Non-invasive bladder volume measurement for the prevention of postoperative urinary retention: validation of two ultrasound devices in a clinical setting

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Abstract

Ultrasound scanning of bladder volume is used for prevention of postoperative urinary retention (POUR). Accurate assessment of bladder volume is needed to allow clinical decision-making regarding the need for postoperative catheterization. Two commonly used ultrasound devices, the BladderScan® BVI 9400 and the newly released Prime® (Verathon Medical®, Bothell, WA, USA), with or without the ‘pre-scan’ option, have not been validated in clinical practice. The aim of this study was to assess the performance of these devices in daily clinical practice. Between June and September 2016 a prospective observational study was conducted in 318 surgical patients (18 years or older) who needed a urinary catheter perioperatively for clinical reasons. For acceptable performance, we required that the volume as estimated by the BladderScan® differs by no more than 5% from the actual urine volume after catheterization. The Schuirmann’s two one-sided test was performed to assess equivalence between the BladderScan® estimate and catheterization. The BVI 9400® overestimated the actual bladder volume by + 17.5% (95% CI + 8.8 to + 26.3%). The Prime® without pre-scan underestimated by − 4.1% (95% CI − 8.8 to + 0.5%) and the Prime® with pre-scan underestimated by − 6.3% (95% CI − 11.6 to − 1.1%). This study shows that while both ultrasound devices were able to approximate current bladder volume, both BVI 9400® and Prime®—with and without pre-scan—were not able to measure the actual bladder volume within our predefined limit of ± 5%. Using the pre-scan feature of the Prime® did not further improve accuracy.

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CITATION STYLE

APA

Brouwer, T. A., van den Boogaard, C., van Roon, E. N., Kalkman, C. J., & Veeger, N. (2018). Non-invasive bladder volume measurement for the prevention of postoperative urinary retention: validation of two ultrasound devices in a clinical setting. Journal of Clinical Monitoring and Computing, 32(6), 1117–1126. https://doi.org/10.1007/s10877-018-0123-6

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