Revisiting approaches to and considerations for urinalysis and urine culture reflexive testing

Alternative Strategies to Provide Actionable Results When a Supply of Urinalysis Strips Is Unavailable

CONTEXT.—

Urinalysis instrument-specific dip strips offer physicians qualitative results for actionable analytes (protein, glucose, leukocyte esterase, nitrates, hemoglobin, and ketones).

OBJECTIVE.—

To explain a strategy implemented to support clinical decision-making by providing urine quantification of protein, glucose, white blood cells (WBCs), and red blood cells because of urine strip shortages.

DESIGN.—

During shortages, we implemented an automated algorithm that triggered sending urine samples to the automation line for quantification of protein and glucose and ensured that urine microscopy was performed to obtain WBC and red blood cell counts. The algorithm printed 2 labels so nursing staff would collect 2 specimens. We monitored the turnaround time from the specimen being received in the laboratory to result verification, ensured that the culture reflex order was triggered, and tracked complaints by physicians regarding not having usual urinalysis results. Prior to implementation, correlation between sample types for protein and glucose measurement was found acceptable.

RESULTS.—

The algorithm was put in place twice during 2022. The turnaround time of urine microscopic study was identical to that obtained when the urinalysis was done with the strips; however, the quantification of glucose and protein took approximately 30 minutes more. Urine reflex cultures were triggered correctly with the algorithm, as they were derived entirely from a WBC count higher than 10 per high-power field. During the shortage period we had only 1 complaint, by a physician wanting to have results of nitrates.

CONCLUSIONS.—

During urine strip shortages, we successfully implemented a diversion algorithm that provided actionable urinalysis analytes in a timely manner with minimal provider complaints.

Implementing diagnostic stewardship to improve diagnosis of urinary tract infections across three medical centers: A qualitative assessment

BACKGROUND

Urine-culture diagnostic stewardship aims to decrease misdiagnosis of urinary tract infections (UTIs); however, these interventions are not widely adopted. We examined UTI diagnosis and management practices to identify barriers to and facilitators of diagnostic stewardship implementation.

METHODS

Using a qualitative descriptive design, we conducted semistructured interviews at 3 Veterans' Affairs medical centers. Interviews were conducted between November 2021 and May 2022 via Zoom videoconferencing using an interview guide and visual prototypes of proposed interventions. Interviewees were asked about current practices and thoughts on proposed interventions for urine-culture ordering, processing, and reporting. We used a rapid analysis matrix approach to summarize key interview findings and compare practices and perceptions across sites.

RESULTS

We interviewed 31 stakeholders and end users. All sites had an antimicrobial stewardship program but limited initiatives targeting appropriate diagnosis and management of UTIs. The majority of those interviewed identified the importance of diagnostic stewardship. Perceptions of specific interventions ranged widely by site. For urine-culture ordering, all 3 sites agreed that documentation of symptomology would improve culturing practices but did not want it to interrupt workflow. Representatives at 2 sites expressed interest in conditional urine-culture processing and 1 was opposed. All sites had similar mechanisms to report culture results but varied in perceptions of the proposed interventions. Feedback from end users was used to develop a general diagnostic stewardship implementation checklist.

CONCLUSION

Interviewees thought diagnostic stewardship was important. Qualitative assessment involving key stakeholders in the UTI diagnostic process improved understanding of site-specific beliefs and practices to better implement interventions for urine-culture ordering, processing, and reporting.

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

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.