Content

Welcome to the CJU website » LOG IN

Details

Near-infrared spectroscopy: validation of bladder-outlet obstruction assessment using non-invasive parameters
Department of Urological Sciences, UBC Hospital: Bladder Care Centre, University of British Columbia, Vancouver, BC, Canada
Oct  2008 (Vol.  15, Issue  5, Pages( 4241 - 4248)
PMID: 18814812

Abstract

Text-Size + 

  • INTRODUCTION:

    Near infrared spectroscopy (NIRS) is a non-invasive optical technique able to monitor changes in the concentration of oxygenated and deoxygenated hemoglobin in the bladder detrusor during bladder filling and emptying.

    OBJECTIVE:

    To evaluate the ability of a new NIRS instrument and algorithm to classify male patients with LUTS as obstructed or unobstructed based on comparison with classification via conventional invasive urodynamics (UDS). METHOD: Male patients with LUTS were recruited and underwent uroflow and urodynamic pressure flow studies with simultaneous transcutaneous NIRS monitoring following measurement of post residual volume (PVR) via ultrasound. Data analysis first classified each subject as obstructed or unobstructed using the standard pressure flow data and nomogram, then compared these results with a classification derived via a customized algorithm which analyzed the pattern of change of the NIRS data plus measurements of PVR and Qmax.

    RESULTS:

    Seventy subjects enrolled: 57 data sets had all required parameters [13 incomplete sets due to: communication error between NIRS and urodynamics instruments (9); data saving error (1); damaged fiber optic cables (3)]. Two complete data sets were excluded [subjects with hematuria (2)]. Thus data from 55 subjects was analyzed. The NIRS algorithm correctly identified those diagnosed as obstructed by conventional urodynamic classification in 24 of 28 subjects (sensitivity = 85.71%) and, and those diagnosed as unobstructed in 24 of 27 subjects (specificity = 88.89%).

    CONCLUSION:

    Scores derived from NIRS data plus PVR and Qmax are able to correctly identify > 85% of subjects classified as obstructed using UDS.