JMIR Publications

ABSTRACT

Background:

Pelvic organ prolapse (POP) is symptomatic descent of the vaginal wall. To reduce surgical failure rates, surgical correction can be augmented with insertion of polypropylene mesh. This benefit is offset by risk of mesh complication, such as mesh exposure into the vaginal wall. If mesh placement is under consideration as part of prolapse repair, patient selection and counseling would benefit from prediction of mesh exposure, yet no such reliable preoperative method currently exists. Past studies indicate that inflammation and associated cytokine release is correlated with mesh complication. While some degree of mesh-induced cytokine response accompanies implantation, excessive or persistent cytokine responses may elicit inflammation and implant rejection.

Objective:

Here, we explore the levels of biomaterial-induced blood cytokines from patients who have undergone POP repair surgery to: (1) identify correlations among cytokine expression; and (2) predict post-surgical mesh exposure into the vaginal wall.

Methods:

Blood samples from 20 female patients who previously underwent surgical intervention with transvaginal placement of polypropylene mesh to correct POP were collected for the study. These subjects included 10 that experienced post-surgical mesh exposure and 10 that did not. Blood samples incubated with inflammatory agent lipopolysaccharide (LPS), with sterile polypropylene mesh, or alone were analyzed for plasma levels of 13 pro- and anti-inflammatory cytokines using multiplex assay. Data were analyzed by principal component analysis (PCA) to uncover associations among cytokines and identify cytokine patterns that correlate with post-surgical mesh exposure into the vaginal wall. Supervised machine learning models were created to predict presence or absence of mesh exposure and probe the number of cytokine measurements required for effective predictions.

Results:

PCA revealed that pro-inflammatory cytokines IFN-, IL-12 p70, and IL-2 are the largest contributors to the variance explained in PC 1, while anti-inflammatory cytokines IL-10, IL-4, and IL-6 are the largest contributors to the variance explained in PC 2. PCA additionally distinguished cytokine correlations that implicate prospective therapies to improve post-surgical outcomes. Among machine learning models trained with all 13 cytokines, the Artificial Neural Network, the highest performing model, predicted POP surgical outcomes with 83.3% accuracy; the same model predicted POP surgical outcomes with 77.8% accuracy when trained with just 7 cytokines, demonstrating retention of predictive capability using a smaller cytokine group.

Conclusions:

This preliminary study, incorporating a sample size of just 20 subjects, identified correlations among cytokines and demonstrated the potential of this novel approach to predict mesh exposure into the vaginal wall following transvaginal POP repair surgery. Further study with a larger sample size will be pursued to confirm these results. If corroborated, this method could provide a personalized medicine approach to assist surgeons in their recommendation of POP repair surgeries with minimal potential for adverse outcomes.