A validated web-based tool to display individualised Crohn's disease predicted outcomes based on clinical, serologic and genetic variables

Alimentary Pharmacology and Therapeutics

Volumn 43, Issue 2, 2016, Pages 262-271

  Siegel, C A ^a,b   Horton, H ^c   Siegel, L S ^d,e   Thompson, K D ^a,b   MacKenzie, T ^b   Stewart, S K ^a   Rice, P W ^e   Stempak, J M ^f   Dezfoli, S ^c   Haritunians, T ^c   Levy, A ^c   Baek, M ^c   Milgrom, R ^f   Dulai, P S ^a,b   Targan, S R ^c   Silverberg, M S ^f   Dubinsky, M C ^g   McGovern, D P ^c  

^a DARTMOUTH HITCHCOCK MEDICAL CENTER   (United States)

^b DARTMOUTH MEDICAL SCHOOL   (United States)

^c CEDARS SINAI MEDICAL CENTER   (United States)

^d Siegel Environmental Dynamics   (United States)

^e Climate Interactive   (United States)

^f UNIVERSITY OF TORONTO   (Canada)

^g MOUNT SINAI SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASPASE RECRUITMENT DOMAIN PROTEIN 15; IMMUNOGLOBULIN A; IMMUNOGLOBULIN G; NEUTROPHIL CYTOPLASMIC ANTIBODY; IMMUNOLOGIC FACTOR; TUMOR NECROSIS FACTOR;

ADULT; AGED; ANUS DISEASE; ARTICLE; CLINICAL FEATURE; COLON DISEASE; COMPUTER INTERFACE; CONTROLLED STUDY; CROHN DISEASE; DEMOGRAPHY; DRUG EXPOSURE; FEMALE; FRAMESHIFT MUTATION; HEREDITY; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL DECISION MAKING; METHODOLOGY; OUTCOME ASSESSMENT; PATIENT DECISION MAKING; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROPORTIONAL HAZARDS MODEL; RISK ASSESSMENT; SEROLOGY; SMALL INTESTINE DISEASE; ADOLESCENT; ANTAGONISTS AND INHIBITORS; INTERNET; PROSPECTIVE STUDY; RETROSPECTIVE STUDY; RISK; VALIDATION STUDY; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; CROHN DISEASE; FEMALE; HUMANS; IMMUNOLOGIC FACTORS; INTERNET; MALE; PROSPECTIVE STUDIES; RETROSPECTIVE STUDIES; RISK; TUMOR NECROSIS FACTOR-ALPHA; YOUNG ADULT;

EID: 84955173280     PISSN: 02692813     EISSN: 13652036     Source Type: Journal    
DOI: 10.1111/apt.13460     Document Type: Article

References (30)

