Rosh; Neal Leleiko; David R. Mack; Wallace Crandall; Jonathan Evans; David J. Keljo; Anthony R. Otley; Maria Oliva-Hemker; Sharmayne Farrior; Christine R. Langton; Iwona T. Wrobel; Ghassan Wahbeh; J. Antonio Quiros; Gary Silber; Ron J. Bahar; Bruce E. Sands; Marla C. Dubinsky

• Publisher: John Wiley and Sons
• Year: 2011
• Tongue: English
• Weight: 813 KB
• Volume: 17
• Category: Article
• ISSN: 1078-0998
• DOI: 10.1002/ibd.21386

✦ Synopsis

Background: Immunomodulators and biologics are effective treatments for children with Crohn's disease (CD). The challenge of communicating the anticipated disease course with and without therapy to patients and parents is a barrier to the timely use of these agents. The aim of this project was to develop a tool to graphically display the predicted risks of CD and expected benefits of therapy.

Methods: Using prospectively collected data from 796 pediatric CD patients we developed a model using system dynamics analysis (SDA). The primary model outcome is the probability of developing a CD-related complication. Input variables include patient and disease characteristics, magnitude of serologic immune responses expressed as the quartile sum score (QSS), and exposure to medical treatments.

Results: Multivariate Cox proportional analyses show variables contributing a significant increase in the hazard ratio (HR) for a disease complication include female gender, older age at diagnosis, small bowel or perianal disease, and a higher QSS. As QSS increases, the HR for early use of corticosteroids increases, in contrast to a decreasing HR with early use of immunomodulators, early or late biologics, and early combination therapy. The concordance index for the model is 0.81. Using SDA, results of the Cox analyses are transformed into a simple graph displaying a real-time individualized probability of disease complication and treatment response.

Conclusions:

We have developed a tool to predict and communicate individualized risks of CD complications and how this is modified by treatment. Once validated, it can be used at the bedside to facilitate patient decision making.