The AIDPRO-CRC Trial

NCT06645015 | Recruiting

• 1 other identifier: p-2025-19466
• Study Type: interventional
• Target: 1,200
• Locations: 1 country
• Sites: 7

Brief Summary

The AIDPRO-CRC trial aims to improve outcomes for patients undergoing surgery for colorectal cancer by using artificial intelligence (AI) to assist surgeons in risk assessment. The trial will evaluate whether AI can help surgeons better predict the risk of complications and death, leading to improved care, fewer complications, and better use of healthcare resources. In this nationwide, randomized clinical trial, participants will be divided into two groups. One group will have their risk assessed by a surgeon using standard clinical methods, while the other group will have their risk assessed by a surgeon using AI assistance. Based on the risk level, patients will receive varying levels of perioperative care. The AI-assisted risk assessment aims to tailor the treatment more precisely to each patient's individual needs, precisely allocating care to those who need it to more efficiently allocate heath system resources while having no deterioration in patient outcomes. The primary hypothesis is that AI-assisted risk assessment will lead to more efficient and economic patient care without a deterioration in patient outcomes. The trial also aims to explore clinician satisfaction with the platform and its perceived effect. This is paired with a substudy exploring the variability of suggested treatment plans by clinicians with and without access to the MDT presentation platform. The trial will include patients at seven hospitals across Denmark, involving patients diagnosed with colorectal cancer who are scheduled for curative surgery. All patients will receive standard treatment according to national guidelines, with the only difference being the modality of risk assessment. For the evaluation of the clinicians satisfactory with the device and the substudy of variability of suggested treatment plans, the trial will enroll clinicians using the device. This study is a researcher-initiated, nationwide, randomized clinical trial involving patients diagnosed with colorectal cancer across eight hospitals in Denmark. Participants will be randomly assigned to one of two groups: AI-assisted risk assessment or standard surgeon-led assessment. The intervention focuses on optimizing perioperative care based on individual risk levels determined by either AI or the surgeon's clinical judgment. The study builds on a successful pilot project (AID-SURG) that showed promising results in reducing complications, hospital stays, and readmissions.

Conditions

Colo-rectal Cancer

Keywords

Prehabilitation; AI; Artificial Intelligence; Risk-estimation; perioperative; Post-operative complications; Risk-stratification; Algorithm

Outcome Measures

Primary Outcomes (3)

• Cost Effectiveness

  This primary endpoint is the saved marginal cost of perioperative intervention bundles achieved by integrating AI-augmented decision support. This will be assessed by comparing the overall marginal cost per patient between the AI-assisted arm (Intervention-arm) and the standard clinician-based stratification arm (control-arm). This cost-calculation factors in the following: * Distribution of patients across risk strata (A, B, C, D) * Cost per intervention bundle * Total perioperative expenditure per patient pr. bundle

  Baseline

• Perceived Effect of Clinical Support Tool & User Feedback

  This primary endpoint domain evaluates the user-perceived satisfaction with and clinical relevance of the AI-driven MDTPlatform medical device which contains the risk prediction algorithm. * Perceived relevance of the MDTPlatform provided information (i.e., the risk stratification and other displayed data) in support of making clinical decisions regarding perioperative treatment for a patient? * Perceived relevance of the displayed information provided by the MDTPlatform to clinical decision-making? * Comparison of the ability of MDTPlatform to provide a better overview of the current patient's treatment course in the context of multidisciplinary decision-making compared to usual practice? Each measured using a 7 point Likert scale where responses of 5, 6 or 7 are considered relevant All perceived clinician satisfaction with the use of the MDTPlatform will be assessed by questionnaire sent to users

  After 8 weeks of use again at 24-52 weeks of use and at after inclusion of last patient

• Variability of Suggested Treatment With and Without MDTPlatform

  This primary endpoint relates to the simulation substudy which will be carried out by letting users a subset of a set of patients. Over 1-2 sessions clinicians will evaluate a subset of a predetermined set of realistic patient cases. The total set will include 75 patients with and without MDTPlatform, yielding a total of 150 (2x75) cases. Clinicians will be asked to evaluate a minimum of 20 cases. Clinicians will score risk class based on the given data and will suggest a treatment plan, which will be recorded. The data given will be the same for cases with and without MDTPlatform, except for the risk stratification which will only be included in the cases presented via the MDTPlatform. The cases that are not presented via the MDTPlatform will be presented in the standard manner of the site where the clinician works.

