Improving the clinical prediction of detrusor overactivity by utilizing additional symptoms and signs to overactive bladder symptoms alone - Abstract

INTRODUCTION AND HYPOTHESIS: We attempted to improve the accuracy of the clinical diagnosis of detrusor overactivity (DO) by using other significant clinical parameters in addition to overactive bladder (OAB) symptoms alone.

METHODS: One thousand one hundred and forty women attending for their initial urogynecological assessment, including urodynamics, due to symptoms of pelvic floor dysfunction, underwent a comprehensive clinical and urodynamic assessment. Multivariate logistic regression analysis of a wide range of clinical parameters was used in order to determine a model of factors most accurately predicting the urodynamic diagnosis of DO. Data were separated according to women without DO; women with DO. The analysis involved the stepwise building of an optimal clinical model for predicting DO.

RESULTS: In multivariate analysis, the OAB symptoms of urgency incontinence, urgency and nocturia (not frequency) were significantly associated with DO. Their prediction of DO was not particularly accurate (sensitivity 0.64; specificity 0.67). The addition of other significant clinical parameter, i.e. absent symptoms of stress incontinence; lower parity (0-1); no signs of prolapse, to the diagnostic model, resulted in marginally improved accuracy (area under the ROC curve increased from 0.70 to 0.74).

CONCLUSIONS: Overactive bladder symptoms alone are not accurate in predicting DO. Adding other significant clinical parameters to the model resulted in a small statistical advantage, which is not clinically useful. An accurate clinical diagnosis of DO in women would appear to remain elusive.

Written by:
Haylen BT1, Chiu TL, Avery D, Zhou J, Law M.   Are you the author?
Suite 904, St Vincent's Clinic, 438 Victoria Street, Darlinghurst, 2010, NSW, Australia.  

Reference: Int Urogynecol J. 2014 Mar 25. Epub ahead of print.
doi: 10.1007/s00192-014-2362-5


PubMed Abstract
PMID: 24664215

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