Clinical research studies often collect data via repeated measurements of collected urine. Unfortunately, the accuracy of timed urine collections is limited by the presence of a residual volume of urine remaining in the bladder following each timed void due to incomplete emptying of the bladder. This residual urine volume adds significant imprecision to the urine collection method, rendering an important and fundamental clinical research tool inaccurate. We present an unbiased method to estimate the residual bladder volumes via a mathematical model of the bladder process. Regardless of the substance of primary interest, the model leverages conservation of mass and conservation of concentration principles towards a substance of secondary interest in order to solve a system of recursive equations, resulting in our Recursive Residual Estimation method to predict the residual volumes at each time point. We verify the model on simulated patients and also investigate the sensitivity of the model to initial value specification.
ASJC Scopus subject areas
- Statistics and Probability
- Health Information Management