A device for automatically measuring and supervising the critical care patient's urine output

Accuracy and availability of automated urine output monitoring in the operating room using a smart scale

PURPOSE

Urine output (UO) is an important intraoperative parameter that is not yet electronically monitored. We compared an automatic urinometer (AU) based on a smart scale with a manual urinometer (MU).

PATIENTS AND METHODS

This prospective study investigated the hourly UO of 35 preoperative patients with an indwelling urinary catheter using AU, MU, and cylinder measurements. Data were analyzed using the Bland-Altman method. A questionnaire related to the use of the AU was completed by medical staff (n=25).

RESULTS

Compared to the cylinder measurements, the differences in measurements by the AU and the MU were -6.31 ​± ​15.03 ​mL/h (p=0.018) and 20.26 ​± ​26.81 ​mL/h (p=0.001), respectively. The r values for the comparison of cylinder measurements with AU and MU values were 0.985 (p<0.001) and 0.968 (p<0.001), respectively. Bland-Altman analyses showed that cylinder measurements had better agreement with the AU measurements than with the MU measurements. Also, the medical staff reported that the use of the AU was easier to learn than the use of the MU (p<0.001).

CONCLUSIONS

Compared to the MU values, AU values were noninferior; they had significantly less bias and temporal deviation. Additionally, the medical staff reported that the use of the AU was easier to learn than the use of the MU.

Development of a urinometer for automatic measurement of urine flow in catheterized patients

Urinary flow measurement and colorimetry are vital medical indicators for critically ill patients in intensive care units. However, there is a clinical need for low-cost, continuous urinary flow monitoring devices that can automatically and in real-time measure urine flow. This need led to the development of a non-invasive device that is easy to use and does not require proprietary disposables. The device operates by detecting urine flow using an infrared barrier that returns an unequivocal pattern, and it is capable of measuring the volume of liquid in real-time, storing the history with a precise date, and returning alarms to detect critical trends. The device also has the ability to detect the color of urine, allowing for extended data and detecting problems in catheterized patients such as hematuria. The device is proposed as an automated clinical decision support system that utilizes the concept of the Internet of Medical Things. It works by using a LoRa communication method with the LoRaWAN protocol to maximize the distance to access points, reducing infrastructure costs in massive deployments. The device can send data wirelessly for remote monitoring and allows for the collection of data on a dashboard in a pseudonymous way. Tests conducted on the device using a gold standard medical grade infusion pump and fluid densities within the 1.005 g/ml to 1.030 g/ml urine density range showed that droplets were satisfactorily captured in the range of flows from less than 1 ml/h to 500 ml/h, which are acceptable ranges for urinary flow. Errors ranged below 15%, when compared to the values obtained by the hospital infusion pump used as gold standard. Such values are clinically adequate to detect changes in diuresis patterns, specially at low urine output ranges, related to renal disfunction. Additionally, tests carried out with different color patterns indicate that it detects different colors of urine with a precision in detecting RGB values <5%. In conclusion, the results suggest that the device can be useful in automatically monitoring diuresis and colorimetry in real-time, which can facilitate the work of nursing and provide automatic decision-making support to intensive care physicians.

Research of System Design and Automatic Detection Method for Excretion Nursing Equipment

(1) Background: The nursing of the elderly has received more and more attention, especially the nursing of urination and defecation for the elderly. (2) Purpose: Design an excretion nursing equipment that can accurately identify and deal with urine and stool. (3) Methods: In this paper, based on the analysis of the requirements of excretion nursing equipment, a split mechanical design method and a modular control method are used to design the equipment. The Dempster-Shafer (D-S) evidence theory is used in the identification of urine and stool. (4) Results: The excretion nursing equipment designed in this paper works well according to functional test, and the success rate of stool and urine identification method using D-S evidence theory is 20% higher than that of traditional methods, reaching 90%. (5) Conclusions: The urine and stool recognition and detection algorithm based on the D-S evidence theory used in this paper can improve the recognition accuracy of traditional detection methods, and the designed excretion nursing equipment can realize the function of excretion care for patients.

