Clinical trial NCT06457035: Development and Validation of a Nomogram for Predicting Surgery in Newly-diagnosed Crohn's Disease: a Retrospective Cohort Study
Official title: Development and Validation of a Nomogram for Predicting Surgery in Newly-diagnosed Crohn's Disease: a Retrospective Cohort Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Newly-diagnosed Crohn's Disease
Keywords: Crohn's disease
surgery
nomogram
prediction model
Lead sponsor: First Affiliated Hospital, Sun Yat-Sen University
Sponsor class: OTHER
First posted: 2024-06-13
Enrollment: 490
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: patients newly diagnosed with Crohn's disease

Brief summary: A majority of patients with Crohn's disease undergo surgery during the disease course. We aimed to develop an easily available nomogram to predict the risk of surgery at diagnosis.

Detailed description: Crohn's disease (CD), a chronic inflammatory disorder involving all of the gastrointestinal tract, has a progressive and destructive course and is increasing in incidence worldwide.1 Although the most common disease behavior of patients with newly-diagnosed CD is inflammatory (B1 in the Montreal Classification2), a rapid and prominent progression in disease behaviour will be observed in approximate half of the patients within 10 years after diagnosis.3-5 Data from a population-based cohort show that nearly half of patients developed intestinal complications such as strictures and fistulae, in the 20 years following the diagnosis.3 In spite of the application of immunosuppressive maintenance therapies, more than half of the patients suffer from severe complications and required intestinal resection.6,7 In recent decades, with the advent of targeted biologic therapies such as tumor necrosis factor antagonists, gut-selective monoclonal anti-integrin antibody and inhibitors of IL-12 and IL-23 signaling, the medical management of CD has been revolutionized.8 Earlier and more aggressive application of biologics or novel small molecules and combination therapies have been demonstrated to induce a profound alteration of natural disease course and diminish the requirement for hospitalization and surgery among patients with newly-diagnosed CD.9,10 Nevertheless, one of the most difficult challenges in the so-called top-down treatment strategy is the identification of patients who are at high risk for disease progression and thus necessitate more intensive treatment pattern despite the therapy-related adverse events and heavy costs. From another perspective, failure to identify high-risk patients also delays the prescription of more effective therapies and accounts for an increase in the risk of disease progression.

Much effort has been made in the field of baseline risk stratification for newly-diagnosed CD. Many clinical characteristics have been found to independently correlate with prognosis, including age at diagnosis, disease location, disease behavior, smoking status, and history of medication.9,11,12 Meanwhile, several prognostic biomarkers have been discovered in pilot studies, encompassing immune-related molecules and specific gene expression levels.13,14 Nonetheless, inconvenience and high expense has impeded their full validation and clinical application. Accordingly, the therapy selection is still tailored to the individual patient newly diagnosed with CD based on the clinical risk factors and patient comorbidities8, which is far from precision treatment.

In this era of artificial intelligence, a lot of machine learning models have been developed for innovation in all fields of inflammatory bowel disease, such as diagnosis, monitoring, disease course prediction and management.15 Unfortunately, the majority of popular machine learning prediction models are essentially black boxes, rendering verdicts with a few accompanying justifications, which limits clinical reliability and hence obstructs clinical implementation.16 To balance effectiveness with convenience and interpretability, we aimed to construct a well-interpreted Cox statistical regression model together with a nomogram based on clinical characteristics and available serological indicators to predict the long-term prognosis of newly diagnosed CD.

Eligibility criteria: Inclusion Criteria:

* The diagnosis of CD was finally confirmed according to diagnostic criteria, including clinical manifestations and ileocolonoscopic, histopathologic, and radiologic evidence
* Laboratory data of complete blood count and routine blood biochemical examination were available within one week before the diagnostic ileocolonoscopy

Exclusion Criteria:

* The time of diagnosis was unclear
* They underwent bowel resection within three months after diagnosis, which reflected an early complicated disease
* They suffered from severe infection around the laboratory test

