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Abstract：
OBJECTIVE: As interstitial cystitis/bladder pain syndrome (IC/BPS) likely represents multiple pathophysiologies, we sought to validate three clinical phenotypes of IC/BPS patients in a large, multi-center cohort using unsupervised machine learning (ML) analysis.
METHODS: Using the female Genitourinary Pain Index and O\'Leary-Sant Indices, k-means unsupervised clustering was utilized to define symptomatic phenotypes in 130 premenopausal IC/BPS participants recruited through the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network. Patient-reported symptoms were directly compared between MAPP ML-derived phenotypic clusters to previously defined phenotypes from a single center (SC) cohort.
RESULTS: Unsupervised ML categorized IC/BPS participants into three phenotypes with distinct pain and urinary symptom patterns: myofascial pain, non-urologic pelvic pain, and bladder-specific pain. Defining characteristics included presence of myofascial pain or trigger points on examination for myofascial pain patients (p = 0.003) and bladder pain/burning for bladder-specific pain patients (p < 0.001). The three phenotypes were derived using only 11 features (fGUPI subscales and ICSI/ICPI items), in contrast to 49 items required previously. Despite substantial reduction in classification features, unsupervised ML independently generated similar symptomatic clusters in the MAPP cohort with equivalent symptomatic patterns and physical examination findings as the SC cohort.
CONCLUSIONS: The reproducible identification of IC/BPS phenotypes, distinguishing bladder-specific pain from myofascial and genital pain, using independent ML analysis of a multicenter database suggests these phenotypes reflect true pathophysiologic differences in IC/BPS patients.
METHODS: Using the female Genitourinary Pain Index and O\'Leary-Sant Indices, k-means unsupervised clustering was utilized to define symptomatic phenotypes in 130 premenopausal IC/BPS participants recruited through the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network. Patient-reported symptoms were directly compared between MAPP ML-derived phenotypic clusters to previously defined phenotypes from a single center (SC) cohort.
RESULTS: Unsupervised ML categorized IC/BPS participants into three phenotypes with distinct pain and urinary symptom patterns: myofascial pain, non-urologic pelvic pain, and bladder-specific pain. Defining characteristics included presence of myofascial pain or trigger points on examination for myofascial pain patients (p = 0.003) and bladder pain/burning for bladder-specific pain patients (p < 0.001). The three phenotypes were derived using only 11 features (fGUPI subscales and ICSI/ICPI items), in contrast to 49 items required previously. Despite substantial reduction in classification features, unsupervised ML independently generated similar symptomatic clusters in the MAPP cohort with equivalent symptomatic patterns and physical examination findings as the SC cohort.
CONCLUSIONS: The reproducible identification of IC/BPS phenotypes, distinguishing bladder-specific pain from myofascial and genital pain, using independent ML analysis of a multicenter database suggests these phenotypes reflect true pathophysiologic differences in IC/BPS patients.
摘要：
目的：由于间质性膀胱炎/膀胱疼痛综合征（IC/BPS）可能代表多种病理生理，我们试图验证IC/BPS患者的三种临床表型，使用无监督机器学习(ML)分析的多中心队列。
方法：使用女性泌尿生殖系统疼痛指数和O'Leary-Sant指数，在通过多学科慢性盆腔疼痛研究（MAPP）研究网络招募的130名绝经前IC/BPS参与者中，利用k-means无监督聚类来定义症状表型。将患者报告的症状在MAPPML衍生的表型簇与来自单中心（SC）队列的先前定义的表型之间进行直接比较。
结果：无监督ML将IC/BPS参与者分为三种具有不同疼痛和泌尿症状模式的表型：肌筋膜疼痛，非泌尿系盆腔疼痛,和膀胱特异性疼痛。定义特征包括肌筋膜疼痛患者的检查中存在肌筋膜疼痛或触发点(p=0.003)和膀胱特异性疼痛患者的膀胱疼痛/灼烧(p<0.001)。三种表型仅使用11个特征（fGUPI分量表和ICSI/ICPI项目）得出，与之前要求的49项相比。尽管分类特征大幅减少，无监督ML在MAPP队列中独立产生相似的症状聚类,其症状模式和体格检查结果与SC队列相当.
结论：IC/BPS表型的可重复鉴定，将膀胱特异性疼痛与肌筋膜和生殖器疼痛区分开来，使用多中心数据库的独立ML分析提示,这些表型反映了IC/BPS患者的真实病理生理差异.
方法：使用女性泌尿生殖系统疼痛指数和O'Leary-Sant指数，在通过多学科慢性盆腔疼痛研究（MAPP）研究网络招募的130名绝经前IC/BPS参与者中，利用k-means无监督聚类来定义症状表型。将患者报告的症状在MAPPML衍生的表型簇与来自单中心（SC）队列的先前定义的表型之间进行直接比较。
结果：无监督ML将IC/BPS参与者分为三种具有不同疼痛和泌尿症状模式的表型：肌筋膜疼痛，非泌尿系盆腔疼痛,和膀胱特异性疼痛。定义特征包括肌筋膜疼痛患者的检查中存在肌筋膜疼痛或触发点(p=0.003)和膀胱特异性疼痛患者的膀胱疼痛/灼烧(p<0.001)。三种表型仅使用11个特征（fGUPI分量表和ICSI/ICPI项目）得出，与之前要求的49项相比。尽管分类特征大幅减少，无监督ML在MAPP队列中独立产生相似的症状聚类,其症状模式和体格检查结果与SC队列相当.
结论：IC/BPS表型的可重复鉴定，将膀胱特异性疼痛与肌筋膜和生殖器疼痛区分开来，使用多中心数据库的独立ML分析提示,这些表型反映了IC/BPS患者的真实病理生理差异.