Accuracy of Visual Assessment of Urimeter Bag Volumes: The Whiz Quiz

IMPORTANCE

Although visual estimate of urine output via urimeter bag is common, data on accuracy are limited.

OBJECTIVE

This study aimed to assess the accuracy of a visual estimate of urine output in standard urimeter bags by health care workers.

STUDY DESIGN

This is a prospective observational study. Perioperative health care workers were asked to visually estimate fluid volumes in 5 standard urimeter bags. Actual volumes were 50, 150, 350, 500, and 750 mL. Visual estimates were recorded. The primary outcome was accuracy, defined as estimated visual volume within 20% of actual volume. Secondary outcomes included effect of health care provider type, specialty, experience, sex, and age on accuracy.

RESULTS

A total of 159 responses were analyzed. There were 55 (35.3%) registered nurses, 19 (12.2%) certified registered nurse anesthetists, 18 (11.5%) advanced practice providers not identified as a certified registered nurse anesthetist, and 64 (41%) physicians. Mean estimated volumes (in milliliters) ± standard deviation and accuracy for the bags were as follows: (a) actual volume of 50 mL and estimated volume of 66 ± 29 mL (45% accuracy), (b) actual volume of 150 mL and estimated volume of 149 ± 43 mL (46% accuracy), (c) actual volume of 350 mL and estimated volume of 356 ± 74 mL (76% accuracy), (d) actual volume of 500 mL and estimated volume of 452 ± 77 mL (85% accuracy), and (e) actual volume of 750 mL and estimated volume of 675 ± 108 mL (85% accuracy). There was reasonable accuracy for individual volume estimates, but accuracy across all 5 urimeter bags was low: 22 of 159 (13.8%). There were no significant differences in accuracy based on health care provider type, specialty, experience, sex, or age.

CONCLUSIONS

Consistent accuracy of visual assessment of calibrated urimeter bag volumes was low and not influenced by health care provider characteristics.

Automated versus manual urine output monitoring in the intensive care unit

Acute kidney injury (AKI) is defined by changes in serum creatinine and urine output (UO). Significant limitations exist regarding accurate ascertainment of urine output even within the intensive care unit. We sought to evaluate an automated urine output collections system and compare it to nursing measurements. We prospectively collected urine output using an electronic urine monitoring system and compared it to charted hourly UO in 44 patients after cardiac surgery at a single university hospital ICU. We calculated UO and oliguria rates and compared them to data from the sensor and from nursing charting. A total of 187 hourly UO measurements were obtained and on average, UO was reported 47 min late, with a median of 18 min, and a maximum of almost 6 h. Patients had a mean hourly UO of 76.3 ml over the observation period. Compared to manual measurements by study personnel, nurses significantly overestimated hourly UO by 19.9 ml (95% CI: 10.3; 29.5; p =  < 0.001). By contrast, the mean difference between the UO measured with the sensor and by study personnel was 2.29 ml (95% CI: − 6.7; 11.3), p = 0.61. Electronic UO monitoring is significantly more accurate than nurse-performed manual measurements in actual intensive care patients. Furthermore, timely ascertainment of UO is difficult to achieve with manual technique, resulting in important delays in detecting oliguria perhaps leading to missed cases of AKI.

Silicone Oil Decreases Biofilm Formation in a Capacitance-Based Automatic Urine Measurement System

Capacitance-based automatic urine measurement is a validated technique already implemented in clinical practice. However, albuminuria and free hemoglobinuria cause progressive biofilm buildup on the capacitance sensors of the urinometers. The aim of this experimental study is to investigate the influence of albumin and free hemoglobin on the capacitance signal of an automatic urinometer with and without the addition of silicone oil. A solution of Ringer’s acetate mixed with either albumin or free hemoglobin was run through an automatic urinometer containing either a water-soluble capsule with silicone oil or not. In total, around 500 capacitance measurements were retrieved from the albumin and free hemoglobin group, respectively. The mean increase in capacitance in the albumin 3 g/L group was 257 ± 100 pF without and 105 ± 30 pF with silicone oil, respectively, during 24 h. After ten hours of recording, 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 ± 170 pF with silicone oil, and 324 ± 80 pF without, with a significant difference between the groups after 20 h and onwards. Coating of the capacitance measurement membrane of the automatic urinometer by albumin or free hemoglobin was significantly decreased by silicone oil, prolonging the functionality of the device.

