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

NCT05810207 | Unknown

• 1 other identifier: 2021.0626
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 8

Brief 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

Keywords

Intraoperative Prediction; Colorectal resection; Colorectal anastomotic leakage; Machine learning

Outcome Measures

Primary Outcomes (1)

• Colorectal anastomotic leakage

  colorectal anastomotic leakage is defined according to Reisinger: "clinically relevant anastomotic leakage is defined as extra luminal presence of contrast fluid on contrast-enhanced computed tomography scans and/or leakage when relaparotomy was performed, requiring reintervention or treatment."

  the occurrence of the primary outcome is assessed after 30 days postoperatively.

Secondary Outcomes (1)

• Length of Hospital Stay

  The length of hospital stay is assessed 90 days postoperatively

Other Outcomes (1)

• The predictive performance of the prediction model

  30 days postoperatively.

Study Arms (1)

Adult patients undergoing colorectal resection with the construction of an anastomosis

The exposure of interest in the current study regards the occurrence of anastomotic leakage in patients undergoing colorectal resection with the construction of an anastomosis. Information on the exposure of interest is gained by obtaining data from the patient files.

Procedure: Colorectal resection

Interventions

Colorectal resection PROCEDURE

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

Adult patients undergoing colorectal resection with the construction of an anastomosis

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Primary care clinic

You may qualify if:

• patients undergoing colorectal resection with the construction of an anastomosis
• patients with the age of 18 years or older
• patients able to give informed consent

You may not qualify if:

• when more than 25% of the target variables are missing on which the machine learning model bases the prediction
• non-elective surgeries

Sponsors & Collaborators

• Freek Daams: lead
• SAS Institute: collaborator

Study Sites (8)

Gelre Ziekenhuis

Apeldoorn, Gelderland, 7334DZ, Netherlands

RECRUITING

Slingeland Ziekenhuis

Doetinchem, Gelderland, 7009BL, Netherlands

NOT YET RECRUITING

Zuyderland MC

Heerlen, Limburg, 6419PC, Netherlands

NOT YET RECRUITING

ZGT

Almelo, Overijssel, 7609PP, Netherlands

RECRUITING

Deventer ziekenhuis

Deventer, Overijssel, 7418SE, Netherlands

RECRUITING

Medisch Spectrum Twente

Enschede, Overijssel, 7512KZ, Netherlands

NOT YET RECRUITING

Tjongerschans ziekenhuis

Heerenveen, Provincie Friesland, 8441PW, Netherlands

NOT YET RECRUITING

Meander MC

Amersfoort, Utrecht, 3813TZ, Netherlands

RECRUITING

Related Publications (15)

• Mirnezami A, Mirnezami R, Chandrakumaran K, Sasapu K, Sagar P, Finan P. Increased local recurrence and reduced survival from colorectal cancer following anastomotic leak: systematic review and meta-analysis. Ann Surg. 2011 May;253(5):890-9. doi: 10.1097/SLA.0b013e3182128929.

  PMID: 21394013 BACKGROUND

• Fujita S, Teramoto T, Watanabe M, Kodaira S, Kitajima M. Anastomotic leakage after colorectal cancer surgery: a risk factor for recurrence and poor prognosis. Jpn J Clin Oncol. 1993 Oct;23(5):299-302.

  PMID: 8230754 BACKGROUND

• Branagan G, Finnis D; Wessex Colorectal Cancer Audit Working Group. Prognosis after anastomotic leakage in colorectal surgery. Dis Colon Rectum. 2005 May;48(5):1021-6. doi: 10.1007/s10350-004-0869-4.

  PMID: 15789125 BACKGROUND

• Koedam TWA, Bootsma BT, Deijen CL, van de Brug T, Kazemier G, Cuesta MA, Furst A, Lacy AM, Haglind E, Tuynman JB, Daams F, Bonjer HJ; on behalf of the COLOR COLOR II study group. Oncological Outcomes After Anastomotic Leakage After Surgery for Colon or Rectal Cancer: Increased Risk of Local Recurrence. Ann Surg. 2022 Feb 1;275(2):e420-e427. doi: 10.1097/SLA.0000000000003889.

  PMID: 32224742 BACKGROUND

• La Regina D, Di Giuseppe M, Lucchelli M, Saporito A, Boni L, Efthymiou C, Cafarotti S, Marengo M, Mongelli F. Financial Impact of Anastomotic Leakage in Colorectal Surgery. J Gastrointest Surg. 2019 Mar;23(3):580-586. doi: 10.1007/s11605-018-3954-z. Epub 2018 Sep 13.

