NCT05810207: A1Check

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

Status unknown Last updated 28 April 2023

What this trial tests

trial testing Colorectal resection in Anastomotic Leak in 1,000 participants. Status unknown.

Timeline

1 February 2022

Primary endpoint
1 July 2024

31 December 2024

Quick facts

Lead sponsor	Freek Daams
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	1,000
Start date	1 February 2022
Primary completion	1 July 2024
Estimated completion	31 December 2024
Sites	8 locations across Netherlands

Drugs / interventions tested

• Colorectal resection

Conditions studied

• Anastomotic Leak — all drugs for Anastomotic Leak →
• Anastomotic Leak Large Intestine — all drugs for Anastomotic Leak Large Intestine →
• Anastomotic Complication — all drugs for Anastomotic Complication →
• Anastomotic Leak Rectum — all drugs for Anastomotic Leak Rectum →

Sponsor

Freek Daams

Who can join

18 and older, any sex, with Anastomotic Leak or Anastomotic Leak Large Intestine. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.