UScale: a digital device for automatic urine volume measurement and frequency volume charting

Background

Health issues relating to the lower urinary tract are an increasing burden on the health economy. Measurement of urination frequency/volume using diaries to evaluate symptoms and assess severity is established in the management of these health problems. In current practice, these frequency volume diaries are completed by voiding into a measuring jug and the completion of paper or digital charts. Despite being shown useful to diagnosis, this can be a cumbersome method of data collection, leading to issues with patient compliance. In this paper we describe the established benefits of providing clinicians accurate micturition data followed by an analysis of the problems with the current data collection method.

Methods

We introduce our prototype electronic device and accompanying method, which is designed to improve data accuracy and patient compliance, while reducing patient training requirements and clinician workload.

Results

The device hardware calibration and testing procedure is described, and two sets of initial data from assumed healthy volunteers are presented, allowing us to demonstrate the advantages of digital data in the fast calculation of diary summary statistics and their potential use to clinicians.

Conclusions

We discuss the design improvements to the UScale device, collection bag, and electronic medical records integration undertaken while validating our described method.

Factors that confound the prediction of renal medullary oxygenation and risk of acute kidney injury from measurement of bladder urine oxygen tension

AIM

Urinary oxygen tension (uPO₂ ) may provide an estimate of renal medullary PO₂ (mPO₂ ) and thus risk of acute kidney injury (AKI). We assessed the potential for variations in urine flow and arterial PO₂ (aPO₂ ) to confound these estimates.

METHODS

In 28 sheep urine flow, uPO₂ , aPO₂ and mPO₂ were measured during development of septic AKI. In 65 human patients undergoing cardiac surgery requiring cardiopulmonary bypass (CPB) uPO₂ and aPO₂ were measured continuously during CPB, and in a subset of 20 patients, urine flow was estimated every 5 minutes.

RESULTS

In conscious sheep breathing room air, uPO₂ was more closely correlated with mPO₂ than with aPO₂ or urine flow. The difference between mPO₂ and uPO₂ varied little with urine flow or aPO₂ . In patients, urine flow increased abruptly from 3.42 ± 0.29 mL min^-1 to 6.94 ± 0.26 mL min^-1 upon commencement of CPB, usually coincident with reduced uPO₂ . During hyperoxic CPB high values of uPO₂ were often observed at low urine flow. Low urinary PO₂ during CPB (<10 mm Hg at any time during CPB) was associated with greater (4.5-fold) risk of AKI. However, low urine flow during CPB was not significantly associated with risk of AKI.

CONCLUSIONS

uPO₂ provides a robust estimate of mPO₂ , but this relationship is confounded by the simultaneous presence of systemic hyperoxia and low urine flow. Urine flow increases and uPO₂ decreases during CPB. Thus, CPB is probably the best time to use uPO₂ to detect renal medullary hypoxia and risk of post-operative AKI.

Validation of a Real-Time Minute-to-Minute Urine Output Monitor and the Feasibility of Its Clinical Use for Patients Undergoing Cardiac Surgery

Acute kidney injury after cardiac surgery is associated with increased morbidity and mortality. Methods for measuring urine output in real time may better ensure renal perfusion perioperatively in contrast to the current standard of care where urine output is visually estimated after empiric epochs of time. In this study, we describe an accurate method for monitoring urine output continuously during cardiopulmonary bypass. This may provide a means for setting patient-specific targets for blood pressure and cardiopulmonary bypass flow as a potential strategy to reduce the risk for acute kidney injury.

Correlations between physiological parameters related with kidney function and minute-by-minute urine output

AIM

Recently, devices capable of measuring minute-by-minute urine output (UOm) have become available. It is not known how UOm correlates with physiological parameters in normal conditions and in disease states characterized by vascular dysfunction. This paper analyzes correlations between UOm and physiological parameters related to kidney perfusion to provide some insight about UOm pathophysiological interpretation and its relationship with renal blood flow.

