Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and Medical Record Data: Machine Learning Approach.

Journal: JMIR formative research
PMID: 40311089

Abstract

BACKGROUND: Transvaginal insertion of polypropylene mesh was extensively used in surgical procedures to treat pelvic organ prolapse (POP) due to its cost-efficiency and durability. However, studies have reported a high rate of complications, including mesh exposure through the vaginal wall. Developing predictive models via supervised machine learning holds promise in identifying risk factors associated with such complications, thereby facilitating better informed surgical decisions. Previous studies have demonstrated the efficacy of anticipating medical outcomes by employing supervised machine learning approaches that integrate patient health care data with laboratory findings. However, such an approach has not been adopted within the realm of POP mesh surgery.

Authors

  • Mihyun Lim Waugh
    Department of Biomedical Engineering, University of South Carolina, 301 Main St, Rm 2C02, Columbia, SC, 29208-4101, United States, 1 8646336181.
  • Tyler Mills
    University of South Carolina School of Medicine, Columbia, SC, United States.
  • Nicholas Boltin
    Department of Biomedical Engineering, University of South Carolina, 301 Main St, Rm 2C02, Columbia, SC, 29208-4101, United States, 1 8646336181.
  • Lauren Wolf
    Department of Biomedical Engineering, University of South Carolina, 301 Main St, Rm 2C02, Columbia, SC, 29208-4101, United States, 1 8646336181.
  • Patti Parker
    Prisma Health, Greenville, SC, United States.
  • Ronnie Horner
    Department of Health Services Research and Administration, University of Nebraska Medical Center, Omaha, NE, United States.
  • Thomas L Wheeler Ii
    Department of Obstetrics and Gynecology, Spartanburg Regional Healthcare, Spartanburg, SC, United States.
  • Richard L Goodwin
    Department of Biomedical Engineering, University of South Carolina, 301 Main St, Rm 2C02, Columbia, SC, 29208-4101, United States, 1 8646336181.
  • Melissa A Moss
    Department of Biomedical Engineering, University of South Carolina, 301 Main St, Rm 2C02, Columbia, SC, 29208-4101, United States, 1 8646336181.

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