Performance Evaluation of Three URiSCAN Devices for Routine Urinalysis

Development of a Quantitative Digital Urinalysis Tool for Detection of Nitrite, Protein, Creatinine, and pH

This paper presents a cost-effective, quantitative, point-of-care solution for urinalysis screening, specifically targeting nitrite, protein, creatinine, and pH in urine samples. Detecting nitrite is crucial for the early identification of urinary tract infections (UTIs), while regularly measuring urinary protein-to-creatinine (UPC) ratios aids in managing kidney health. To address these needs, we developed a portable, transmission-based colorimeter using readily available components, controllable via a smartphone application through Bluetooth. Multiple colorimetric detection strategies for each analyte were identified and tested for sensitivity, specificity, and stability in a salt buffer, artificial urine, and human urine. The colorimeter successfully detected all analytes within their clinically relevant ranges: nitrite (6.25-200 µM), protein (2-1024 mg/dL), creatinine (2-1024 mg/dL), and pH (5.0-8.0). The introduction of quantitative protein and creatinine detection, and a calculated urinary protein-to-creatinine (UPC) ratio at the point-of-care, represents a significant advancement, allowing patients with proteinuria to monitor their condition without frequent lab visits. Furthermore, the colorimeter provides versatile data storage options, facilitating local storage on mobile devices or in the cloud. The paper further details the setup of the colorimeter's secure connection to a cloud-based environment, and the visualization of time-series analyte measurements in a web-based dashboard.

Performance evaluation of Hipee S2 point-of-care testing urine dipstick analyser: a cross-sectional study

OBJECTIVE

With advances in mobile technology, smartphone-based point-of-care testing (POCT) urinalysis hold great potential for disease screening and health management for clinicians and individual users. The purpose of this study is to evaluate the analytical performance of Hipee S2 POCT urine dipstick analyser.

DESIGN

A multicentre, hospital-based, cross-sectional study.

SETTING

Analytical performance of the POCT analyser was conducted at a clinical laboratory, and method comparison was performed at three clinical laboratories in China.

PARTICIPANTS

Urine samples were collected from 1603 outpatients and inpatients at three hospitals, and 5 health check-up population at one of the hospitals.

OUTCOME MEASURES

All tests were performed by clinical laboratory technicians. Precision, drift, carry-over, interference and method comparison of Hipee S2 were evaluated. Diagnostic accuracy of semiquantitative albumin-to-creatinine ratio (ACR) for albuminuria was carried out using quantitative ACR as the standard.

RESULTS

The precision for each parameter, assessed by control materials, was acceptable. No sample carry-over or drift was observed. Ascorbate solution with 1 g/L had an inhibitory effect for the haemoglobin test. Agreement for specific gravity (SG) varied between moderate to substantial (κ values 0.496-0.687), for pH was moderate (κ values 0.423-0.569) and for other parameters varied between substantial to excellent (κ values 0.669-0.991), on comparing the Hipee S2 with laboratory analysers. The semiquantitative microalbumin and creatinine were highly correlated with the quantitative results. The sensitivity of semiquantitative ACR to detect albuminuria was 87.2%-90.7%, specificity was 70.7%-78.4%, negative predictive value was 85.3%-87.9% and positive predictive value was 73.9%-83%.

CONCLUSIONS

Hipee S2 POCT urine analyser showed acceptable analytical performance as a semiquantitative method. It serves as a convenient alternate device for clinicians and individual users for urinalysis and health management. In addition, the POCT semiquantitative ACR would be useful in screening for albuminuria.

