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Predicting Transvaginal Surgical Mesh Exposure Outcomes Using an Integrated Dataset of Blood Cytokine Levels and

Mihyun Lim Waugh¹, Tyler Mills², Nicholas Boltin¹

¹Department of Biomedical Engineering, University of South Carolina, 301 Main St, Rm 2C02, Columbia, SC, 29208-4101, United States, 1 8646336181.

JMIR Formative Research

|May 1, 2025

Summary

Supervised machine learning accurately predicts pelvic organ prolapse (POP) mesh exposure by integrating patient data and cytokine levels, achieving 94% accuracy. This approach enhances surgical decision-making and patient care.

Keywords:

cost-efficiency cytokines digital health efficacy female health care data inflammatory response informed decision-making medical record mesh surgery patient care pelvic organ prolapse polypropylene polypropylene mesh risk factor supervised machine learning models surgical outcome

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Area of Science:

Biomedical Engineering
Surgical Innovation
Machine Learning in Healthcare

Background:

Transvaginal polypropylene mesh is widely used for pelvic organ prolapse (POP) surgery.
Mesh exposure is a significant complication, necessitating better risk prediction.
Supervised machine learning offers a novel approach to identify risk factors for POP mesh complications.

Purpose of the Study:

To evaluate the efficacy of supervised machine learning in predicting mesh exposure after transvaginal POP surgery.
To compare the predictive performance of models using patient medical records, blood cytokine levels, or integrated data.

Main Methods:

Collected medical records and blood samples from 20 female patients undergoing transvaginal POP mesh surgery.
Measured cytokine levels (e.g., IL-1β, IL-12 p40, IL-8, TNF-α) in blood incubated with polypropylene mesh.
Trained machine learning models on datasets comprising medical records, cytokine levels, or both, splitting data into 70% training and 30% testing sets.

Main Results:

Patient medical data identified systolic blood pressure, pulse pressure, and alcohol history as predictors.
Cytokine analysis highlighted IL-1β and IL-12 p40 as key predictors.
Integrated data revealed IL-8, tumor necrosis factor-α, and hemorrhoids as primary predictors, achieving 94% prediction accuracy.

Conclusions:

Integrating patient medical data with blood cytokine biomarkers significantly improves the accuracy of predicting mesh exposure in POP surgery.
This hybrid approach demonstrates superior predictive performance compared to using individual data sources.
The findings suggest a promising strategy for enhancing surgical outcome prediction and patient care in procedures involving biomaterials.