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J Urol. 2007 Jan;177(1):142-8.

Discovery of morphological subgroups that correlate with severity of symptoms in interstitial cystitis: a proposed biopsy classification system.

Author information

1
University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104-6021, USA.

Abstract

PURPOSE:

We identified morphologically distinct subgroups in interstitial cystitis using cluster analysis and investigated the associations between cluster membership and urinary symptoms.

MATERIALS AND METHODS:

Of 637 patients enrolled in the Interstitial Cystitis Data Base Study 203 (32%) provided bladder biopsies at baseline screening, representing the focus of this analysis. A cluster analysis algorithm implemented in SAS PROC CLUSTER using standardized distances to measure the dissimilarity of each pair of patients with respect to select histopathological features was used to construct subgroups of these patients. Multivariate regression models for baseline nighttime and 24-hour voiding frequency, urinary urgency and pain were developed, incorporating indicator variables for cluster membership as predictors. Longitudinal urinary symptom profiles during 3 years of followup were also compared among the morphology clusters.

RESULTS:

Three morphology clusters were identified, corresponding to unique pathological groupings. In cluster C2 7 patients showed multiple pathological features of parenchymal damage, including several inflammatory features. In cluster C1 17 patients was characterized by complete denudation of the urothelium and variable edema. In cluster C0 in 179 patients none of the pathological features were present above the specified thresholds for C2. Cluster membership was significantly associated with baseline nighttime and 24-hour frequency (p <0.001, and with urinary urgency (p = 0.03). These significant increases in baseline symptom severity among clusters from C0 to C1 to C2 persisted throughout the 3 years of followup.

CONCLUSIONS:

These results suggest an important role for histopathological features in the predictive modeling of interstitial cystitis symptoms.

PMID:
17162024
DOI:
10.1016/j.juro.2006.08.096
[Indexed for MEDLINE]

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