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

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.

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.

A Pilot Evaluation of a Capacitance-Based Automatic Urinometer in a Pediatric Intensive Care Setting

OBJECTIVES

To compare a modified capacitance-based automatic urinometer to a manual urinometer, with regard to precision of measurement and to evaluate the staff's opinion regarding the automatic urinometer.

DESIGN

Prospective observational cohort study.

SETTING

PICU at Astrid Lindgren's Children Hospital in Solna, Sweden.

PATIENTS

Twelve children weighing up to 10 kg with an indwelling urinary catheter in place before enrollment.

INTERVENTIONS

Measurement of hourly diuresis using either an automatic urinometer or manual urinometer.

MEASUREMENTS AND MAIN RESULTS

Hourly diuresis was measured with an automatic urinometer (n = 127; Sippi; Observe Medical Nordic AB, Gothenburg, Sweden) or an manual urinometer (n = 83; Unometer Safeti Plus; Convatec, Lejre, Denmark) and thereafter validated with a measuring cylinder. The absolute mean bias was -1.1 mL for the automatic urinometer (CI, -0.6 to -1.5) and -0.6 mL (CI, ± 0.0 to -1.2) for the manual urinometer (p = 0.21). The SDs were 2.6 and 2.8 mL, respectively. User evaluation comparing the automatic urinometer with the manual urinometer concerning the ease of use was made with a questionnaire (n = 18). The majority of staff preferred the automatic urinometer to the manual urinometer in terms of ease of use, learning, and handling.

CONCLUSIONS

The two urinometers were comparable in performance for children weighing up to 10 kg. Taking into account the overwhelming staff satisfaction with the automatic urinometer and benefits in less well-staffed wards as well as lack of temporal deviation, the modified automatic urinometer may be considered for clinical use in the PICU.

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

BACKGROUND

Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions.

METHODS

This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed.

RESULTS

As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration.

CONCLUSION

It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.

In-human subject-specific evaluation of a control-theoretic plasma volume regulation model

The goal of this study was to conduct a subject-specific evaluation of a control-theoretic plasma volume regulation model in humans. We employed a set of clinical data collected from nine human subjects receiving fluid bolus with and without co-administration of an inotrope agent, including fluid infusion rate, plasma volume, and urine output. Once fitted to the data associated with each subject, the model accurately reproduced the fractional plasma volume change responses in all subjects: the error between actual versus model-reproduced fractional plasma volume change responses was only 1.4 ± 1.6% and 1.2 ± 0.3% of the average fractional plasma volume change responses in the absence and presence of inotrope co-administration. In addition, the model parameters determined by the subject-specific fitting assumed physiologically plausible values: (i) initial plasma volume was estimated to be 36 ± 11 mL/kg and 37 ± 10 mL/kg in the absence and presence of inotrope infusion, respectively, which was comparable to its actual counterpart of 37 ± 4 mL/kg and 43 ± 6 mL/kg; (ii) volume distribution ratio, specifying the ratio with which the inputted fluid is distributed in the intra- and extra-vascular spaces, was estimated to be 3.5 ± 2.4 and 1.9 ± 0.5 in the absence and presence of inotrope infusion, respectively, which accorded with the experimental observation that inotrope could enhance plasma volume expansion in response to fluid infusion. We concluded that the model was equipped with the ability to reproduce plasma volume response to fluid infusion in humans with physiologically plausible model parameters, and its validity may persist even under co-administration of inotropic agents.

Prospective validation of a near real-time EHR-integrated automated SOFA score calculator

OBJECTIVES

We created an algorithm for automated Sequential Organ Failure Assessment (SOFA) score calculation within the Electronic Health Record (EHR) to facilitate detection of sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS-3) clinical definition. We evaluated the accuracy of near real-time and daily automated SOFA score calculation compared with manual score calculation.

METHODS

Automated SOFA scoring computer programs were developed using available EHR data sources and integrated into a critical care focused patient care dashboard at Mayo Clinic in Rochester, Minnesota. We prospectively compared the accuracy of automated versus manual calculation for a sample of patients admitted to the medical intensive care unit at Mayo Clinic Hospitals in Rochester, Minnesota and Jacksonville, Florida. Agreement was calculated with Cohen's kappa statistic. Reason for discrepancy was tabulated during manual review.

RESULTS

Random spot check comparisons were performed 134 times on 27 unique patients, and daily SOFA score comparisons were performed for 215 patients over a total of 1206 patient days. Agreement between automatically scored and manually scored SOFA components for both random spot checks (696 pairs, κ=0.89) and daily calculation (5972 pairs, κ=0.89) was high. The most common discrepancies were in the respiratory component (inaccurate fraction of inspired oxygen retrieval; 200/1206) and creatinine (normal creatinine in patients with no urine output on dialysis; 128/1094). 147 patients were at risk of developing sepsis after intensive care unit admission, 10 later developed sepsis confirmed by chart review. All were identified before onset of sepsis with the ΔSOFA≥2 point criterion and 46 patients were false-positives.

CONCLUSIONS

Near real-time automated SOFA scoring was found to have strong agreement with manual score calculation and may be useful for the detection of sepsis utilizing the new SEPSIS-3 definition.

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.

Parameters used to discontinue dialysis in acute kidney injury recovery: a survey of United States nephrologists

BACKGROUND

Despite advances in the approach to cure acute kidney injury (AKI), including definition, classification and treatment methods, there are no standard criteria to withdraw dialysis in the setting of improving AKI. We conducted this survey to elucidate parameters that United States (US) nephrologists used to determine when to stop dialysis with improving renal function in AKI. We hypothesized that there would be a difference in approach to weaning a patient off dialysis based on years in practice or the number of cases of AKI treated per year.

METHODS

This was an anonymous electronic survey of practicing nephrologists who treated AKI. Data was de-identified and analyzed using descriptive statistics.

RESULTS

The commonest criteria used to stop dialysis when renal function improved was, in decreasing order of importance, resolution in oliguria (51%), resolution of volume overload (29%), improvement in serum creatinine (26.7%) and resolution of hyperkalemia (21%). The most common reasons for re-starting dialysis within 28 days did not show a specific trend but respondents (20%) reported re-starting if estimated glomerular filtration rates (eGFR) declined. There was no significant pattern in approach to withdrawing dialysis or resuming dialysis based on the number of years in nephrology practice. However, responses of nephrologists who saw more than 20 AKI patients/year were significantly different in stopping dialysis with clinical stabilization of blood pressure (p < 0.001), improvement in respiratory parameters (p = 0.005), improvement in pre-dialysis blood urea nitrogen (BUN) levels despite the same dose of dialysis (p = 0.05) and resolution of oliguria (p = 0.025) compared to those who saw fewer cases.

CONCLUSION

Resolution of oliguria was the commonest factor used to help deciding to stop dialysis in improving AKI. However, considerable variation was noted among US nephrologists who participated in this survey, regarding what criteria they used to withdraw dialysis in the setting of improving AKI. These results call for more studies in withdrawing dialysis in the setting of AKI that could lead to guideline formulation.