Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study

Interstitial cystitis/bladder pain syndrome (IC/BPS) is defined as an unpleasant sensation perceived to be related to the bladder with associated urinary symptoms. Due to difficulties discriminating pelvic visceral sensation, IC/BPS likely represents multiple phenotypes with different etiologies tha...

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Autores principales:	Patricia J. Mwesigwa, Nicholas J. Jackson, Ashley T. Caron, Falisha Kanji, James E. Ackerman, Jessica R. Webb, Victoria C. S. Scott, Karyn S. Eilber, David M. Underhill, Jennifer T. Anger, A. Lenore Ackerman
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	interstitial cystitis bladder pain syndrome urinary symptoms phenotypes pelvic pain/discomfort lower urinary tract symptoms Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/b400e1a2e4824721b27e303df4c7eae4
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spelling	oai:doaj.org-article:b400e1a2e4824721b27e303df4c7eae42021-11-05T07:19:53ZUnsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study2673-561X10.3389/fpain.2021.757878https://doaj.org/article/b400e1a2e4824721b27e303df4c7eae42021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpain.2021.757878/fullhttps://doaj.org/toc/2673-561XInterstitial cystitis/bladder pain syndrome (IC/BPS) is defined as an unpleasant sensation perceived to be related to the bladder with associated urinary symptoms. Due to difficulties discriminating pelvic visceral sensation, IC/BPS likely represents multiple phenotypes with different etiologies that present with overlapping symptomatic manifestations, which complicates clinical management. We hypothesized that unique bladder pain phenotypes or “symptomatic clusters” would be identifiable using machine learning analysis (unsupervised clustering) of validated patient-reported urinary and pain measures. Patients (n = 145) with pelvic pain/discomfort perceived to originate in the bladder and lower urinary tract symptoms answered validated questionnaires [OAB Questionnaire (OAB-q), O'Leary-Sant Indices (ICSI/ICPI), female Genitourinary Pain Index (fGUPI), and Pelvic Floor Disability Index (PFDI)]. In comparison to asymptomatic controls (n = 69), machine learning revealed three bladder pain phenotypes with unique, salient features. The first group chiefly describes urinary frequency and pain with the voiding cycle, in which bladder filling causes pain relieved by bladder emptying. The second group has fluctuating pelvic discomfort and straining to void, urinary frequency and urgency without incontinence, and a sensation of incomplete emptying without urinary retention. Pain in the third group was not associated with voiding, instead being more constant and focused on the urethra and vagina. While not utilized as a feature for clustering, subjects in the second and third groups were significantly younger than subjects in the first group and controls without pain. These phenotypes defined more homogeneous patient subgroups which responded to different therapies on chart review. Current approaches to the management of heterogenous populations of bladder pain patients are often ineffective, discouraging both patients and providers. The granularity of individual phenotypes provided by unsupervised clustering approaches can be exploited to help objectively define more homogeneous patient subgroups. Better differentiation of unique phenotypes within the larger group of pelvic pain patients is needed to move toward improvements in care and a better understanding of the etiologies of these painful symptoms.Patricia J. MwesigwaNicholas J. JacksonAshley T. CaronFalisha KanjiJames E. AckermanJessica R. WebbVictoria C. S. ScottKaryn S. EilberDavid M. UnderhillJennifer T. AngerA. Lenore AckermanFrontiers Media S.A.articleinterstitial cystitisbladder pain syndromeurinary symptomsphenotypespelvic pain/discomfortlower urinary tract symptomsNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Pain Research, Vol 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	interstitial cystitis bladder pain syndrome urinary symptoms phenotypes pelvic pain/discomfort lower urinary tract symptoms Neurology. Diseases of the nervous system RC346-429
spellingShingle	interstitial cystitis bladder pain syndrome urinary symptoms phenotypes pelvic pain/discomfort lower urinary tract symptoms Neurology. Diseases of the nervous system RC346-429 Patricia J. Mwesigwa Nicholas J. Jackson Ashley T. Caron Falisha Kanji James E. Ackerman Jessica R. Webb Victoria C. S. Scott Karyn S. Eilber David M. Underhill Jennifer T. Anger A. Lenore Ackerman Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study
description	Interstitial cystitis/bladder pain syndrome (IC/BPS) is defined as an unpleasant sensation perceived to be related to the bladder with associated urinary symptoms. Due to difficulties discriminating pelvic visceral sensation, IC/BPS likely represents multiple phenotypes with different etiologies that present with overlapping symptomatic manifestations, which complicates clinical management. We hypothesized that unique bladder pain phenotypes or “symptomatic clusters” would be identifiable using machine learning analysis (unsupervised clustering) of validated patient-reported urinary and pain measures. Patients (n = 145) with pelvic pain/discomfort perceived to originate in the bladder and lower urinary tract symptoms answered validated questionnaires [OAB Questionnaire (OAB-q), O'Leary-Sant Indices (ICSI/ICPI), female Genitourinary Pain Index (fGUPI), and Pelvic Floor Disability Index (PFDI)]. In comparison to asymptomatic controls (n = 69), machine learning revealed three bladder pain phenotypes with unique, salient features. The first group chiefly describes urinary frequency and pain with the voiding cycle, in which bladder filling causes pain relieved by bladder emptying. The second group has fluctuating pelvic discomfort and straining to void, urinary frequency and urgency without incontinence, and a sensation of incomplete emptying without urinary retention. Pain in the third group was not associated with voiding, instead being more constant and focused on the urethra and vagina. While not utilized as a feature for clustering, subjects in the second and third groups were significantly younger than subjects in the first group and controls without pain. These phenotypes defined more homogeneous patient subgroups which responded to different therapies on chart review. Current approaches to the management of heterogenous populations of bladder pain patients are often ineffective, discouraging both patients and providers. The granularity of individual phenotypes provided by unsupervised clustering approaches can be exploited to help objectively define more homogeneous patient subgroups. Better differentiation of unique phenotypes within the larger group of pelvic pain patients is needed to move toward improvements in care and a better understanding of the etiologies of these painful symptoms.
format	article
author	Patricia J. Mwesigwa Nicholas J. Jackson Ashley T. Caron Falisha Kanji James E. Ackerman Jessica R. Webb Victoria C. S. Scott Karyn S. Eilber David M. Underhill Jennifer T. Anger A. Lenore Ackerman
author_facet	Patricia J. Mwesigwa Nicholas J. Jackson Ashley T. Caron Falisha Kanji James E. Ackerman Jessica R. Webb Victoria C. S. Scott Karyn S. Eilber David M. Underhill Jennifer T. Anger A. Lenore Ackerman
author_sort	Patricia J. Mwesigwa
title	Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study
title_short	Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study
title_full	Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study
title_fullStr	Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study
title_full_unstemmed	Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study
title_sort	unsupervised machine learning approaches reveal distinct phenotypes of perceived bladder pain: a pilot study
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b400e1a2e4824721b27e303df4c7eae4
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Unsupervised Machine Learning Approaches Reveal Distinct Phenotypes of Perceived Bladder Pain: A Pilot Study

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