Evaluation of Ames' "Clini-Tek".

Peele, James; Gadsden, Richard H.; Crews, R

doi:10.1093/clinchem/23.12.2238

Cited by 13 publications

(2 citation statements)

References 3 publications

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“…When this automated analyser was first evaluated (in nonpregnant women) it gave only a 2% to 3% incidence of false positive results but this was testing contrived urine samples containing known amounts of albumin 10 . The automated analyser also compared favourably with visual readings when it was used for determination of proteinuria from nephrotic patients 11 .…”

Section: Discussionmentioning

confidence: 99%

Improved methods of assessing proteinuria in hypertensive pregnancy

Saudan

Brown

Farrell

et al. 1997

BJOG

135

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Objective To determine whether use of an automated urinalysis device will improve the accuracy of detecting proteinuria, and whether spot urine protein to creatinine ratio will provide accurate quantitation of proteinuria in hypertensive pregnant women.Design Prospective studies assessing the accuracy of both detection and quantitation of proteinuria.Setting Antenatal ward and pregnancy day assessment unit of St George Hospital, a teaching hospital in Sydney, Australia.Population Hypertensive pregnant women admitted to hospital or day assessment unit for management of their hypertensive disorders.Methods I. Routine dipstick urinalysis and 2. Urinalysis by an automated device (Clinitek 100 Ames) on a midstream urine sample were compared with measurement of protein concentration on that sample (n = 103). In a third study, the pr0tein:creatinine ratio on a midstream (spot) urine sample was compared with protein excretion over the subsequent 24 hours (n = 100). Main outcome measuresRelations between urine protein concentrations and 1. dipstick urinalysis and 2. automated urinalysis; 3. Positive and negative predictive values of spot protein : creatinine ratio for true proteinuria (2 300 mglday).Results Automated urinalysis improved the percentage of true positive urinalyses from 48% with visual urinalysis to 74% (P = 0.02). True negatives were 98% to 100% for both methods. Spot urine protein: creatinine ratio correlated well with subsequent 24-hour urine proteinuria (r = 0.93, P < 0.001). A pr0tein:creatinine ratio > 30 mg proteidmmol creatinine was the optimum discriminant value for true proteinuria, with sensitivity 93%, specificity 92%, positive predictive value 95% and negative predictive value 90%.Use of an automated urinalysis device improved accurate detection of proteinuria, particularly reducing false positive tests. A random urine protein: creatinine ratio provides an accurate and rapid quantitation of proteinuria in hypertensive pregnant women. This should improve clinical care, especially when managing hypertensive pregnant women as outpatients. Conclusions

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Section: Discussionmentioning

confidence: 99%

Improved methods of assessing proteinuria in hypertensive pregnancy

Saudan

Brown

Farrell

et al. 1997

BJOG

135

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“…As urinary indices are sensitive to small changes in hydration status, urine colour is feasible for the detection of early hydration problems. The visual-manual interpretation has limitations surrounding variations in perception of colour, lighting conditions, and timing at which reagents are read [ 15 ]. On the other hand, modern cameras and computer displays allow for rapid photo processing in colour balancing and compensation.…”

Section: Introductionmentioning

confidence: 99%

Assessing dehydration status in dengue patients using urine colourimetry and mobile phone technology

et al. 2020

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Background Dengue is a systemic and dynamic disease with symptoms ranging from undifferentiated fever to dengue shock syndrome. Assessment of patients' severity of dehydration is integral to appropriate care and management. Urine colour has been shown to have a high correlation with overall assessment of hydration status. This study tests the feasibility of measuring dehydration severity in dengue fever patients by comparing urine colour captured by mobile phone cameras to established laboratory parameters. Methodology/Principal findings Photos of urine samples were taken in a customized photo booth, then processed using Adobe Photoshop to index urine colour into the red, green, and blue (RGB) colour space and assigned a unique RGB value. The RGB values were then correlated with patients' clinical and laboratory hydration indices using Pearson's correlation and multiple linear regression. There were strong correlations between urine osmolality and the RGB of urine colour, with r =-0.701 (red), r =-0.741 (green), and r =-0.761 (blue) (all p-value <0.05). There were strong correlations between urine specific gravity and the RGB of urine colour, with r =-0.759 (red), r =-0.785 (green), and r =-0.820 (blue) (all p-value <0.05). The blue component had the highest correlations with urine specific gravity and urine osmolality. There were moderate correlations between RGB components and serum urea, at r =-0.338 (red),-0.329 (green),-0.360 (blue). In terms of urine biochemical parameters linked to dehydration, multiple linear regression studies showed that the green colourimetry code was predictive of urine osmolality (β coefficient-0.082, p-value <0.001) while the blue colourimetry code was predictive of urine specific gravity (β coefficient-2,946.255, p-value 0.007). Conclusions/Significance Urine colourimetry using mobile phones was highly correlated with the hydration status of dengue patients, making it a potentially useful hydration status tool.

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Intelligent Method for Dipstick Urinalysis Using Smartphone Camera

Ginardi

Saikhu

Sarno

et al. 2014

Information and Communication Technology

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Abstract. This paper introduces an intelligent method for helping people to maintain their healthy by doing a self urinalysis utilising a smartphone camera. A color sensing method using a smartphone camera is designed to determine the value of a reagent strip in a urinalysis dipstick. In the dipstick urinalysis, a color change in each reagent strip is examined. This color change is a result of the reaction of dipstick to the chemical contents of urine including pH, Protein, Glucose, Ketones, Leucocyte, Nitrite, Bilirubine, and Urobilinogen. Performing disptick urinalysis can be done in almost any places even on the very remote area where medical laboratory cannot be found, and it is much easier and cheaper than medical lab visit.The proposed intelligent method includes a framework for color acquisition using a smartphone camera which covers the color management system, color correction, color matching, and quantification. The usage of RGB and CIELAB color space is discussed in the color management part. An automated color constancy approach is introduced to provide a better color correction, and a step-wise linear interpolation is introduced to better estimate the urinalysis value.To implement this proposed method, a closed acquisition box does not required. Disptick capturing can be done directly with a smartphone camera in almost any normal lighting condition.

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Evaluation of Ames' "Clini-Tek".

Cited by 13 publications

References 3 publications

Improved methods of assessing proteinuria in hypertensive pregnancy

Improved methods of assessing proteinuria in hypertensive pregnancy

Assessing dehydration status in dengue patients using urine colourimetry and mobile phone technology

Intelligent Method for Dipstick Urinalysis Using Smartphone Camera

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