Are We Able to Optimize Outcomes and Predict Complications in Pelvic Floor Surgery With a Better Understanding of Hormonal, Microbial and Other Factors? A Report From the ICI-RS 2024.

INTRODUCTION

Pelvic organ prolapse (POP) is a common condition, affecting women worldwide and is known to have a significant impact on Health Related Quality of Life (HRQoL). Although there are various treatment options available, including pelvic floor muscle training and support pessaries, many women opt for or require surgery, with a lifetime risk of needing surgery of 12%-19%. As with any operation, this does not come without its complications and the reoperation rate following POP surgery is up to 36%. This International Consultation on Incontinence-Research Society (ICI-RS) report aims to look at the different factors which may play a role in objective and subjective outcomes following pelvic floor surgery and to summarize the evidence and uncertainties regarding prediction of POP surgical outcomes, how to optimize them and the tools available to predict them. Research question proposals to further this field have been highlighted.

METHODS

At ICI-RS 2024, the evidence for predicting the outcomes from POP surgery and methods to optimize outcomes were discussed and presented in this paper.

RESULTS

There are many reasons why POP surgery may fail, such as variations in lifestyle and occupation, persistent constipation, failure in the perineal body, connective tissue types or the shape of the pelvis. There may also be inherent conditions of the vagina, such as hormonal or microbial features. The literature lacks evidence about the potential use of advanced statistical modeling or supervised machine learning in the development of management plans for patients with POP. Furthermore, future research is needed to determine the role of UDS in the preoperative evaluation of POP patients.

CONCLUSIONS

High-quality powered studies are required to assess optimization for long-term outcomes of pelvic surgery and then, once these are well established, and possible interventions are elucidated, prediction modeling can have a real impact clinically.