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

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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.