MedTrace Logo

A1Check: the External Validation of a Machine Learning Model Predicting Colorectal Anastomotic Leakage

NCT05810207 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2023-04-28

No results posted yet for this study

Summary

Anastomotic leakage is a severe complication that can arise following a colorectal resection. It impairs both the short- and long-term outcomes, and negatively influences cancer recurrence rates. Its detrimental effects resound in healthcare costs of a patient after anastomotic leakage, €71,978, versus patients with an uncomplicated course, €17,647. Despite multiple innovations within the field of colorectal surgery, the incidence of colorectal anastomotic leakage did not reduce in the past decade. Mitigation strategies such as prehabilitation, intraoperative optimization, selective bowel decontamination, and reconstruction techniques are promising but do not completely eliminate the risk of leakage. The only true prevention of colorectal anastomotic leakage is the omission of an anastomosis and implies an ostomy, which in itself has a negative impact on the quality of life. A stoma is associated with stoma-related morbidity and should, therefore, be avoided in patients who do not need it. Predicting anastomotic leakage intra-operatively, just before the construction of the anastomosis, may offer a solution. A stoma will then only be constructed in those at high risk of anastomotic leakage. Currently, there are prediction models for anastomotic leakage based on conventional multivariate logistic regression analysis, however, these are not useful for clinical practice due to suboptimal results. Machine learning algorithms, on the other hand, take well into account the multifactorial nature of complications and might thus be able to predict anastomotic leakage more accurately. The machine learning model we created proved to be well capable of making accurate predictions. This model was developed based on a database containing both pre- and intra-operative data from 2,483 patients. Before these models can be used in daily practice, external validation is essential. Our models should be tested on unseen data from patients treated in centers that were not previously involved in the database that was used to train the model in order to achieve high reproducibility. Our hypothesis is that with our model, we can accurately predict anastomotic leakage intra-operatively during colorectal surgery.

Conditions

  • Anastomotic Leak
  • Anastomotic Leak Large Intestine
  • Anastomotic Complication
  • Anastomotic Leak Rectum

Interventions

PROCEDURE

Colorectal resection

Patients undergoing a colorectal resection with the construction of a primary anastomosis

Sponsors & Collaborators

  • SAS Institute

    collaborator INDUSTRY

  • Freek Daams

    lead OTHER

Principal Investigators

  • Freek Daams, MD PhD · Amsterdam UMC, location VUmc

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-02-01
Primary Completion
2024-07-01
Completion
2024-12-31

Countries

  • Netherlands

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05810207 on ClinicalTrials.gov