Accuracy of the IDEXX SediVue Dx analyzer for quantifying RBC and WBC indices in the urine sediments of cats and dogs compared with manual microscopic evaluations.

BACKGROUND: The IDEXX SediVue Dx (SediVue) is an automated, in-clinic urine sediment analyzer for veterinary patients. The bias between the results from manual microscopy and the SediVue is currently unknown. OBJECTIVES: To assess the diagnostic accuracy of the SediVue, we aimed to determine the bias between the SediVue (index test) and manual microscopy (reference standard) for the quantification of RBCs and WBCs in urine. METHODS: Urine remnant samples were collected from cats and dogs that contained RBCs (n = 462) and WBCs (n = 510). Retrospective analysis of results from urine sediment examinations using both manual microscopy (using a KOVA and DeciSlide system) and the SediVue (1.0.1.3) was performed. Bias was determined with Bland-Altman plots. SediVue-captured images from high-bias samples were reviewed, and biases were compared. RESULTS: The median bias for semi-quantitative RBC and WBC counts was determined for RBC and WBC counts. The cutoffs were RBC ≤ 5/HPF, 0.3; RBC 5.1-10/HPF, 10.1; RBC 10.1-20/HPF, 10.6; and RBC > 20/HPF, 28.93; WBC ≤ 5/HPF, 0.1; WBC 5.1-10/HPF, 2.2; WBC 10.1-20/HPF, 9.4; and WBC > 20/HPF, 26.6. High bias between the methods was identified in 98 samples (21.0%) with RBCs and 77 samples (15.7%) with WBCs. Reviewer-based enumeration of the SediVue-captured images decreased the percentage of samples with high bias to 17.3% for RBCs and to 11.4% for WBCs. CONCLUSIONS: Bias in the RBC and WBC counts between manual microscopy and the SediVue was unlikely to impact clinical interpretations in a majority of cases. Although reviewer enumeration of SediVue-captured images reduced observed bias, inherent differences between methodologies appeared to have a larger impact on the bias.

Comparison of the performance of the IDEXX SediVue Dx® with manual microscopy for the detection of cells and 2 crystal types in canine and feline urine.

BACKGROUND: Microscopic evaluation of urine is inconsistently performed in veterinary clinics. The IDEXX SediVue Dx® Urine Sediment Analyzer (SediVue) recently was introduced for automated analysis of canine and feline urine and may facilitate performance of urinalyses in practice. OBJECTIVE: Compare the performance of the SediVue with manual microscopy for detecting clinically relevant numbers of cells and 2 crystal types. SAMPLES: Five-hundred thirty urine samples (82% canine, 18% feline). METHODS: For SediVue analysis (software versions [SW] 1.0.0.0 and 1.0.1.3), uncentrifuged urine was pipetted into a cartridge. Images were captured and processed using a convolutional neural network algorithm. For manual microscopy, urine was centrifuged to obtain sediment. To determine sensitivity and specificity of the SediVue compared with manual microscopy, thresholds were set at ≥5/high power field (hpf) for red blood cells (RBC) and white blood cells (WBC) and ≥1/hpf for squamous epithelial cells (sqEPI), non-squamous epithelial cells (nsEPI), struvite crystals (STR), and calcium oxalate dihydrate crystals (CaOx Di). RESULTS: The sensitivity of the SediVue (SW1.0.1.3) was 85%-90% for the detection of RBC, WBC, and STR; 75% for CaOx Di; 71% for nsEPI; and 33% for sqEPI. Specificity was 99% for sqEPI and CaOx Di; 87%-90% for RBC, WBC, and nsEPI; and 84% for STR. Compared to SW1.0.0.0, SW1.0.1.3 had increased sensitivity but decreased specificity. Performance was similar for canine versus feline and fresh versus stored urine samples. CONCLUSIONS AND CLINICAL IMPORTANCE: The SediVue exhibits good agreement with manual microscopy for the detection of most formed elements evaluated, but improvement is needed for epithelial cells.

Patient-based feedback control for erythroid variables obtained using in-house automated hematology analyzers in veterinary medicine.

BACKGROUND: Automated in-house diagnostic analyzers, most commonly used for hematologic and biochemical analysis, are typically calibrated, and then control materials are used to confirm the quality of results. Although this approach provides indirect knowledge that the system is performing correctly, it does not provide direct knowledge of system performance between control runs. OBJECTIVES: The objectives of this study were to apply analysis of weighted moving averages to assess performance of hematology analyzers using animal patient samples from dogs, cats, and horses as they were analyzed and apply correction factors to mitigate instrument-driven biases when they developed. METHODS: A set of algorithms was developed and applied to sequential batches of 20 samples. Repeated samples within a batch and large populations of samples with similar abnormalities were excluded. Data for 6 hematologic variables were grouped into batches of weighted moving averages; data were analyzed with control chart rules, a gradient descent algorithm, and fuzzy logic to define and apply adjustments. RESULTS: A total of 102 hematology analyzers that had developed biases in RBC count, HCT, hemoglobin (HGB) concentration, MCV, MCH, and MCHC were evaluated. Following analysis, all variables except HGB concentration required adjustment, with RBC counts requiring only slight change and MCV requiring the greatest change. Adjustments were validated by comparing PCVs with the original and adjusted HCT values. CONCLUSIONS: The proposed system provides feedback control to minimize system bias for RBC count, HCT, HGB concentration, MCV, MCH, and MCHC. Fundamental assumptions must be met for the approach to assure proper functionality.