Ignite Creation Date:
2024-06-16 @ 11:52 AM
Last Modification Date:
2024-10-26 @ 3:32 PM
Study NCT ID:
NCT06457035
Status:
COMPLETED
Last Update Posted:
2024-06-13
First Post:
2024-06-08
Brief Title:
Development and Validation of a Nomogram for Predicting Surgery in Newly-diagnosed Crohns Disease a Retrospective Cohort Study
Sponsor:
First Affiliated Hospital Sun Yat-Sen University
Organization:
First Affiliated Hospital Sun Yat-Sen University
Study Overview
Official Title:
Development and Validation of a Nomogram for Predicting Surgery in Newly-diagnosed Crohns Disease a Retrospective Cohort Study
Status:
COMPLETED
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
A majority of patients with Crohns 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:
Crohns disease CD a chronic inflammatory disorder involving all of the gastrointestinal tract has a progressive and destructive course and is increasing in incidence worldwide1 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 diagnosis3-5 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 diagnosis3 In spite of the application of immunosuppressive maintenance therapies more than half of the patients suffer from severe complications and required intestinal resection67 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 revolutionized8 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 CD910 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 medication91112 Meanwhile several prognostic biomarkers have been discovered in pilot studies encompassing immune-related molecules and specific gene expression levels1314 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 management15 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 implementation16 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
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 medication91112 Meanwhile several prognostic biomarkers have been discovered in pilot studies encompassing immune-related molecules and specific gene expression levels1314 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 management15 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 implementation16 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None