A Low Cost Device for Monitoring the Urine Output of Critical Care Patients

In critical care units most of the patients’ physiological parameters are sensed by commercial monitoring devices. These devices can also supervise whether the values of the parameters lie within a pre-established range set by the clinician. The automation of the sensing and supervision tasks has discharged the healthcare staff of a considerable workload and avoids human errors, which are common in repetitive and monotonous tasks. Urine output is very likely the most relevant physiological parameter that has yet to be sensed or supervised automatically. This paper presents a low cost patent-pending device capable of sensing and supervising urine output. The device uses reed switches activated by a magnetic float in order to measure the amount of urine collected in two containers which are arranged in cascade. When either of the containers fills, it is emptied automatically using a siphon mechanism and urine begins to collect again. An electronic unit sends the state of the reed switches via Bluetooth to a PC that calculates the urine output from this information and supervises the achievement of therapeutic goals

An automatic critical care urine meter

Nowadays patients admitted to critical care units have most of their physiological parameters measured automatically by sophisticated commercial monitoring devices. More often than not, these devices supervise whether the values of the parameters they measure lie within a pre-established range, and issue warning of deviations from this range by triggering alarms. The automation of measuring and supervising tasks not only discharges the healthcare staff of a considerable workload but also avoids human errors in these repetitive and monotonous tasks. Arguably, the most relevant physiological parameter that is still measured and supervised manually by critical care unit staff is urine output (UO). In this paper we present a patent-pending device that provides continuous and accurate measurements of patient’s UO. The device uses capacitive sensors to take continuous measurements of the height of the column of liquid accumulated in two chambers that make up a plastic container. The first chamber, where the urine inputs, has a small volume. Once it has been filled it overflows into a second bigger chamber. The first chamber provides accurate UO measures of patients whose UO has to be closely supervised, while the second one avoids the need for frequent interventions by the nursing staff to empty the containe

An automatic critical care urine meter

Nowadays patients admitted to critical care units have most of their physiological parameters measured automatically by sophisticated commercial monitoring devices. More often than not, these devices supervise whether the values of the parameters they measure lie within a pre-established range, and issue warning of deviations from this range by triggering alarms. The automation of measuring and supervising tasks not only discharges the healthcare staff of a considerable workload but also avoids human errors in these repetitive and monotonous tasks. Arguably, the most relevant physiological parameter that is still measured and supervised manually by critical care unit staff is urine output (UO). In this paper we present a patent-pending device that provides continuous and accurate measurements of patient’s UO. The device uses capacitive sensors to take continuous measurements of the height of the column of liquid accumulated in two chambers that make up a plastic container. The first chamber, where the urine inputs, has a small volume. Once it has been filled it overflows into a second bigger chamber. The first chamber provides accurate UO measures of patients whose UO has to be closely supervised, while the second one avoids the need for frequent interventions by the nursing staff to empty the containe

Evaluation of an automatic urinometer including use of silicone oil to decrease biofilm formation due to proteinuria, hemoglobinuria and bacterial growth

