Cluster analysis and lower urinary tract symptoms in men:: findings from the Boston Area Community Health Survey

OBJECTIVES To classify lower urinary tract symptoms (LUTS) in a large, representative sample of men in the USA by means of cluster analysis and to investigate risk factors and comorbidities associated with the resulting cluster patterns. SUBJECTS AND METHODS A combination of hierarchical and non-hierarchical cluster methods was used to assign men with LUTS in the Boston Area Community Health (BACH) study to symptom-based categories or clusters. Of the 2301 men in the BACH study, those reporting one or more of 14 common LUTS (1592 men) were included in the analysis. The prevalence and frequency of symptoms in each cluster was assessed, in addition to the demographic, lifestyle risk factors, comorbidities, quality of life, and interference with activities of daily living associated with each cluster. We used ANOVA methods for assessing cluster effects on continuous variables, and cross-classification and chi-square tests for categorical measures. Internal validity of the cluster solution was tested by means of a split-half replication, and external validity by comparison with previously published data. RESULTS Five clusters were identified among symptomatic men. About half of the symptomatic men were assigned to Cluster 1, which included individuals with a low prevalence and frequency of urological symptoms and a correspondingly low level of interference with activities of daily living. There were intermediate levels of symptom frequency and prevalence in Clusters 2-4, which were characterized by mixed patterns of voiding, storage and postvoiding symptoms. Cluster 5 consisted of predominantly older men (mean age 58.9 years), with a high prevalence and frequency of urological symptoms with a mean (SD) number of symptoms of 9.9 (2.1), and with elevated levels of comorbid cardiovascular disease (P < 0.001). These men also had higher rates of kidney and bladder infections and previous urological surgery. Men with increased waist circumference and more sedentary lifestyles were over-represented in the more symptomatic clusters. CONCLUSION Cluster analysis provides an empirically based method for categorizing men with LUTS. These findings provide a new framework for examining aetiological pathways and mechanisms, the potential impact of and consequences for comorbid conditions, and for assessing prognosis and outcomes associated with common urological disorders.