Smartphone-based mobile biosensors for the point-of-care testing of human metabolites

Rapid, accurate, portable and quantitative profiling of metabolic biomarkers is of great importance for disease diagnosis and prognosis. The recent development in the optical and electric biosensors based on the smartphone is promising for profiling of metabolites with advantages of rapid, reliability, accuracy, low-cost and multi-analytes analysis capability. In this review, we introduced the optical biosensing platforms including colorimetric, fluorescent and chemiluminescent sensing, and electrochemical biosensing platforms including wired and wireless communication. Challenges and future perspectives desired for reliable, accurate, cost-effective, and multi-functions smartphone-based biosensing systems were also discussed. We envision that such smartphone-based biosensing platforms will allow daily and comprehensive metabolites monitoring in the future, thus unlocking the potential to transform clinical diagnostics into non-clinical self-testing. We also believed that this progress report will encourage future research to develop advanced, integrated and multi-functional smartphone-based Point-of-Care testing (POCT) biosensors for the monitoring and diagnosis as well as personalized treatments of a spectrum of metabolic-disorder related diseases.

Internal Quality Control Data of Urine Reagent Strip Tests and Derivation of Control Rules Based on Sigma Metrics

Background

Urine reagent strip test (URST) results are semi-quantitative; therefore, the precision of URSTs is evaluated as the proportion of categorical results from repeated measurements of a sample that are concordant with an expected result. However, URSTs have quantitative readout values before ordinal results challenging statistical monitoring for internal quality control (IQC) with control rules. This study aimed to determine the sigma metric of URSTs and derive appropriate control rules for IQC.

Methods

The URiSCAN Super Plus fully automated urine analyzer (YD Diagnostics, Yongin, Korea) was used for URSTs. Change in reflectance rate (change %R) data from IQC for URSTs performed between November 2018 and May 2020 were analyzed. Red blood cells, bilirubin, urobilinogen, ketones, protein, glucose, leukocytes, and pH were measured from 2-3 levels of control materials. The total allowable error (TE_a) for a grade was the difference in midpoints of a predefined change %R range between two adjacent grades. The sigma metric was calculated as TE_a/SD. Sigma metric-based control rules were determined with Westgard EZ Rules 3 software (Westgard QC, Madison, WI, USA).

Results

Seven out of the eight analytes had a sigma metric >4 in the control materials with a negative grade (-), which were closer to the cut-offs. Corresponding control rules ranged from 1_2.5s to 1_3.5s.

Conclusions

Although the URST is a semi-quantitative test, statistical IQC can be performed using the readout values. According to the sigma metric, control rules recommended for URST IQC in routine clinical practice are 1_2.5s to 1_3.5s.

Analytical verification of 12 most commonly used urine dipsticks in Croatia: comparability, repeatability and accuracy

Introduction

Variability among manufacturers of urine dipsticks, respective to their accuracy and measurement range, may lead to diagnostic errors and thus create a serious risk for the patient. Our aims were to determine the level of agreement between 12 most commonly used urine dipsticks in Croatia, examine their accuracy for glucose and total protein and to test their repeatability.

Materials and methods

A total of 75 urine samples were used to examine comparability and accuracy of 12 dipstick brands (Combur 10 TestM, ChoiceLine 10, Combur 10 TestUX, ComboStik 10M, ComboStik 11M, CombiScreen 11SYS, CombiScreen 10SL, Combina 13, Combina 11S, Combina 10M, UriGnost 11, Multistix 10SG). Agreement between each dipstick and the reference (Combur 10 TestM) was expressed as kappa coefficient (acceptable κ ≥ 0.80). Accuracy for glucose and total protein was tested by comparison with quantitative measurements on analysers: AU400 (Beckman Coulter, USA), Cobas 6000 c501 (Roche Diagnostics, Germany) and Architect plus c4000 (Abbott, USA). Repeatability was assessed on 20 replicates (acceptable > 90%).

Results

Best agreement was achieved for glucose, total protein and nitrite (11/11, k > 0.80) and the lowest for bilirubin (5/5, k < 0.60). Sensitivities for total protein were 41-75% (AU400) and 56-92% (Cobas and Architect); while specificities were 41-75% (AU400, Cobas, Architect). Dipsticks' sensitivity and specificity for glucose were 68-98%. Most of the dipsticks showed unacceptable repeatability (6/12, < 90%) for one parameter, most prominently for pH (3/12, < 90%).

Conclusions

Most commonly used dipsticks in Croatia showed low level of agreement between each other. Moreover, their repeatability varies among manufacturers and their accuracy for glucose and proteins is poor.