Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach

Abstract

BackgroundPelvic organ prolapse (POP) refers to symptomatic descent of the vaginal wall. To reduce surgical failure rates, surgical correction can be augmented with the insertion of polypropylene mesh. This benefit is offset by the risk of mesh complication, predominantly mesh exposure through the vaginal wall. If mesh placement is under consideration as part of prolapse repair, patient selection and counseling would benefit from the 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. ObjectiveHere, 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 postsurgical mesh exposure through the vaginal wall. MethodsBlood 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 included 10 who experienced postsurgical mesh exposure through the vaginal wall and 10 who did not. Blood samples incubated with inflammatory agent lipopolysaccharide, with sterile polypropylene mesh, or alone were analyzed for plasma levels of 13 proinflammatory 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 postsurgical mesh exposure through the vaginal wall. Supervised machine learning models were created to predict the presence or absence of mesh exposure and probe the number of cytokine measurements required for effective predictions. ResultsPCA revealed that proinflammatory cytokines interferon gamma, interleukin 12p70, and interleukin 2 are the largest contributors to the variance explained in PC 1, while anti-inflammatory cytokines interleukins 10, 4, and 6 are the largest contributors to the variance explained in PC 2. Additionally, PCA distinguished cytokine correlations that implicate prospective therapies to improve postsurgical outcomes. Among machine learning models trained with all 13 cytokines, the artificial neural network, the highest performing model, predicted POP surgical outcomes with 83% (15/18) accuracy; the same model predicted POP surgical outcomes with 78% (14/18) accuracy when trained with just 7 cytokines, demonstrating retention of predictive capability using a smaller cytokine group. ConclusionsThis preliminary study, incorporating a sample size of just 20 participants, identified correlations among cytokines and demonstrated the potential of this novel approach to predict mesh exposure through 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.