Division of Urogynecology and Reconstructive Pelvic Surgery, Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St. Louis, MO.
Division of Clinical Research, Department of Obstetrics and Gynecology, Washington University School of Medicine in St. Louis, St. Louis, MO.
Am J Obstet Gynecol. 2024 Sep;231(3):332.e1-332.e12. doi: 10.1016/j.ajog.2024.04.017. Epub 2024 Apr 18.
The gold-standard treatment for advanced pelvic organ prolapse is sacrocolpopexy. However, the preoperative features of prolapse that predict optimal outcomes are unknown.
This study aimed to develop a clinical prediction model that uses preoperative scores on the Pelvic Organ Prolapse Quantification examination to predict outcomes after minimally invasive sacrocolpopexy for stages 2, 3, and 4 uterovaginal prolapse and vaginal vault prolapse.
A 2-institution database of pre- and postoperative variables from 881 cases of minimally invasive sacrocolpopexy was analyzed. Data from patients were analyzed in the following 4 groups: stage 2 uterovaginal prolapse, stage 3 to 4 uterovaginal prolapse, stage 2 vaginal vault prolapse, and stage 3 to 4 vaginal vault prolapse. Unsupervised machine learning was used to identify clusters and investigate associations between clusters and outcome. The k-means clustering analysis was performed with preoperative Pelvic Organ Prolapse Quantification points and stratified by previous hysterectomy status. The "optimal" surgical outcome was defined as postoperative Pelvic Organ Prolapse Quantification stage <2. Demographic variables were compared by cluster with Student t and chi-square tests. Odds ratios were calculated to determine whether clusters could predict the outcome. Age at surgery, body mass index, and previous prolapse surgery were used for adjusted odds ratios.
Five statistically distinct prolapse clusters (phenotypes C, A, A>P, P, and P>A) were found. These phenotypes reflected the predominant region of prolapse (apical, anterior, or posterior) and whether support was preserved in the nonpredominant region. Phenotype A (anterior compartment prolapse predominant, posterior support preserved) was found in all 4 groups of patients and was considered the reference in the analysis. In 111 patients with stage 2 uterovaginal prolapse, phenotypes A and A>P (greater anterior prolapse than posterior prolapse) were found, and patients with phenotype A were more likely than those with phenotype A>P to have an optimal surgical outcome. In 401 patients with stage 3 to 4 uterovaginal prolapse, phenotypes C (apical compartment predominant, prolapse in all compartments), A, and A>P were found, and patients with phenotype A>P were more likely than those with phenotype A to have ideal surgical outcome. In 72 patients with stage 2 vaginal vault prolapse, phenotypes A, A>P, and P (posterior compartment predominant, anterior support preserved) were found, and those with phenotype A>P were less likely to have an ideal outcome than patients with phenotype A. In 297 patients with stage 3 to 4 vaginal vault prolapse, phenotypes C, A, and P>A (prolapse greater in posterior than in anterior compartment) were found, but there were no significant differences in rate of ideal outcome between phenotypes.
Five anatomic phenotypes based on preoperative Pelvic Organ Prolapse Quantification scores were present in patients with stages 2 and 3 to 4 uterovaginal prolapse and vaginal vault prolapse. These phenotypes are predictive of surgical outcome after minimally invasive sacrocolpopexy. Further work needs to confirm the presence and predictive nature of these phenotypes. In addition, whether the phenotypes represent a progression of prolapse or discrete prolapse presentations resulting from different anatomic and life course risk profiles is unknown. These phenotypes may be useful in surgical counseling and planning.
对于晚期盆腔器官脱垂，金标准治疗方法是骶骨阴道固定术。然而，预测最佳结果的脱垂术前特征尚不清楚。
本研究旨在开发一种临床预测模型，该模型使用盆腔器官脱垂定量检查的术前评分来预测 2、3 和 4 级子宫阴道脱垂和阴道穹窿脱垂的微创骶骨阴道固定术的术后结果。
对 881 例微创骶骨阴道固定术的术前和术后变量的 2 个机构数据库进行了分析。将患者的数据分为以下 4 组：2 级子宫阴道脱垂、3 至 4 级子宫阴道脱垂、2 级阴道穹窿脱垂和 3 至 4 级阴道穹窿脱垂。使用无监督机器学习来识别集群并研究集群与结果之间的关联。对术前盆腔器官脱垂定量点进行 k-均值聚类分析，并按既往子宫切除术状态分层。“最佳”手术结果定义为术后盆腔器官脱垂定量分期<2。通过学生 t 检验和卡方检验比较簇的人口统计学变量。计算比值比以确定集群是否可以预测结果。手术时的年龄、体重指数和既往脱垂手术用于调整比值比。
发现了 5 个统计学上明显不同的脱垂簇（表型 C、A、A>P、P 和 P>A）。这些表型反映了脱垂的主要部位以及非主要部位是否保留了支撑。表型 A（前腔室脱垂为主，后腔室支撑保留）见于所有 4 组患者，在分析中被认为是参考。在 111 例 2 级子宫阴道脱垂患者中，发现了表型 A 和 A>P（前腔室脱垂大于后腔室脱垂），表型 A 的患者比表型 A>P 的患者更有可能获得理想的手术结果。在 401 例 3 至 4 级子宫阴道脱垂患者中，发现了表型 C（顶腔室为主，所有腔室均有脱垂）、A 和 A>P，表型 A>P 的患者比表型 A 的患者更有可能获得理想的手术结果。在 72 例 2 级阴道穹窿脱垂患者中，发现了表型 A、A>P 和 P（后腔室为主，前腔室支撑保留），表型 A>P 的患者比表型 A 的患者不太可能获得理想的结果。在 297 例 3 至 4 级阴道穹窿脱垂患者中，发现了表型 C、A 和 P>A（后腔室脱垂大于前腔室脱垂），但表型之间的理想结果率没有显著差异。
在 2 级和 3 至 4 级子宫阴道脱垂和阴道穹窿脱垂患者中，基于术前盆腔器官脱垂定量评分的 5 种解剖表型存在。这些表型可预测微创骶骨阴道固定术的手术结果。需要进一步的工作来确认这些表型的存在和预测性质。此外，这些表型是否代表脱垂的进展，或者是否代表来自不同解剖和生命过程风险概况的离散脱垂表现尚不清楚。这些表型可能对手术咨询和计划有用。