  Baseline

Secondary Outcomes (7)

• The rate of complicated postoperative course 90 days after surgery

  90 days post-operative

• Postoperative Complications

  90 days Post-Operative

• Length of Hospital stay (LOS) > 4 days

  90 days post-operative

• Readmission Rates

  90 days postoperatively

• Days Alive and Out of Hospital 30 days and 90 days

  90 Days Postoperatively

Other Outcomes (13)

• Surgical & Medical Complications

  90 Days Postoperatively

• 30, 90 and 365 day mortality

  Up to 1 year post-operatively

• Return to intended oncological treatment (RIOT) for patients requiring further treatment in addition to surgery, including chemotherapy, radiotherapy, or a combination hereof.

  30 days post-operative

Study Arms (2)

AI-augmented risk-stratification

EXPERIMENTAL

* Description: An advanced AI model functions as a decision-support tool to estimate each patient's perioperative risk. * Purpose: The AI model uses various patient-specific data inputs to predict risk and assign a tailored care pathway, based on a large historical dataset. * Expected Outcome: The use of AI is expected to improve the precision of risk stratification, thereby optimizing resource utilization.

Device: AI augmented risk-stratification

Expert-based Risk-stratification

ACTIVE COMPARATOR

* Description: Experienced colorectal surgeons assess patient risk based on clinical judgment and national guidelines. * Purpose: Traditional clinical assessment is used to assign patients to the appropriate care pathway. * Expected Outcome: This arm serves as the clinical standard-of-care comparator against which the effectiveness of AI-guided decision-making is evaluated.

Other: Expert-based Risk-stratification

Interventions

AI augmented risk-stratification DEVICE

A state-of-the-art artificial intelligence (AI) model called AIDPRO manual CRC is used as a decision support tool to estimate the 1-year mortality risk for each patient

AI-augmented risk-stratification

Expert-based Risk-stratification OTHER

Experienced colorectal surgeons assess patient risk based on clinical judgment and national guidelines.

Expert-based Risk-stratification

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• To be eligible for study participation, the following criteria must be met:
• Histologically confirmed diagnosis or strong clinical suspicion of first-time colon or rectal cancer, clinical stage I-IV.
• Signed written informed consent obtained prior to any study-specific procedures.
• Age ≥18 years at the time of consent.
• Scheduled for potentially curative surgery as determined by a multidisciplinary team (MDT) conference.
• Availability of all required input variables for the AI model not directly assessed by the surgeon (e.g., ASA score, WHO performance status).

You may not qualify if:

• A patient will be excluded from the study if:
• Surgery with curative intent is no longer planned despite previous eligibility.
• Healthcare Professionals Surgeons and other healthcare professionals involved in the use of the AI-based platform will be invited to participate in two sub-studies: a user satisfaction survey and a simulation-based study. Eligible personnel will be automatically invited upon registration as platform users.
• To be eligible to participate in the survey and simulation study, individuals must:
• Be licensed medical doctors.
• Be either board-certified specialists in surgical oncology or currently in training to become one.

Sponsors & Collaborators

• Zealand University Hospital: lead
• Hvidovre University Hospital: collaborator
• Viborg Regional Hospital: collaborator
• Hillerod Hospital, Denmark: collaborator
• Aalborg University Hospital: collaborator
• Randers Regional Hospital: collaborator
• Gødstrup Hospital: collaborator
• Odense University Hospital: collaborator

Study Sites (7)

Aalborg University Hospital, Department of Gastrointestinal Surgery

Aalborg, 9000, Denmark

RECRUITING

Regional Hospital Gødstrup, Department of Surgery

Herning, 7400, Denmark

NOT YET RECRUITING

Copenhagen University Hospital - North Zealand, Hillerød, Department of Surgery

Hillerød, 3400, Denmark

NOT YET RECRUITING

Copenhagen University Hospital Hvidovre, Gastro Unit, Surgical Division

Hvidovre, 2650, Denmark

RECRUITING

Regional Hospital Randers, Department of Surgery

Randers, 8930, Denmark

RECRUITING

Odense University Hospital, Svendborg, Department of Colorectal Surgery

Svendborg, 5700, Denmark

NOT YET RECRUITING

Viborg Regional Hospital, Hospitalunit Midt, Department of Surgery

Viborg, 8800, Denmark

RECRUITING

Related Links

• Website hosting documents and information central to the study

MeSH Terms

Conditions

Colonic Neoplasms

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases

Central Study Contacts

Ismail Gögenur, Professor

CONTACT

+4526336426; igo@regionsjaelland.dk

Magnus N Jung, MD

CONTACT

+4524825249; majuj@regionsjaelland.dk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, MD, DMSc, Consultant

Study Record Dates

• First Submitted: October 9, 2024
• First Posted: October 16, 2024
• Study Start: October 24, 2025
• Primary Completion (Estimated): February 1, 2027
• Study Completion (Estimated): February 1, 2028
• Last Updated: January 30, 2026

Record last verified: 2025-12

Locations

DK (7)