Vicious circles in inflammatory bowel disease

• Published in: Inflammatory bowel diseases Vol. 12; no. 10; pp. 944 - 949
• Main Authors: Sonnenberg, Amnon, Collins, Judith F.
• Format: Journal Article
• Language: English
• Published: Philadelphia Lippincott Williams & Wilkins, Inc 01.10.2006
• Subjects: Adult; Aged; complications of IBD; Computer Simulation; Crohn Disease - complications; Crohn Disease - etiology; Crohn Disease - therapy; Crohn's disease; decision analysis; Decision Making; Decision Support Techniques; Decision Trees; Drug-Related Side Effects and Adverse Reactions; Feedback; Female; health service research; Humans; Inflammatory bowel diseases; Inflammatory Bowel Diseases - complications; Inflammatory Bowel Diseases - etiology; Inflammatory Bowel Diseases - therapy; influence diagram; Joint diseases; Lymphoma - complications; Lymphoma - etiology; Lymphoma - therapy; Male; Malnutrition - complications; Malnutrition - etiology; Middle Aged; Side effects; simulation; surgery of IBD; Thrombosis - chemically induced; Thrombosis - complications
• ISSN: 1078-0998; 1536-4844
• DOI: 10.1097/01.mib.0000231577.19301.95

Background: Inflammatory bowel disease can present with a bewildering array of disease manifestations whose overall impact on patient health is difficult to disentangle. The multitude of disease complications and therapeutic side effects result in conflicting ideas on how to best manage a patient. The aim of the study is to test the usefulness of influence diagrams in resolving conflicts centered on managing complex disease processes. Methods: The influences of a disease process and the ensuing medical interventions on the health of a patient with inflammatory bowel disease are modeled by an influence diagram. Patient health is the focal point of multiple influences affecting its overall strength. Any downstream influence represents the focal point of other preceding upstream influences. The mathematics underlying the influence diagram is similar to that of a decision tree. Its formalism allows one to consider additive and inhibitory influences and include in the same analysis qualitatively different types of parameters, such as diagnoses, complications, side effects, and therapeutic outcomes. Results: Three exemplary cases are presented to illustrate the potential use of influence diagrams. In all three case scenarios, Crohn's disease resulted in disease manifestations that seemingly interfered with its own therapy. The presence of negative feedback loops rendered the management of each case particularly challenging. The analyses by influence diagrams revealed subtle interactions among the multiple influences and their joint contributions to the patient's overall health that would have been difficult to appreciate by verbal reasoning alone. Conclusion: Influence diagrams represent a decision tool that is particularly suited to improve decision‐making in inflammatory bowel disease. They highlight key factors of a complex disease process and help to assess their quantitative interactions.