Ignite Creation Date:
2024-10-25 @ 7:50 PM
Last Modification Date:
2024-10-26 @ 3:35 PM
Study NCT ID:
NCT06516341
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-11
Brief Title:
Spatially and Temporally Resolving Predictive Biomarkers of Postoperative Recurrence and Complications in Chronic Intestinal Inflammation
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Spatially and Temporally Resolving Predictive Biomarkers of Postoperative Recurrence and Complications in Chronic Intestinal Inflammation
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
A portion of patients with Inflammatory bowel disease often require surgical intervention since they do not respond to the current therapies Besides this risk patients may develop post-operative disease complications and the factors beneath are far from being understood or predicted The investigators hypothesize that some priming factors remain in the resection margin after surgery and act as a memory of the evolution of the disease leading to the recurrence or complications The following proposals are made
1 defining and validating in humanized experimental models of intestinal inflammation the spatial and temporal dynamics of the postoperative complications-priming factors
2 integrating them into a machine-learning-driven model to determine risk indices of disease recurrence in IBD patients This risk prediction model will not change the clinical decision-making process but will only be built for research Consequently patients enrolled in this study will be monitored and treated as per the standard of care
This project will reveal possible causes and build methods predictive of postoperative complications ultimately resulting in changes in clinical management in the near future
1 defining and validating in humanized experimental models of intestinal inflammation the spatial and temporal dynamics of the postoperative complications-priming factors
2 integrating them into a machine-learning-driven model to determine risk indices of disease recurrence in IBD patients This risk prediction model will not change the clinical decision-making process but will only be built for research Consequently patients enrolled in this study will be monitored and treated as per the standard of care
This project will reveal possible causes and build methods predictive of postoperative complications ultimately resulting in changes in clinical management in the near future
Detailed Description:
A portion of patients with Inflammatory bowel disease often require surgical intervention since they do not respond to the current therapies Besides this risk patients may develop post-operative disease complications and the factors beneath are far from being understood or predicted The investigators hypothesize that some priming factors remain in the resection margin after surgery and act as a memory of the evolution of the disease leading to the recurrence or complications The following proposals are made
1 defining and validating in humanized experimental models of intestinal inflammation the spatial and temporal dynamics of the postoperative complications-priming factors
2 integrating them into a machine-learning-driven model to determine risk indices of disease recurrence in IBD patients
This risk prediction model will not change the clinical decision-making process but will only be built for research Consequently patients enrolled in this study will be monitored and treated as per standard of care This project will reveal possible causes and build methods that could help predict postoperative complications ultimately resulting in changes in clinical management
1 defining and validating in humanized experimental models of intestinal inflammation the spatial and temporal dynamics of the postoperative complications-priming factors
2 integrating them into a machine-learning-driven model to determine risk indices of disease recurrence in IBD patients
This risk prediction model will not change the clinical decision-making process but will only be built for research Consequently patients enrolled in this study will be monitored and treated as per standard of care This project will reveal possible causes and build methods that could help predict postoperative complications ultimately resulting in changes in clinical management
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None