Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays

Kavuru, Varun; Vu, Tommy; Karageorge, Lampros; Choudhury, Devasmita; Senger, Ryan S.; Robertson, John L.

doi:10.1080/00325481.2019.1679540

Cited by 29 publications

(20 citation statements)

References 56 publications

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“…Urine dipsticks represent a time-saving POC screening test in resource-limited settings that lack laboratory analysis [ 40 ]. However, to adequately decide whether to use urine dipsticks, or in cases which use it, stakeholders should consider its sensitivity and specificity, along with the expected number of false positives and false negatives for their study population.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic accuracy of urine dipstick testing for albumin-to-creatinine ratio and albuminuria: A systematic review and meta-analysis

Mejia

Fernández-Chinguel

Dolores-Maldonado

et al. 2021

Heliyon

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Background: The accuracy of urine dipsticks to detect increased albuminuria is uncertain. We aimed to assess the diagnostic accuracy of urine dipsticks for detecting albuminuria. Methods: A systematic review of studies that assessed the diagnostic accuracy of urine dipstick testing for detecting albuminuria has been conducted (using as reference standard the albuminuria in a 24-hour sample or the albumin-to-creatinine ratio) in Scopus, PubMed, and Google Scholar. The risk of bias of the included studies has been assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Whenever possible, we performed meta-analyses for sensitivity and specificity. The certainty of the evidence has also been assessed using the Grading of Recommendations Assessment, Development, and Evaluation methodology. Results: A total of 14 studies have been included in this review, having assessed all albumin-to-creatinine ratio (ACR) as assessed standard. Each study used different dipstick types. The resulting pooled sensitivity and specificity for each cutoff point were as follows: for ACR >30 mg/g (13 studies): 0.82 (95% confidence interval: 0.76-0.87) and 0.88 (0.83-0.91); for ACR 30-300 mg/g (7 studies): 0.72 (0.68-0.77) and 0.82 (0.76-0.89); and for ACR >300 mg/g (7 studies): 0.84 (0.71-0.90) and 0.97 (0.95-0.99), respectively. An overall high risk of bias, an important heterogeneity in all pooled analysis, and a very low certainty of the evidence have been found. Conclusions: Pooled sensitivity and specificity of urine dipsticks have been calculated for different ACR cutoff points. However, the dipstick types differed across studies, and the certainty of the evidence was very low. Thus, further well-designed studies are needed to reach more confident estimates and to assess accuracy differences across dipstick types. Registration: PROSPERO (CRD42019124637).

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Section: Discussionmentioning

confidence: 99%

“…For detecting ACR >30 mg/g, dipsticks showed a limited sensitivity (0.82, 95% CI: 0.76–0.87) and specificity (0.88, 95% CI: 0.83–0.91). A suboptimal accuracy could be expected, due to the effect of some interfering compounds (drugs, vitamins, urine preservatives, and detergents), urine pH, and storage deficiencies [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of urine dipstick testing for albumin-to-creatinine ratio and albuminuria: A systematic review and meta-analysis

Mejia

Fernández-Chinguel

Dolores-Maldonado

et al. 2021

Heliyon

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“…Detection or suspicion of hematuria is invariably confirmed with other medical tests, including urine sediment examination, biochemical (dry chemistry, “dipstick”) assays, and multimodality imaging. 1–4…”

Section: Introductionmentioning

confidence: 99%

“…Detection or suspicion of hematuria is invariably confirmed with other medical tests, including urine sediment examination, biochemical (dry chemistry, "dipstick") assays, and multimodality imaging. [1][2][3][4] Hematuria can be caused by a large variety of diseases (ranging, e.g., from bladder infections to cancer), injuries, or even exposure to toxicants (such as the chemotherapeutic agent cyclophosphamide). 2,5,6 Testing for macrohematuria and early intervention has been shown to be cost effective, 7 but early, aggressive testing for microhematuria has not been shown to produce an increased health benefit (i.e., early detection of disease) compared to the cost of screening at-risk populations.…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine

2022

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Hematuria refers to the presence of blood in urine. Even in small amounts, it may be indicative of disease, ranging from urinary tract infection to cancer. Here, Raman spectroscopy was used to detect and quantify macro- and microhematuria in human urine samples. Anticoagulated whole blood was mixed with freshly collected urine to achieve concentrations of 0, 0.25, 0.5, 1, 2, 6, 10, and 20% blood/urine (v/v). Raman spectra were obtained at 785 nm and data analyzed using chemometric methods and statistical tests with the Rametrix toolboxes for Matlab. Goldindec and iterative smoothing splines with root error adjustment (ISREA) baselining algorithms were used in processing and normalization of Raman spectra. Rametrix was used to apply principal component analysis (PCA), develop discriminate analysis of principal component (DAPC) models, and to validate these models using external leave-one-out cross-validation (LOOCV). Discriminate analysis of principal component models were capable of detecting various levels of microhematuria in unknown urine samples, with prediction accuracies of 91% (using Goldindec spectral baselining) and 94% (using ISREA baselining). Partial least squares regression (PLSR) was then used to estimate/quantify the amount of blood (v/v) in a urine sample, based on its Raman spectrum. Comparing actual and predicted (from Raman spectral computations) hematuria levels, a coefficient of determination (R2) of 0.91 was obtained over all hematuria levels (0–20% v/v), and an R2 of 0.92 was obtained for microhematuria (0–1% v/v) specifically. Overall, the results of this preliminary study suggest that Raman spectroscopy and chemometric analyses can be used to detect and quantify macro- and microhematuria in unprocessed, clinically relevant urine specimens.

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“…Although urinalysis is simple, it requires a significant amount of attention due to the problems, such as infection, that may occur during the collection and testing process [ 6 ]. In particular, because the test results are obtained through chemical and enzymatic reactions, errors in results may occur owing to reaction times and environment changes [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

Kim

Cho

2022

Sensors

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Urinalysis, an elementary chemical reaction-based method for analyzing color conversion factors, facilitates examination of pathological conditions in the human body. Recently, considerable urinalysis-centered research has been conducted on the analysis of urine dipstick colors using smartphone cameras; however, such methods have a drawback: the problem of reproducibility of accuracy through quantitative analysis. In this study, to solve this problem, the function values for each concentration of a range of analysis factors were implemented in an algorithm through urine dipstick RGB semi-quantitative color analysis to enable real-time results. Herein, pH, glucose, ketones, hemoglobin, bilirubin, protein (albumin), and nitrites were selected as analysis factors, and the accuracy levels of the existing equipment and the test application were compared and evaluated using artificial urine. In the semi-quantitative analysis, the red (R), green (G), and blue (B) characteristic values were analyzed by extracting the RGB characteristic values of the analysis factors for each concentration of artificial urine and obtaining linear function values. In addition, to improve the reproducibility of detection accuracy, the measurement value of the existing test equipment was set to an absolute value; using a machine-learning technique, the confusion matrix, we attempted to stabilize test results that vary with environment.

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Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays

Cited by 29 publications

References 56 publications

Diagnostic accuracy of urine dipstick testing for albumin-to-creatinine ratio and albuminuria: A systematic review and meta-analysis

Diagnostic accuracy of urine dipstick testing for albumin-to-creatinine ratio and albuminuria: A systematic review and meta-analysis

Raman Spectroscopic Detection and Quantification of Macro- and Microhematuria in Human Urine

Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis

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