Journal of Applied Laboratory Medicine最新文献

Background: Clonal plasma cell disorders, such as multiple myeloma (MM), often cause excretion of monoclonal free light chains (MFLC) into urine that serve as diagnostic markers and can cause renal injury.

Content: Measures of urinary protein excretion (PEx) and MFLC excretion are parameters for diagnosing and managing plasma cell disorders, although the roles are evolving as new diagnostic tools are applied. Current guidelines dictate measuring PEx and MFLC excretion using 24-hour urine specimens, which have multiple shortcomings that compromise the quality of testing, delay results, and are burdensome for patients. These problems raise consideration of alternatives to the 24-hour PEx (24-hPEx). Such changes in practice have occurred for evaluating proteinuria in many other disorders. Calculating an estimated 24-hPEx based on urine protein/creatinine ratios on spot specimens is one option that overcomes many shortcomings of the measured 24-hPEx. Random urine specimens also probably are preferable for qualitative testing (absence or presence of MFLC) for diagnostic applications and MM response monitoring.

Summary: Measurement of PEx and MFLC excretion using 24-hour collections is unreliable, inconvenient, and delays evaluation of plasma cell disorders. Estimated 24-hPEx based on protein assays of spot urine specimens overcomes many of these problems and should be evaluated by further studies. Changing routine practice requires guideline and protocol modification and action by laboratories to increase availability of testing and calculated values. Issues described here also have relevance to evaluating proteinuria in other disorders.

Background: Point-of-care (POC) diagnostic devices deliver rapid, near-patient results that drive timely clinical decisions across diverse settings (from emergency departments to home care). Their decentralized deployment mandates a rigorous, multi-phase validation strategy to ensure analytical accuracy, clinical reliability, and real-world utility before both regulatory clearance and reimbursement.

Content: We propose an expanded, integrated framework comprising 4 pillars:Analytical validity: Quantification of sensitivity, specificity, predictive values adjusted for prevalence, limits of detection, bias/imprecision, and reproducibility using Receiver Operating Characteristic (ROC) curve analysis, Bland-Altman comparison, Passing-Bablok/Deming regression, and nonparametric techniques for semiquantitative outputs.Clinical validity: Demonstration of substantial equivalence via FDA 510(k) (Class II), de novo (novel low/moderate risk), or premarket approval (PMA; Class III with Investigational Device Exemption (IDE)-supported pivotal trials) pathways, supported by prospective, multicenter clinical studies, and human-factors usability assessments in intended use environments.Clinical utility: Evidence of improved patient care from outcome-based trials (e.g., time-to-treatment and readmission rates), health-economic analyses (cost per quality-adjusted life year and budget-impact models), and patient-reported outcome measures capturing usability, satisfaction, and adherence.Regulatory alignment: Harmonization of FDA and Health Canada requirements, including ISO 14971 risk management, post-market surveillance (21 CFR 820; Medical Device Licence [MDL] vigilance), to streamline market access and payer coverage decisions.

Summary: This comprehensive, staged validation pathway, from analytical benchmarks through clinical performance and utility to regulatory and reimbursement strategies, provides a practical roadmap for innovators, clinicians, and regulators. Embedding real-world evidence and coordinating US and Canadian frameworks accelerates the adoption of safe, effective, and value-based POC diagnostics, fostering better patient outcomes, and supporting modern precision medicine.

Background: Manual morphological analysis of peripheral blood smears (PBS) with light microscopy is an essential diagnostic and monitoring tool. Recently, automated morphology analyzers have been developed that can preclassify cells using artificial intelligence algorithms. This study aims to evaluate the preliminary leukocyte classification capabilities of the MC-80 digital morphology analyzer, a novel system, in pediatric patients and compare its performance with that of manual microscopy, the current gold standard.

Methods: This retrospective study was conducted at SBU Bursa Yüksek İhtisas Training and Research Hospital between September 5 and 29, 2022. Blood samples from 153 consecutive pediatric patients (age range: 0-18 years; median age: 3 years) undergoing simultaneous hemograms and PBS analyses were assessed using both the MC-80 digital morphology analyzer and manual microscopy.

Results: Spearman rank correlation coefficients indicated a high correlation for neutrophils (rho = 0.742; 95% CI: 0.661-0.807) and lymphocytes (rho = 0.745; 95% CI: 0.666-0.810) while the correlation for blast cells was significantly lower (rho = 0.079; 95% CI: -0.099-0.238). Concordance between the MC-80 and manual microscopy was minimal for monocytes (κ = 0.21; 95% CI: 0.11-0.29) and negligible for blast cells (κ = 0.08; 95% CI: 0.00-0.17).

Conclusions: The MC-80 digital morphology analyzer shows acceptable preliminary classification for neutrophils and lymphocytes; further development is required before it can be routinely implemented in laboratory workflows.

Background: Coagulation testing has an important role in the diagnosis, monitoring, and therapeutic decision-making for patients with abnormal hemostasis. As the field of anticoagulation options has expanded, the introduction of direct oral anticoagulant (DOAC) drugs has provided an option for patients utilizing drugs that do not require routine monitoring. Patients on these drugs may still need coagulation testing for prognostic purposes, and there are a number of nuances to consider when performing coagulation testing in patients who are prescribed DOACs.

Content: This document provides guidance on the tests that may or may not be impacted by the presence of DOACs in the blood. A discussion is provided about specific coagulation tests used and the impact of testing samples with a DOAC present. Options are presented to help mitigate the impact of DOACs on the testing. In addition, this document discusses how to test for and interpret DOAC concentrations in specific patient populations.

Background: Urine cultures are frequently ordered tests with a low positivity rate. Development of a machine learning model to predict urine culture outcomes could not only reduce unnecessary urine cultures but also prevent preliminary antibiotic treatment, thereby improving the quality of diagnostic healthcare decision-making.

Methods: An eXtreme Gradient Boosting (XGBoost) model to predict urine culture outcomes of adult patients was developed. Data was extracted from the electronic health records and laboratory information system of the Amsterdam University Medical Centers (Amsterdam UMC) between 2019 and 2021. Amsterdam UMC is an academic hospital in the Netherlands with 2 separate locations: VU Medical Center (VUmc) and Academic Medical Center (AMC). The VUmc cohort was used for model development and internal validation. External validation was performed in the AMC cohort. All ordering departments were included, i.e., emergency department and inpatient and outpatient clinics. No specific patient groups were excluded.

Results: The VUmc and AMC cohort consisted of 8015 and 10 078 unique urine cultures, respectively. The positive urine culture rate was 19.4% in the VUmc and 12.0% in the AMC. In the VUmc, the model achieved an area under the receiver operating characteristic (AUROC) of 0.834 (95% CI ± 0.010). During external validation in the AMC, the AUROC was 0.800 (95% CI ± 0.015).

Conclusions: We presented an XGBoost model to predict urine culture outcomes, which retained its performance during external validation. Contrary to most other models, adult patients of all ordering departments were included, which impedes future implementation. An additional external validation and prospective evaluation will be necessary before implementation with the aim of reducing unnecessary urine cultures.