Background: A new capacitance-based automatic urinometer (AU) facilitates continuous urine output (UO) measurement, which may help to predict and diagnose acute kidney injury (AKI). To prevent mismeasurement due to bacterial, albumin or free hemoglobin biofilm, a water-soluble capsule with silicone oil has been integrated in the device. Aims: To assess: the performance of a new capacitance-based AU in adult patients in a cardiothoracic intensive care unit (ICU) and compare it with a manual urinometer (MU) in regard of bias, precision, temporal deviation and to evaluate the staff’s opinion of the AU (Study I); a modified capacitance-based AU in comparison with an MU regarding measuring bias among patients ≤10 kg in a pediatric intensive care unit and to evaluate the staff’s opinion of the AU (Study II); whether a silicone oil-coated polypropylene plastic surface, as used in an AU, may reduce early microbial biofilm formation and to identify the silicone oil target; to compare polypropylene with polystyrene and low with medium viscosity silicone oil regarding the propensity to impede biofilm formation (Study III); if silicone oil added to the measuring chamber of the AU may prevent the rise in capacitance due to albumin or free hemoglobin biofilm, allowing the device to function for longer periods of time (Study IV). Methods: Study I-II were prospective observational cohort studies, whereas Study III-IV were experimental prospective in vitro studies. Study I: 34 postoperative patients had their hourly UO registered with either an AU (n=220) or an MU (n=188), which were validated by cylinder measurements and analyzed using the Bland-Altman method. The temporal deviation of the MU measurements was recorded (n=108) and at the end, the nursing staff (n=28) evaluated the AU. Study II: The hourly diuresis was measured using either an AU (n=127) or an MU (n=83) in 12 children (weight ≤10 kg) and validation was carried out using a measuring cylinder. Thereafter, the nursing staff (n=18) evaluated the AU. Study III: Clear flat-bottomed wells of either polypropylene or polystyrene were pretreated with silicone oil of low or medium viscosity, after which a panel of microbes, including common uropathogenic bacteria and Candida albicans, were added. The plates were left for 3 days and the amount of biofilm formation was assessed using the crystal violet assay. Study IV: A solution of Ringer’s acetate mixed with either albumin or free hemoglobin was run through an AU with either a water-soluble capsule with silicone oil (n=20) or not (n=20) and the derived 400-500 capacitance measurements, respectively, were retrieved from the AU device and analyzed. Results: Study I: The AU had a smaller mean bias (+1.9 mL) than the MU (+5.3 mL) (p<0.0001). Defined by their limits of agreements (±15.2 mL AU vs. ±16.6 mL MU, p=0.11), the measurement precision of the two urinometers were similar. The AU had inherently no temporal deviation, whereas the mean temporal deviation of the MU was ±7.4 minutes (±12.4%) (p<0.0001). The nursing staff rated the AU significantly higher than the MU in terms of user-friendliness, measuring reliability, efficacy and safety. Study II: The AU and the MU had a mean bias of −1.1 mL (CI, -0.6 to -1.5) and -0.6 mL (CI, ±0.0 to -1.2) respectively (p=0.21). The participating staff considered the AU significantly easier to learn, use and handle compared with the MU. Study III: Polypropylene plastic exhibited less biofilm growth than polystyrene. Silicone oil, irrespective of viscosity, significantly decreased biofilm formation by common uropathogenic bacteria, including ESBLproducing and multi-drug resistant strains, as well as C. albicans. E. coli curli fimbriae were established as the main focus of silicone oil. Study IV: The mean increase in capacitance with albumin 3 g/L group was 257±96 without and 105±32 with silicone oil, respectively, during 24 hours. After ten hours of registration, differences between the two albumin groups reached statistical significance. For the free hemoglobin groups (0.01 g/L), the mean increase in capacitance was 190±174 with silicone oil and 324±78 without. A significant difference between the free hemoglobin groups was seen after 20 hours and onwards. Conclusions: For adult postoperative patients, the AU was non-inferior to the MU with regard to measuring precision and significantly better than the MU in terms of bias and temporal deviation (Study I); for children weighing ≤10 kg, the urinometers were comparable in performance (Study II); staff consistently appraised the AU significantly higher than the MU in terms of user-friendliness, reliability, safety and efficacy (Study I and II). Both low and medium viscosity silicone oil coating of a polypropylene surface decreased biofilm formation from common uropathogenic bacteria including Candida albicans and the biofilm-promoting factor curli fimbriae was identified as a plausible target (Study III); coating of the capacitance measurement membrane of the AU by albumin or free hemoglobin significantly disturbed the capacitance measurement capability of the AU, and this could be prevented by incorporating silicone oil in the device (Study IV)

Smart IoTs based urine measurement system.

