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Ultrasound Obstet Gynecol. 2007 Jun;29(6):688-91.

Ultrasound assessment of pelvic organ prolapse: the relationship between prolapse severity and symptoms.

Author information

1
University of Sydney, Nepean Clinical School, Penrith, Australia. hpdietz@bigpond.com

Abstract

OBJECTIVE:

At present little information is available to help define whether a certain degree of pelvic organ prolapse is clinically relevant. We performed a retrospective study to define cut-offs for significant pelvic organ descent on the basis of prolapse symptoms.

METHODS:

At a tertiary urogynecological center, 735 women with symptoms of lower urinary tract dysfunction and prolapse were seen for interview, clinical examination, multi-channel urodynamics and ultrasound imaging, while supine and after voiding, for prolapse quantification. Women with multi-compartment prolapse, i.e. those in whom no compartment was clearly dominant were excluded. Receiver-operator statistics were used to test pelvic organ descent as a predictor of prolapse symptoms.

RESULTS:

Mean age was 55.1 years, mean parity 2.8 (range, 0-12). Symptoms of prolapse were reported by 188 women (25.6%). Seventy-four showed a symptomatic multi-compartment prolapse and were excluded, 56 symptomatic women had cystoceles and 48 had rectoceles. Symptomatic cystoceles descended on average to 23.8 mm below the symphysis pubis and symptomatic rectoceles to 21.4 mm below the symphysis pubis. Descent was strongly associated with symptoms of prolapse (both, P < 0.001). Receiver-operating characteristics (ROC) statistics suggested a cut-off of 10 mm below the symphysis pubis for cystocele, and 15 mm below the symphysis pubis for rectocele. ROC curves were similar for both compartments (area under the curve, 0.857 and 0.821, respectively).

CONCLUSIONS:

Descent of the bladder to > or = 10 mm and of the rectum to > or = 15 mm below the symphysis pubis are strongly associated with symptoms, and these values are proposed as cut-offs for the diagnosis of significant prolapse on the basis of ROC statistics.

PMID:
17471449
DOI:
10.1002/uog.4024
[Indexed for MEDLINE]
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