Automated urIne Flow Detection to Reduce Errors and Nursing Workload
Automated urIne Flow Detection to Reduce Errors and Nursing Workload
This study is an observational study which seeks to examine a) the accuracy of the Clarity Renal Monitoring System (Clarity RMS)® sensor kit at the bedside compared to manual urine output monitoring, b) total time/effort per patient with and without the device, c) the ease of use, clinical acceptance, and d) preliminary data on the detection of AKI using the Clarity RMS® sensor kit compared to standard care
The majority of physiological parameters of the patient in a critical care setting today are electronically monitored. Automation of these parameters not only reduces workload and human error, but also may provide alarms and warnings when these parameters fall below a pre-set range. Currently, urine output may be the most relevant physiological parameter that still involves manual recordings in the critical care setting.
In 2004, The Acute Dialysis Quality Initiative (ADQI), a group of experts in kidney dysfunction, proposed the RIFLE criteria for acute kidney injury (AKI). They describe Risk, Injury and Failure severity classes and Loss and End stage Kidney Disease outcome classes. The severity grades are based on serum creatinine, urinary output or both. More recently the Acute Kidney Injury Network (AKIN) stages for kidney injury added smaller relative increases in serum creatinine levels to classify patients at risk. Since the classification has been proposed, tens of thousands of patients have been involved in studies validating the RIFLE and AKIN criteria as a classification system for AKI. The 2012 Kidney Disease Improving Global Outcomes (KDIGO) AKI clinical practice guideline published in 2012 adopted modified RIFLE/AKIN criteria for classification of AKI. Studies using these criteria report kidney injury in Intensive Care Units at incidences of 50-70%.
Serum creatinine is considered a gold standard for measurement of kidney function; however, increases in its levels are seen only after there is approximately 50% loss of renal function, hindering early and sensitive detection of kidney injury and thus appropriate treatment. Many individual factors of the hospitalized patient can also interfere with the accuracy of changes in levels of serum creatinine, making this a less than ideal marker for kidney injury.
While urine output is an easily available biomarker of kidney function, only a small percentage of current studies that incorporate RIFLE and AKIN criteria utilize urinary output as a diagnostic criterion for AKI. Fluid overload has been shown to be a factor of increased mortality and further AKI. Sodium and water overload are common complications of fluid resuscitation, an initial treatment in many cases of AKI. Studies have shown that oliguria for three or more days, and a higher percentage of days with fluid overload after an initial AKI diagnosis is made, are two proven independent predictors for the development of sepsis post-AKI. In a recent study of periods of oliguria as a predictor of higher mortality in critically ill patients the authors note "treating urine flow as a continuous physiological variable instead of an interval parameter that is currently a challenge to measure accurately would provide more time points for the detection of AKI… in clinical practice, the hourly urine flow provides more precision for risk assessment and establishes early time for interventions." Current practice for measuring urine output in most hospitals worldwide involves manual recording on an hourly basis at best, and often one or two times per shift. It is essential to develop updated easy-to use- tools and systems for monitoring and managing patient fluid balance, for prevention and treatment of acute kidney injury and for patient survival. RenalSense has developed such a technology to enable online continuous monitoring of urine output and kidney function.
The current standard of care for urine output monitoring is the "manual" urometer. This approach is labor intensive, and prone to measurement error. An automated system would likely improve accuracy and reduce work load.
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