METHODS

We studied 14 male pigs were anesthetized, tracheostomized, and mechanically ventilated. Mean systemic blood pressure (P_ART ), mean pulmonary artery blood pressure (P_PA ), carotid artery blood flow (Q_CA ), as well as total (Q_REN ), cortical (Q_COR ) and medullary (Q_MED ) renal blood flows, and the renal resistive index (RRI) were measured or calculated. Animals received an intravenous dose of live E. coli for the induction of sepsis (septic group), or an equivalent amount of normal saline (nonseptic group). Three groups were studied: nonseptic (n = 6) and septic (n = 4), both receiving for resuscitation NaCl 0.9% at 4 mL/kg per h; and septic (n = 4), receiving for resuscitation NaCl 0.9% at 17 mL/kg per h. Animals were monitored for 5 h after the induction of sepsis.

RESULTS

In septic animals, UOm was strongly positively correlated with Q_REN (Kendall's τ = 0.770, P < 0.05), Q_COR (τ = -0.566, P < 0.05) and Q_MED (τ = 0.632, P < 0.05); and negatively correlated with P_PA (τ = -0.524, P < 0.05) and RRI (τ = -0.672, P < 0.05). Control animals exhibited weaker correlations.

CONCLUSION

UOm is a good physiological surrogate marker of total and regional renal blood flows and vascular resistance, particularly under septic conditions, probably reflecting glomerulo-tubular dysfunction in sepsis.

A new device to automate the monitoring of critical patients' urine output

Urine output (UO) is usually measured manually each hour in acutely ill patients. This task consumes a substantial amount of time. Furthermore, in the literature there is evidence that more frequent (minute-by-minute) UO measurement could impact clinical decision making and improve patient outcomes. However, it is not feasible to manually take minute-by-minute UO measurements. A device capable of automatically monitoring UO could save precious time of the healthcare staff and improve patient outcomes through a more precise and continuous monitoring of this parameter. This paper presents a device capable of automatically monitoring UO. It provides minute by minute measures and it can generate alarms that warn of deviations from therapeutic goals. It uses a capacitive sensor for the measurement of the UO collected within a rigid container. When the container is full, it automatically empties without requiring any internal or external power supply or any intervention by the nursing staff. In vitro tests have been conducted to verify the proper operation and accuracy in the measures of the device. These tests confirm the viability of the device to automate the monitoring of UO.

On the minute by minute variations of urine output: a study in a porcine model

BACKGROUND

Urine output (UO) is usually measured hourly in acutely ill patients. Devices capable of more continuous (minute by minute urine output, UOm) measurements have become available recently. This paper aims to (1) analyze the minute by minute variations of UO, (2) analyze the impact of sepsis on those variations and (3) test if UO measured over periods shorter than 60 min provides information not available in hourly measurements.

METHODS

Fifteen male pigs were anesthetized, tracheostomized and mechanically ventilated. Sepsis was induced by the administration of live Escherichia coli. Three groups were studied: nonseptic (n = 7) and septic (n = 4), both receiving sodium chloride (NaCl) 0.9 % at 4 ml kg(-1) h(-1); and septic (n = 4) receiving NaCl 0.9 % at 17 ml kg(-1) h(-1). UOm was measured during 6 h.

RESULTS

There was a significant variation of UOm over time, as assessed by the coefficient of variation of the root-mean-squared error (CV(RMSE)), which was significantly more pronounced under conditions of sepsis than under control conditions. A UO production pattern in sepsis was identified, characterized by low UO production compared to baseline levels for approximately 30 min, followed by high UO production for approximately 30 min after initiation of the septic challenge. This pattern was noticeable if UO was measured every 10 min but not over longer periods of time.

CONCLUSIONS

UOm provides information not conveyed by hourly measurements, especially under the cardiovascular alterations associated to sepsis. This information could enable an early identification of sepsis.

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 container.

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.