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XGBoost for Predict Incisional Hernia

NCT05718999 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2023-02-27

No results posted yet for this study

Summary

The objective of this study is to develop a predictive model of IH based on machine learning with the use of the XGBoost technique, this will help surgeons in charge of abdominal wall closure to have objective support to determine high-risk patients and in them modify the closure technique or use a mesh according to their choice or the degree of contamination of the abdominal cavity.

Conditions

  • Incisional Hernia

Interventions

DIAGNOSTIC_TEST

Not intervention

Not having intervention is an observational study

Sponsors & Collaborators

  • Hospital Regional de Alta Especialidad del Bajio

    lead OTHER

Principal Investigators

  • Edgard Efren Lozada Hernández · Hospital Regional Of High Speciality of Bajio

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-30
Primary Completion
2023-02-22
Completion
2023-11-30

Countries

  • Mexico

Study Locations

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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 NCT05718999 on ClinicalTrials.gov