  PMID: 30215201 BACKGROUND

• Ingwersen EW, van der Beek PJK, Dekker JWT, van Dieren S, Daams F. One Decade of Declining Use of Defunctioning Stomas After Rectal Cancer Surgery in the Netherlands: Are We on the Right Track? Dis Colon Rectum. 2023 Jul 1;66(7):1003-1011. doi: 10.1097/DCR.0000000000002625. Epub 2023 Jan 6.

  PMID: 36607894 BACKGROUND

• Abis GSA, Stockmann HBAC, Bonjer HJ, van Veenendaal N, van Doorn-Schepens MLM, Budding AE, Wilschut JA, van Egmond M, Oosterling SJ; SELECT trial study group. Randomized clinical trial of selective decontamination of the digestive tract in elective colorectal cancer surgery (SELECT trial). Br J Surg. 2019 Mar;106(4):355-363. doi: 10.1002/bjs.11117. Epub 2019 Feb 25.

  PMID: 30802304 BACKGROUND

• Bruns ERJ, van Rooijen SJ, Argillander TE, van der Zaag ES, van Grevenstein WMU, van Duijvendijk P, Buskens CJ, Bemelman WA, van Munster BC, Slooter GD, van den Heuvel B. Improving Outcomes in Oncological Colorectal Surgery by Prehabilitation. Am J Phys Med Rehabil. 2019 Mar;98(3):231-238. doi: 10.1097/PHM.0000000000001025.

  PMID: 30153125 BACKGROUND

• van Rooijen SJ, Huisman D, Stuijvenberg M, Stens J, Roumen RMH, Daams F, Slooter GD. Intraoperative modifiable risk factors of colorectal anastomotic leakage: Why surgeons and anesthesiologists should act together. Int J Surg. 2016 Dec;36(Pt A):183-200. doi: 10.1016/j.ijsu.2016.09.098. Epub 2016 Oct 15.

  PMID: 27756644 BACKGROUND

• Stam WT, Ingwersen EW, Ali M, Spijkerman JT, Kazemier G, Bruns ERJ, Daams F. Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery. Surg Today. 2023 Oct;53(10):1209-1215. doi: 10.1007/s00595-023-02662-4. Epub 2023 Feb 25.

  PMID: 36840764 BACKGROUND

• Huisman DE, Reudink M, van Rooijen SJ, Bootsma BT, van de Brug T, Stens J, Bleeker W, Stassen LPS, Jongen A, Feo CV, Targa S, Komen N, Kroon HM, Sammour T, Lagae EAGL, Talsma AK, Wegdam JA, de Vries Reilingh TS, van Wely B, van Hoogstraten MJ, Sonneveld DJA, Veltkamp SC, Verdaasdonk EGG, Roumen RMH, Slooter GD, Daams F. LekCheck: A Prospective Study to Identify Perioperative Modifiable Risk Factors for Anastomotic Leakage in Colorectal Surgery. Ann Surg. 2022 Jan 1;275(1):e189-e197. doi: 10.1097/SLA.0000000000003853.

  PMID: 32511133 BACKGROUND

• Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.

  PMID: 25560730 BACKGROUND

• Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016 Jan 30;35(2):214-26. doi: 10.1002/sim.6787. Epub 2015 Nov 9.

  PMID: 26553135 BACKGROUND

• Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol. 2005 May;58(5):475-83. doi: 10.1016/j.jclinepi.2004.06.017.

  PMID: 15845334 BACKGROUND

• Reisinger KW, Poeze M, Hulsewe KW, van Acker BA, van Bijnen AA, Hoofwijk AG, Stoot JH, Derikx JP. Accurate prediction of anastomotic leakage after colorectal surgery using plasma markers for intestinal damage and inflammation. J Am Coll Surg. 2014 Oct;219(4):744-51. doi: 10.1016/j.jamcollsurg.2014.06.011. Epub 2014 Jun 25.

  PMID: 25241234 BACKGROUND

Related Links

• Homepage of all studies our research group is involved in, concerning the prevention and treatment of anastomotic leakage

MeSH Terms

Conditions

Anastomotic Leak

Condition Hierarchy (Ancestors)

Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Freek Daams, MD PhD

  Amsterdam UMC, location VUmc

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Erik W. Ingwersen, MD

CONTACT

0031623028928; e.ingwersen@amsterdamumc.nl

Freek Daams, MD PhD

CONTACT

0031655688542; f.daams@amsterdamumc.nl

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 90 Days
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Principal investigator, Gastrointestinal Surgeon

Study Record Dates

• First Submitted: March 30, 2023
• First Posted: April 12, 2023
• Study Start: February 1, 2022
• Primary Completion: July 1, 2024
• Study Completion: December 31, 2024
• Last Updated: April 28, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: After enrollment of patients data are available and will be for five years.
• Access Criteria: Upon request data will be available to other researchers.

Locations

NL (8)