Comparison of the clinical performance of the Atyp.C parameter of the UF-5000 fully automated urine particle analyzer with that of microscopic urine sediment analysis

a Objectives

Urinalysis is one of the most common laboratory screening tests to detect problems in the renal and urinary system; however, they cannot detect atypical cells (Atyp.Cs). The Sysmex UF-5000, a fully automated urine particle analyzer, can detect Atyp.Cs via its Atyp.C parameter. This study aimed to compare the clinical value of the Atyp.C parameter with that of urine sediment microscopy.

b Method

A total of 471 leftover urine samples were submitted to the Department of Clinical Laboratory at the University of Tokyo Hospital for urinalysis by manual sediment microscopy examination and UF-5000 Atyp.C analysis.

c Result

Of 471 submitted samples, 117 were positive for Atyp.Cs by urine sediment and 354 samples were negative. The histological subtypes of the Atyp.Cs included 105 cases of suspected urothelial carcinoma cells, 10 suspected squamous carcinoma cells, and 2 of suspected adenocarcinoma cells. The Atyp.C values for the Atyp.C-positive and -negative groups were 2.64 ± 0.69 and 0.38 ± 0.16, respectively. The optimal Atyp.C cutoff value determined by the receiver operating characteristic curve analysis was 0.4/μL. The area under the curve was 0.856, with a sensitivity of 79.5% and specificity of 85.1%. Atyp.C values of the UF-5000 showed high predictive performance for Atyp.C-positive specimens identified by urine sediment microscopy.

d Conclusions

This study shows that a combination of UF-5000 analysis and microscopic examination of urine sediment improves Atyp.C detection in urine sediment analysis. These results suggest that Atyp.C measured by UF-5000 could be a useful screening parameter in routine testing of urine samples.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

BACKGROUND

Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine.

CONTENT

In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine.

SUMMARY

AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

Evaluation of the Atellica® UAS 800: a new member of the automated urine sediment analyzer family

BACKGROUND

In 2017 the Atellica^® UAS 800 urine sediment analyzer was introduced by Siemens Healthineers. We investigated its applicability in the standardization and automation of the laboratory urinalysis workflow, including the prediction of urine culture outcome and glomerular pathology.

METHODS

We evaluated the performance characteristics of the Atellica^® UAS 800 and its correlation with the iQ200 (Beckman Coulter). In addition, we studied the agreement between Atellica^® UAS 800 and CLINITEK Novus^® and determined the predictive value of bacteria and leukocyte counts for urine culture outcome. Furthermore, we investigated the ability of Atellica^® UAS 800 to identify pathological casts and dysmorphic erythrocytes in comparison to manual microscopy.

RESULTS

Erythrocyte and leukocyte analyses indicated high intra- and inter-run precisions and good correlations with the iQ200. We found that the Atellica^® UAS 800 detects bacteria with higher sensitivity than the iQ200. The Atellica^® UAS 800 and CLINITEK Novus^® showed a high degree of conformity. We determined seven combinations of clinical cut-off values of bacteria and leukocytes for predicting urine culture outcome with sensitivity, specificity, and negative predictive values of 95%, 52%, and 93%, respectively. Using the Atellica^® UAS 800, hyaline casts, erythrocyte casts, leukocyte casts, and dysmorphic erythrocytes were correctly recognized in 76%, 22%, 2%, and 39% of the samples, respectively.

CONCLUSIONS

The Atellica^® UAS 800 is a robust, fast, and user-friendly analyzer, which accurately quantifies erythrocytes, leukocytes, bacteria and squamous epithelial cells, and may be utilized for predicting positive urine cultures. The detection of clinically important pathological casts and dysmorphic erythrocytes proved insufficient.

Renal tubular epithelial cells add value in the diagnosis of upper urinary tract pathology

Background Diagnosis of upper urinary tract infections (UTI) is challenging. We evaluated the analytical and diagnostic performance characteristics of renal tubular epithelial cells (RTECs) and transitional epithelial cells (TECs) on the Sysmex UF-5000 urine sediment analyzer. Methods Urinary samples from 506 patients presenting with symptoms of a UTI were collected. Only samples for which a urinary culture was available were included. Analytical (imprecision, accuracy, stability and correlation with manual microscopy) and diagnostic performance (sensitivity and specificity) were evaluated. Results The Sysmex UF-5000 demonstrated a good analytical performance. Depending on the storage time, storage conditions (2-8 °C or 20-25 °C) and urinary pH, RTECs and TECs were stable in urine for at least 4 h. Using Passing-Bablok and Bland-Altman analysis, an acceptable agreement was observed between the manual and automated methods. Compared to TECs, RTECs demonstrated an acceptable diagnostic performance for the diagnosis of upper UTI. Conclusions While TECs do not seem to serve as a helpful marker, increased urinary levels of RTECs add value in the diagnosis of upper UTI and may be helpful in the discrimination between upper and lower UTIs.

