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

摘要 膀胱膨出或阴道前壁脱垂（AVP）是盆腔炎最常见的形式。 器官脱垂是一种令人痛苦的疾病，每年有20多万妇女需要手术。是 也是手术失败的最常见部位，失败率高达30%。成功的， 无并发症和持久治疗这一问题是面临的最大挑战之一， 妇科医生我们的总体假设是AVP的病理机制涉及 三个支撑系统之间的机械相互作用：阴道壁本身（SV），筋膜 附着因子（SF）（例如，筋膜附着于阴道壁， 阴道上部）和肛提肌（SM）提供的肌肉支撑。我们预计 这些系统中的一个或多个中的主要结构损伤可导致可恢复的 其它系统中的变形（即，二次变形）。然而，目前缺乏 能够识别每个女性的主要损伤部位和次要可恢复部位， 变形导致修复不充分或不必要的手术。鉴于这些知识 间隙，我们将制定个性化的基于结构的脱垂诊断和手术计划 平台作为第一步，我们建议结合联合收割机磁共振成像和生物力学建模 开发一个经过验证的虚拟骨盆底“试验台”的方法， 系统地测试病理力学假设，制定针对患者的治疗计划， 评估手术结果。 AIM 1.基于MRI和生物力学建立AVP亚型的分类标准 测量120名患有AVP的妇女和30名正常支持的妇女。 AIM 2.了解至少两种不同的AIM 1亚型的病理机制， 将生物力学模型模拟与系统实施的结构 对AIM 1 MRI测量的损害。 AIM 3.开发并验证手术预测模型，以预测生物力学 手术干预对任何支持系统（SV、SF和SM）的影响， 接受脱垂手术的40名患有AVP的AIM 1女性亚组。 完成这项建议后，我们可以将患有AVP的女性分为不同的机制， 可以计划基于机械的手术的亚型。我们会找出 决定哪个操作将成功的关键参数，并使用这些参数形成 为将来的随机对照试验提供了合理的依据。

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.