1. Sands BE, Siegel CA,. Crohn's disease. In:, Feldman M, Friedman LS, Brandt LJ, eds. Sleisenger and Fordtran's Gastrointestinal and Liver Disease, Vol. 2, 9th ed. Philadelphia, PA: Saunders Elsevier, 2010; 1941-73.
2. Cosnes J, Cattan S, Blain A, et al,. Long-term evolution of disease behavior of Crohn's disease. Inflamm Bowel Dis 2002; 8: 244-50.
3. Colombel JF, Sandborn WJ, Reinisch W, et al,. Infliximab, azathioprine, or combination therapy for Crohn's disease. N Engl J Med 2010; 362: 1383-95.
4. D'Haens G, Baert F, van Assche G, et al,. Early combined immunosuppression or conventional management in patients with newly diagnosed Crohn's disease: an open randomised trial. Lancet 2008; 371: 660-7.
5. Siegel CA,. Shared decision making in inflammatory bowel disease: helping patients understand the tradeoffs between treatment options. Gut 2012; 61: 459-65.
6. Siegel CA, Siegel LS, Hyams JS, et al,. Real-time tool to display the predicted disease course and treatment response for children with Crohn's disease. Inflamm Bowel Dis 2011; 17: 30-8.
7. Dubinsky MC, Kugathasan S, Mei L, et al,. Increased immune reactivity predicts aggressive complicating Crohn's disease in children. Clin Gastroenterol Hepatol 2008; 6: 1105-11.
8. Jostins L, Ripke S, Weersma RK, et al,. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 2012; 491: 119-24.
9. Beaugerie L, Seksik P, Nion-Larmurier I, et al,. Predictors of Crohn's disease. Gastroenterology 2006; 130: 650-6.
10. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: The R Foundation for Statistical Computing, 2011.
11. Harrell FE,. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, NY: Springer, 2001; 87-287.
12. Heagerty PJ, Zheng Y,. Survival model predictive accuracy and ROC curves. Biometrics 2005; 61: 92-105.
13. Ford A,. Modeling the Environment: An Introduction to System Dynamics Modeling of Environmental Systems. Washington, DC: Island Press, 1999.
14. Jones AP, Homer JB, Murphy DL, et al,. Understanding diabetes population dynamics through simulation modeling and experimentation. Am J Public Health 2006; 96: 488-94.
15. Forrester JW,. Industrial Dynamics. Cambridge, MA: The M.I.T. Press, 1961.
16. Sterman J,. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York, NY: Irwin/McGraw-Hill, 2000.
17. Sands BE, Arsenault JE, Rosen MJ, et al,. Risk of early surgery for Crohn's disease: implications for early treatment strategies. Am J Gastroenterol 2003; 98: 2712-8.
18. Rieder F, Paul G, Schnoy E, et al,. Hemoglobin and hematocrit levels in the prediction of complicated Crohn's disease behavior-a cohort study. PLoS ONE 2014; 9: e104706.
19. Rieder F, Schleder S, Wolf A, et al,. Serum anti-glycan antibodies predict complicated Crohn's disease behavior: a cohort study. Inflamm Bowel Dis 2010; 16: 1367-75.
20. Benitez JM, Louis E,. Can we predict the high-risk patient? Dig Dis 2014; 32: 328-36.
21. Solberg IC, Cvancarova M, Vatn MH, et al,. Risk matrix for prediction of advanced disease in a population-based study of patients with Crohn's Disease (the IBSEN Study). Inflamm Bowel Dis 2014; 20: 60-8.
22. Mazor Y, Maza I, Kaufman E, et al,. Prediction of disease complication occurrence in Crohn's disease using phenotype and genotype parameters at diagnosis. J Crohns Colitis 2011; 5: 592-7.
23. Weersma RK, Stokkers PC, van Bodegraven AA, et al,. Molecular prediction of disease risk and severity in a large Dutch Crohn's disease cohort. Gut 2009; 58: 388-95.
24. Silverberg MS, Satsangi J, Ahmad T, et al,. Toward an integrated clinical, molecular and serological classification of inflammatory bowel disease: report of a Working Party of the 2005 Montreal World Congress of Gastroenterology. Can J Gastroenterol 2005; (19 Suppl. A): 5A-36A.
25. Ryan JD, Silverberg MS, Xu W, et al,. Predicting complicated Crohn's disease and surgery: phenotypes, genetics, serology and psychological characteristics of a population-based cohort. Aliment Pharmacol Ther 2013; 38: 274-83.
26. Muller S, Styner M, Seibold-Schmid B, et al,. Anti-Saccharomyces cerevisiae antibody titers are stable over time in Crohn's patients and are not inducible in murine models of colitis. World J Gastroenterol 2005; 11: 6988-94.
27. Roozendaal C, Pogany K, Hummel EJ, et al,. Titres of anti-neutrophil cytoplasmic antibodies in inflammatory bowel disease are not related to disease activity. QJM 1999; 92: 651-8.
28. Eser A, Papay P, Primas C, et al,. The impact of intestinal resection on serum levels of anti-Saccharomyces cerevisiae antibodies (ASCA) in patients with Crohn's disease. Aliment Pharmacol Ther 2012; 35: 292-9.
29. Kamath PS, Wiesner RH, Malinchoc M, et al,. A model to predict survival in patients with end-stage liver disease. Hepatology 2001; 33: 464-70.
30. Karczewski J, Poniedzialek B, Rzymski P, et al,. The effect of cigarette smoking on the clinical course of inflammatory bowel disease. Prz Gastroenterol 2014; 9: 153-9.