Primary outcomes:
measure: Requirement of surgery
timeFrame: Any time during the follow-up (up to Dec 31, 2022)
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References:
citation: Roda G, Chien Ng S, Kotze PG, Argollo M, Panaccione R, Spinelli A, Kaser A, Peyrin-Biroulet L, Danese S. Crohn's disease. Nat Rev Dis Primers. 2020 Apr 2;6(1):22. doi: 10.1038/s41572-020-0156-2.
citation: Satsangi J, Silverberg MS, Vermeire S, Colombel JF. The Montreal classification of inflammatory bowel disease: controversies, consensus, and implications. Gut. 2006 Jun;55(6):749-53. doi: 10.1136/gut.2005.082909.
citation: Thia KT, Sandborn WJ, Harmsen WS, Zinsmeister AR, Loftus EV Jr. Risk factors associated with progression to intestinal complications of Crohn's disease in a population-based cohort. Gastroenterology. 2010 Oct;139(4):1147-55. doi: 10.1053/j.gastro.2010.06.070. Epub 2010 Jul 14.
citation: Tarrant KM, Barclay ML, Frampton CM, Gearry RB. Perianal disease predicts changes in Crohn's disease phenotype-results of a population-based study of inflammatory bowel disease phenotype. Am J Gastroenterol. 2008 Dec;103(12):3082-93. doi: 10.1111/j.1572-0241.2008.02212.x.
citation: Louis E, Collard A, Oger AF, Degroote E, Aboul Nasr El Yafi FA, Belaiche J. Behaviour of Crohn's disease according to the Vienna classification: changing pattern over the course of the disease. Gut. 2001 Dec;49(6):777-82. doi: 10.1136/gut.49.6.777.
citation: Peyrin-Biroulet L, Loftus EV Jr, Colombel JF, Sandborn WJ. The natural history of adult Crohn's disease in population-based cohorts. Am J Gastroenterol. 2010 Feb;105(2):289-97. doi: 10.1038/ajg.2009.579. Epub 2009 Oct 27.
citation: Bemelman WA, Warusavitarne J, Sampietro GM, Serclova Z, Zmora O, Luglio G, de Buck van Overstraeten A, Burke JP, Buskens CJ, Colombo F, Dias JA, Eliakim R, Elosua T, Gecim IE, Kolacek S, Kierkus J, Kolho KL, Lefevre JH, Millan M, Panis Y, Pinkney T, Russell RK, Shwaartz C, Vaizey C, Yassin N, D'Hoore A. ECCO-ESCP Consensus on Surgery for Crohn's Disease. J Crohns Colitis. 2018 Jan 5;12(1):1-16. doi: 10.1093/ecco-jcc/jjx061. No abstract available.
citation: Cushing K, Higgins PDR. Management of Crohn Disease: A Review. JAMA. 2021 Jan 5;325(1):69-80. doi: 10.1001/jama.2020.18936.
citation: Peyrin-Biroulet L, Oussalah A, Williet N, Pillot C, Bresler L, Bigard MA. Impact of azathioprine and tumour necrosis factor antagonists on the need for surgery in newly diagnosed Crohn's disease. Gut. 2011 Jul;60(7):930-6. doi: 10.1136/gut.2010.227884. Epub 2011 Jan 12.
citation: Jenkinson PW, Plevris N, Siakavellas S, Lyons M, Arnott ID, Wilson D, Watson AJM, Jones GR, Lees CW. Temporal Trends in Surgical Resection Rates and Biologic Prescribing in Crohn's Disease: A Population-based Cohort Study. J Crohns Colitis. 2020 Sep 16;14(9):1241-1247. doi: 10.1093/ecco-jcc/jjaa044.
citation: Ramadas AV, Gunesh S, Thomas GA, Williams GT, Hawthorne AB. Natural history of Crohn's disease in a population-based cohort from Cardiff (1986-2003): a study of changes in medical treatment and surgical resection rates. Gut. 2010 Sep;59(9):1200-6. doi: 10.1136/gut.2009.202101. Epub 2010 Jul 21.
citation: Kugathasan S, Denson LA, Walters TD, Kim MO, Marigorta UM, Schirmer M, Mondal K, Liu C, Griffiths A, Noe JD, Crandall WV, Snapper S, Rabizadeh S, Rosh JR, Shapiro JM, Guthery S, Mack DR, Kellermayer R, Kappelman MD, Steiner S, Moulton DE, Keljo D, Cohen S, Oliva-Hemker M, Heyman MB, Otley AR, Baker SS, Evans JS, Kirschner BS, Patel AS, Ziring D, Trapnell BC, Sylvester FA, Stephens MC, Baldassano RN, Markowitz JF, Cho J, Xavier RJ, Huttenhower C, Aronow BJ, Gibson G, Hyams JS, Dubinsky MC. Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study. Lancet. 2017 Apr 29;389(10080):1710-1718. doi: 10.1016/S0140-6736(17)30317-3. Epub 2017 Mar 2.
citation: Forcione DG, Rosen MJ, Kisiel JB, Sands BE. Anti-Saccharomyces cerevisiae antibody (ASCA) positivity is associated with increased risk for early surgery in Crohn's disease. Gut. 2004 Aug;53(8):1117-22. doi: 10.1136/gut.2003.030734.
citation: Smids C, Horjus Talabur Horje CS, Nierkens S, Drylewicz J, Groenen MJM, Wahab PJ, van Lochem EG. Candidate Serum Markers in Early Crohn's Disease: Predictors of Disease Course. J Crohns Colitis. 2017 Sep 1;11(9):1090-1100. doi: 10.1093/ecco-jcc/jjx049.
citation: Stidham RW, Takenaka K. Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice? Gastroenterology. 2022 Apr;162(5):1493-1506. doi: 10.1053/j.gastro.2021.12.238. Epub 2022 Jan 4.
citation: Watson DS, Krutzinna J, Bruce IN, Griffiths CE, McInnes IB, Barnes MR, Floridi L. Clinical applications of machine learning algorithms: beyond the black box. BMJ. 2019 Mar 12;364:l886. doi: 10.1136/bmj.l886. No abstract available.