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It is important to control daily diet, water intake and life style as well as monitor the quality of urine for urolithiasis prevention. For decades, many ion-related indices have been developed for predicting the formation of urinary stones or urolithiasis, such as EQUILs, relative supersaturation (RSS), Tiselius indices (TI), Robertson risk factor algorithms (RRFA) and more recently, the Bonn risk index. However, they mostly demand robust laboratory analysis, are work-intensive, and even require complex computational programs to get the concentration patterns of several urine analytes. A simple and fast platform for measuring multi-frequency electrical conductivity (MFEC) of morning spot urine (random urine) to predict the onset of urolithiasis was implemented in this study. The performance thereof was compared to ion-related indices, urine color and specific gravity. The concentrations of relevant ions, color, specific gravity (SG) and MFEC (MFEC tested at 1, 10, 100, 5001 KHz and 1 MHz) of 80 random urine samples were examined after collection. Then, the urine samples were stored at 4 °C for 24 h to determine whether sedimentation would occur or not. Ion-activity product index of calcium oxalate (AP(CaOx) EQ2) was calculated. The correlation between AP(CaOx) EQ2, urine color, SG and MFEC were analyzed. AP(CaOx) EQ2, urine color and MFEC (at 5 frequencies) all demonstrated good prediction (p = 0.01, 0.01, 0.01, respectively) for stone formation. The positive correlation between AP(CaOx) EQ2 and MFEC is also significant (p = 0.01). MFEC provides a good metric for predicting the onset of urolithiasis, which is comparable to conventional ion-related indices and urine color. This technology can be implemented with much ease for objectively monitoring the quality of urine at points-of-care or at home.

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