# Using a mechanistic classification scheme to identify the causes of anterior vaginal wall prolapse and develop a validated surgical planning system

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $311,301

## Abstract

ABSTRACT
 Cystocele, or anterior vaginal wall prolapse (AVP), is the most common form of pelvic
organ prolapse, a distressing condition requiring surgery in over 200,000 women each year. It is
also the most frequent site of operative failure with a failure rate up to 30%. The successful,
complication-free and durable treatment of this problem is one of the biggest challenges facing
a gynecologist today. Our overarching hypothesis is that the pathomechanism of AVP involves
mechanical interaction between three support systems: the vaginal wall itself (SV), fascia
attachment factors (SF) (e.g., fascial attachments to the vaginal walls and the support of the
upper vagina), and the muscular support provided by the levator ani (SM). We anticipate that
primary structural impairment in one or more of these systems can lead to recoverable
deformations in other systems (i.e., secondary deformations). However, one presently lacks the
ability to identify for each woman the primary impairment sites and secondary recoverable
deformation that lead to either insufficient repair or unnecessary surgery. Given this knowledge
gap, we will develop a personalized structural-based prolapse diagnosis and surgical planning
platform. As a first step, we propose to combine MR imaging and biomechanical modeling
approaches to develop a validated virtual pelvic floor “testbed” that allow surgeons to
systematically test pathomechanics hypotheses, develop patient-specific treatment plans and
evaluate surgical outcomes.
 AIM 1. Establish classification criteria for AVP subtypes based on MRI and biomechanical
 measurements of 120 women with AVP and 30 women with normal support.
 AIM 2. Understand the pathomechanics of at least two different AIM 1 subtypes by
 comparing biomechanical model simulations with systematically implemented structural
 impairments to the AIM 1 MRI measurements.
 AIM 3. Develop and validate surgical prediction models to predict the biomechanical
 consequence of the surgical interventions on any of the support systems (SV, SF and SM) in a
 subset of 40 AIM 1 women with AVP who undergo prolapse surgery.
Upon completion of this proposal, we can classify women with AVP into different mechanistic
subtypes on which mechanistically-based surgery can be planned. We will identify the most
critical parameters that determine which operation will be successful and use these to form
the rational basis for the future randomize controlled trials to test these surgical approaches.

## Key facts

- **NIH application ID:** 9922982
- **Project number:** 5R01HD094954-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Luyun Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $311,301
- **Award type:** 5
- **Project period:** 2018-05-04 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9922982

## Citation

> US National Institutes of Health, RePORTER application 9922982, Using a mechanistic classification scheme to identify the causes of anterior vaginal wall prolapse and develop a validated surgical planning system (5R01HD094954-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9922982. Licensed CC0.

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