Improving urinary oxygen monitoring with a transit time algorithm: enhancing AKI detection in cardiac surgery.

Abstract

Acute kidney injury (AKI) affects 40-50% of cardiac surgery patients and is closely linked to renal medullary hypoxia. Although urinary oxygen partial pressure (PuO₂) offers real-time insight into renal oxygenation, variable urine transit times through the urinary catheter can impair measurement accuracy. This study aimed to develop an algorithm that calculates transit time by modeling urine flow as discrete particles and to assess whether it improves PuO₂ estimation. The proposed algorithm models urine flow as discrete particles, tracking transit time through the urinary catheter. The transit time allows correcting oxygen measurements at the catheter exit, mitigating distortions from variable flow rates. Validation used a bench-top system with a flow sensor, a 30-cm glass tube simulating a catheter, and optode-based oxygen sensors positioned inside a flask and at the catheter entry and exit. Flow rates spanned 20-450 mL/h, and flask oxygen 15-120 mmHg, with exit compared to entrance values. Without adjustment, the root mean square error (RMSE) between entrance and exit oxygen measurements was 15.71 mmHg. Incorporating the transit time correction reduced the RMSE to 5.82 mmHg. This marked improvement indicates that the corrected measurements more accurately reflect the true oxygen levels entering the catheter across various flow conditions. By accounting for dynamic urine transit times, the proposed algorithm substantially enhances the accuracy of urinary oxygen monitoring. This improvement in estimating renal oxygenation may facilitate noninvasive detection of renal hypoxia and allow for timely interventions to reduce the incidence and severity of AKI in cardiac surgery patients.