Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence

NCT ID: NCT05204186

Last Updated: 2023-02-08

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-01-10
• Study Completion Date: 2024-01-31

Brief Summary

Clinician and the multidisciplinary team meeting in oncologic urology (MMO) play a key-role in the decision making. An unexplained surgeon attributable variance, probably linked to the subjective "eyeball test" effect, was identified as a strongest factor underlying non-compliance with guide line recommendations in the management of bladder cancer. So high-quality studies that identify barriers and modulators (such as comorbidities) of provider-level adoption of guidelines and how comorbidities are associated in making therapeutic choice and their impact in bladder cancer specific survival and overall survival, are crucial. To identify patients at high risk of early death, and to improve specific guideline for treatment might be decisive.

In order to assess survival, where mortality events compete, it will be more appropriate to compute a Cumulative Incidence Function (namely CIF). The investigators will compare outcomes across patient populations to obtain information to improve clinical decision-making. Such learning will be done through the use of neural networks or by applying population-based approaches, such as Genetic Algorithms (GA), Ant Colony Systems (ACS) and Particle Swarm Optimization (PSO), using as a four-stage based approach.

First, the investigators propose a "pretopology space" in order to study a dynamic phenomenon. Second, the investigators recall that the K-means approach remains one of the most used approaches for classifying a set of elements (patients / persons / others) into K (disjunctive) clusters. Third, the investigators propose a learning pretopology space for enhancing the clustering. Such an approach can be assimilated in spirit to one applied with high success on deep learning. Fourth and last, the investigators propose a reactive method that is able to include some new elements or remove some contained elements

Conditions

Bladder Cancer; Comorbidity; Deep Learning

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Study Groups

Group A

Patient with (Group A) any Grade 3 (and over) Clavien-Dindo grading complication rate (30dC and 90dC)

No interventions assigned to this group

Group B

Patient without (Group B) any Grade 3 (and over) Clavien-Dindo grading complication rate (30dC and 90dC)

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• 18 years and older
• Patient treated by radical cystectomy for bladder cancer

Exclusion Criteria

• Computed tomography/magnetic resonance evidence of distant metastases.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Centre Hospitalier Universitaire, Amiens

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

CHU Amiens Picardie

Amiens, Picardie, France

Site Status: RECRUITING

Countries

France

Central Contacts

Fabien SAINT, Pr

Role: CONTACT

saint-fabien@chu-amiens.fr

03 22 45 59 52

Facility Contacts

fabien saint, Pr

Role: primary

saint-fabien@chu-amiens.fr

03 22 45 59 52

Other Identifiers

PI2021_843_0176

Identifier Type: -

Identifier Source: org_study_id