Urine Measurement is one of the most important processes for diagnosis in the hospitals nowadays. Acute Kidney Injury (AKI) is usually diagnosed by taking patient’s urine samples for a specific period of time. It has been suggested that the average Urine Output of a patient depends upon his weight. As we are all aware that currently the means to monitor the major vital signs of the human body in the ICU (Intensive Care Unit) or various clinical settings such as Heart Rate, Blood Pressure, Central Pressure etc. is done by the means of a continuous recording of impulses and its digital display. It is utmost necessary to record and continuously monitor a patients’ fluid input, administered mostly by electronic devices (e.g. Syringe infusion pumps). At the same time, it is also important to monitor patients’ fluid outputs, in which, urine volume is one of the major components. Currently, it is obtained intermittently (per hour) from urine meters and urine collection bags, and a visual assessment is made and recorded manually relying heavily on the nurse's capability and skills. Therefore, even after so much technological advancement the measurement of urine output is literally the only critical parameter constantly recorded and monitored non-electronically by the medical staff. The references from Medical Professionals at Royal Bournemouth Hospital clearly indicate a need for automated Urine Measurement System for efficient diagnosis process. There are automated devices for urine measurement, that are discussed in the Literature Review section, but none of them is available commercially. Some have cost issues whereas others are too complex to implement. We have found approx. 15 systems which have been patented by the inventors but none of them made it to the market. Cost-efficiency, complexity, and reliability are the issues we need to address, and we have tried to address in our project. In this project, an integrated prototype based on IoT, that measures urine volume in real time for both high and low flow is developed. The system measures the urine coming from the patient through two different sensors, Photo Interrupter Module and Hall-Effect based liquid sensor, and transmits that data to a cloud-based application via WiFi. The Arduino Yun micro-controller was used because of its built-in WiFi chip and more robust performance as compared to other options. The measurement of both high and low flow of liquid makes our system unique from the existing systems. The application at Cloud analyze the data from the sensors for visualizations as mentioned by the doctors. MATLAB analytics facilities will be used because it provides extended options for multiple real-time visualizations. The data is sent in real-time, every 20 seconds and visualizations are updated accordingly. The data is also available to view on an Android App. The real-time stream of data on cloud and ease of data accessibility distinguishes our system to those described in the literature. Series of experimentation was carried out for the prototype. Firstly, due to a problem in Photo Interrupter sensor for drop by drop measurement, the error was huge. Then, we developed an algorithm that solved the problem of object detection and then the error came to below 10% for both the high and low-flow measurement combined. This algorithm can be used to improve the working of photo interrupter sensor in other scenarios and it is one of the contributions of our project. This system decreases the workload of the nursing staff as well as that of the doctors. The human-error is minimized. The Data Analytics application enables the doctors to have an in-depth understanding of the condition of a patient at several different intervals of time. Hence, our system is expected to benefit the medical industry and especially the staff at the hospitals. Lastly, we have also found our concept to be helpful in process industry also where the liquid measurement is used and we presented this concept at EPSRC conference in Glasgow

Development of a prototype sensor-integrated urine bag for real-time measuring.

The urine output is a rapid bedside test for kidney function, and reduced output is the common biomarker for an acute kidney injury (AKI). The consensus definition of the symptom is used urine output <0.5 ml/kg/hour for ≥6 hours to define AKI. If a patient is suspected to have this problem, the urine output monitoring needs to be done hourly, and this task consumes a lot of time, and easily affected by human errors. Moreover, available evidences in literatures indicate that more frequent patient monitoring could impact clinical decision making and patient’s outcome. However, it is not possible for nurses to dedicate their precious time manually up to minute manually measurements. To date, there is no reliable device has been used in the clinical routine. From the literatures, only a few automated devices were found with the ability to automatically monitor urine outputs, and could reduce nurse workload and at the same time enhance work performance, but these still have some limitations to measure human urine. In this thesis presents the development and testing for such a device. The research was aimed at building a prototype that could be measured a small amount of urine output, and transit information via wireless to a Cloud database with inexpensive and less complex components. The concept is to provide a real-time measurement and generates data records in Cloud database without requiring any intervention by the nurse. The initial experiment was done measure small amount of liquid using a dropvolume calculation technique. An optical sensor was placed in a medical dropper to record number of counted-drops, the Mean Absolute Percent Error from the test is reported ±3.96% for measuring 35 ml of liquid compared with the ISO standard. The second prototype was developed with multi-sensors, including photo interrupter sensor, infrared proximity sensor, and ultrasonic sensor, to detect the dripping and urine flow. However, the optical sensor still provided the most accuracy of all. The final prototype is based on the combination of optical sensor for detecting drops to calculated urine flow rate and its volume, and weight scales to measurement the weight of collected urine in a commercial urine meter. The prototype also provides an alert in two scenarios; when the urine production is not met the goals, and when the urine container is almost full, the system will automatically generate alarms that warn the nurse. Series of experimentation tests have been conducted under consultant of medical professional to verify the proper operation and accuracy in the measurement. The results are improved from the previous prototype. The mean error found of this version is 1.975% or ≈ ±1.215 ml. when measure 35ml of urine under the average density value of urine (1.020). These tests confirm the potential application of the device by assisting nurse to monitor urine output with the accuracy in the measurement. The use of the Cloud based technology has not been previously reported in the literature as far as can be ascertained. These results illustrated the capability, suitability and limitation of the chosen technology