Symptom Based Clustering of Women in the LURN Observational Cohort Study

J Urol. 2018 Dec;200(6):1323-1331. doi: 10.1016/j.juro.2018.06.068. Epub 2018 Jul 7.


Purpose: Women with lower urinary tract symptoms are often diagnosed based on a predefined symptom complex or a predominant symptom. There are many limitations to this paradigm as often patients present with multiple urinary symptoms which do not perfectly fit the preestablished diagnoses. We used cluster analysis to identify novel, symptom based subtypes of women with lower urinary tract symptoms.

Materials and methods: We analyzed baseline urinary symptom questionnaire data obtained from 545 care seeking female participants enrolled in the LURN (Symptoms of Lower Urinary Tract Dysfunction Research Network) Observational Cohort Study. Symptoms were measured with the LUTS (lower urinary tract symptoms) Tool and the AUA SI (American Urological Association Symptom Index), and analyzed using a probability based consensus clustering algorithm.

Results: Four clusters were identified. The 138 women in cluster F1 did not report incontinence but experienced post-void dribbling, frequency and voiding symptoms. The 80 women in cluster F2 reported urgency incontinence as well as urgency and frequency but minimal voiding symptoms or stress incontinence. Cluster F3 included 244 women who reported all types of incontinence, urgency, frequency and mild voiding symptoms. The 83 women in cluster F4 reported all lower urinary tract symptoms at uniformly high levels. All but 2 of 44 LUTS Tool and 8 AUA SI questions significantly differed between at least 2 clusters (p <0.05). All clusters contained at least 1 member from each conventional group, including continence, and stress, urgency, mixed and other incontinence.

Conclusions: Women seeking care for lower urinary tract symptoms cluster into 4 distinct symptom groups which differ from conventional clinical diagnostic groups. Further validation is needed to determine whether management improves using this new classification.

Keywords: cluster analysis; diagnosis-related groups; lower urinary tract symptoms; overactive; surveys and questionnaires; urinary bladder.

Publication types

  • Observational Study

MeSH terms

  • Algorithms
  • Cluster Analysis
  • Cohort Studies
  • Female
  • Humans
  • Lower Urinary Tract Symptoms / diagnosis*
  • Lower Urinary Tract Symptoms / therapy
  • Middle Aged
  • Surveys and Questionnaires / statistics & numerical data
  • Urinary Bladder