Evaluation of red blood cell parameters provided by the UF-5000 urine auto-analyzer in patients with glomerulonephritis

OBJECTIVES

The microscopic examination of hematuria, a cardinal symptom of glomerulonephritis (GN), is time-consuming and labor-intensive. As an alternative, the fully automated urine particle analyzer UF-5000 can interpret the morphological information of the glomerular red blood cells (RBCs) using parameters such as UF-5000 small RBCs (UF-%sRBCs) and Lysed-RBCs.

METHODS

Hematuria samples from 203 patients were analyzed using the UF-5000 and blood and urine chemistries to determine the cut-off values of RBC parameters for GN and non-glomerulonephritis (NGN) classification and confirm their sensitivity to the IgA nephropathy and non-IgA nephropathy groups.

RESULTS

The UF-%sRBCs and Lysed-RBCs values differed significantly between the GN and NGN groups. The cut-off value of UF-%sRBCs was >56.8% (area under the curve, 0.649; sensitivity, 94.1%; specificity, 38.1%; positive predictive value, 68.3%; and negative predictive value, 82.1%), while that for Lysed-RBC was >4.6/μL (area under the curve, 0.708; sensitivity, 82.4%; specificity, 56.0%; positive predictive value, 72.6%; and negative predictive value, 69.1%). Moreover, there was no significant difference in the sensitivity between the IgA nephropathy and non-IgA nephropathy groups (87.1 and 89.8% for UF-%sRBCs and 83.9 and 78.4% for Lysed-RBCs, respectively). In the NGN group, the cut-off values showed low sensitivity (56.0% for UF-%sRBCs and 44.0% for Lysed-RBCs).

CONCLUSIONS

The RBC parameters of the UF-5000, specifically UF-%sRBCs and Lysed-RBCs, showed good cut-off values for the diagnosis of GN.

Comparison of the reliability of Gram-negative and Gram-positive flags of the Sysmex UF-5000 with manual Gram stain and urine culture results

OBJECTIVES

Recently, the fully automated flow cytometry-based UF-5000 (Sysmex Corboration, Kobe, Japan) urine sediment analyzer was developed providing bacteria (BACT) info flags for more accurate bacterial discrimination of urinary tract infections (UTIs). This study aimed to compare the reliability of the UF-5000 BACT-info flags with manual Gram stain and urine culture as the gold standard method.

METHODS

A total of 344 urine samples were analyzed on the UF-5000 and compared with manual microscopic Gram stain and urine cultures. Agreement was assessed by Cohen's kappa (κ) analysis. The Youden index was used to determine the optimal BACT and white blood cell (WBC) cut-off points for discriminating positive and negative urine cultures.

RESULTS

Overall 98/344 (28.5%) samples were urine culture positive at a cut-off of ≥105 CFU/mL. "Gram-negative?" UF-5000 BACT-Info flags showed a better concordance of 25/40 (62.5%) with urine culture compared to Gram stain with 30/50 (60%). The results for UF-5000 discrimination of Gram-positive and Gram-negative microorganisms demonstrated a substantial (κ = 0.78) and fair (κ = 0.40) agreement with urine culture. Optimal cut-off points detecting positive urine cultures were 135 BACT/µL (sensitivity [SE]: 92.1%, specificity [SP]: 85.4%, positive predictive value [PPV]: 71%, negative predictive value [NPV]: 96%) and 23 WBC/µL (SE: 73.5%, SP: 84.1%, PPV: 65%, NPV: 89%).

CONCLUSIONS

The UF-5000 analyzer (Sysmex) is a reliable diagnostic tool for UTI screening. The displayed BACT-Info flags allow a quick diagnostic orientation for the clinician. However, the authors suggest verifying the automated Gram categories with urine culture.

Urine Sediment Findings and the Immune Response to Pathologies in Fungal Urinary Tract Infections Caused by Candida spp

Fungi are pathogenic agents that can also cause disseminated infections involving the kidneys. Besides Candida, other agents like Cryptococcus spp. can cause urinary tract infection (UTI), as well as other non-yeast fungi, especially among immunocompromised patients. The detection and identification of fungi in urine samples (by microscopy and culture) plays an essential role in the diagnosis of fungal UTI. However, variable cutoff definitions and unreliable culture techniques may skew analysis of the incidence and outcome of candiduria. The sediment analysis plays a key role in the identification of fungal UTI because both yeasts and pseudohyphae are easily identified and can be used as a clinical sign of fungal UTI but should not be overinterpreted. Indeed, urine markers of the immune response (leukocytes), urine barriers of tissue protection (epithelial cells), and urine markers of kidney disease (urinary casts) can be found in urine samples. This work explores the manifestations associated with the fungal UTI from the urinalysis perspective, namely the urinary findings and clinical picture of patients with fungal UTI caused by Candida spp., aspects associated with the immune response, and the future perspectives of urinalysis in the diagnosis of this clinical condition.