Archives of pathology & laboratory medicine最新文献

Context.—: The blood bank is often consulted for transfusion support of patients with suspected platelet transfusion refractoriness (PTR). The workup is complex because testing includes specialized assays that are uncommonly ordered with limited availability. Add to this the variety of possible products-crossmatched platelets, human leukocyte antigen (HLA)-matched platelets, HLA antigen-negative platelets-and the approach to PTR can be overwhelming. Moreover, most literature on the subject is published in transfusion medicine journals aimed at transfusion medicine physicians and blood bank specialists in academic settings. Resources tailored to community hospital blood banks are lacking.

Objective.—: To provide pathologists who may not have subspecialized training in transfusion medicine and who direct blood banks algorithmic workflows based on clinical scenario and test availability to provide appropriate transfusion support for patients with PTR.

Data sources.—: This review is a comprehensive overview of terminology, HLA testing procedures, interpretations, and practical recommendations for managing PTR in various scenarios based on expert opinion as well as relevant medical literature published from 2007 to 2022.

Conclusions.—: Consultation on PTR is complicated and encompasses many clinical and laboratory aspects. The lack of guidelines derived from high-quality prospective studies poses challenges in the workup and management of PTR. Hindering the process further are limited test availability, unfamiliarity with the technical assays, and the various specialized platelet products. The clinical evaluation algorithm presented herein along with the workflow pathways offer pathologists user-friendly and best-practice guidelines with different options based on the clinical scenario and the tests available.

Context.—: Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential.

Objective.—: To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools.

Design.—: The article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics.

Results.—: CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms' performance compared to that of pathologists.

Conclusions.—: A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.

Context.—: Distinguishing metastatic carcinomas from mesotheliomas or reactive mesothelial cells in pleural, peritoneal, and pericardial effusions is a common diagnostic problem cytopathologists encounter.

Objective.—: To perform the first meta-analysis on the pooled diagnostic accuracy of claudin-4 immunochemistry in serous effusion cytopathology.

Design.—: This report followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for diagnostic test accuracy studies. Three databases (PubMed, Scopus, and the Cochrane Library) were searched until October 9, 2023, followed by study selection using specific inclusion and exclusion criteria and data extraction. The study quality assessment was performed by using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Statistical analysis was performed by using R to calculate the pooled sensitivity and specificity of claudin-4 immunochemistry. In addition, the diagnostic odds ratio was measured, representing the odds ratio of a positive result indicating a carcinoma rather than a mesothelial process in serous effusion cytology.

Results.—: Fourteen observational studies, published between 2011 and 2023, fulfilled the selection criteria and were included. All 14 studies used the 3E2C1 clone. Claudin-4 immunochemistry showed a high diagnostic accuracy in serous effusion cytology. The pooled sensitivity and specificity were 98.02% (95% CI, 93.96%-99.37%) and 99.72% (95% CI, 97.36%-99.97%), respectively. Lastly, the pooled diagnostic odds ratio was 1660.5 (95% CI, 760.0-3627.8) and no evidence of statistical heterogeneity between the included studies was found (I2 = 0%, τ2 = 0).

Conclusions.—: Claudin-4 may be used as a single pan-carcinoma immunochemical biomarker in the differential diagnosis between metastatic carcinomas and mesotheliomas or reactive mesothelial cells in serous effusion cytology.

Context.—: The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based models share some features of conventional laboratory testing methods. Accreditation requirements that specifically address clinical laboratories' use of ML remain in the early stages of development.

Objective.—: To identify relevant CAP accreditation requirements that may be applied to the clinical adoption of ML-based molecular oncology assays, and to provide examples of current and emerging ML applications in molecular oncology testing.

Design.—: CAP accreditation checklists related to molecular pathology and general laboratory practices (Molecular Pathology, All Common and Laboratory General) were reviewed. Examples of checklist requirements that are generally applicable to validation, revalidation, quality management, infrastructure, and analytical procedures of ML-based molecular oncology assays were summarized. Instances of ML use in molecular oncology testing were assessed from literature review.

Results.—: Components of the general CAP accreditation framework that exist for traditional molecular oncology assay validation and maintenance are also relevant for implementing ML-based tests in a clinical laboratory. Current and emerging applications of ML in molecular oncology testing include DNA methylation profiling for central nervous system tumor classification, variant calling, microsatellite instability testing, mutational signature analysis, and variant prediction from histopathology images.

Conclusions.—: Currently, much of the ML activity in molecular oncology is within early clinical implementation. Despite specific considerations that apply to the adoption of ML-based methods, existing CAP requirements can serve as general guidelines for the clinical implementation of ML-based assays in molecular oncology testing.

Context.—: Laboratory testing, beyond what is essential for managing health, is considered low-value care, posing patient risks and wasting resources. Measuring excess testing on a national level is crucial to identify waste and optimize healthcare resource allocation for maximum impact.

Objective.—: To measure inappropriate laboratory testing and its cost across Medicare and many US commercial payers.

Design.—: A retrospective analysis on 2019 claims data measured the frequency of 4 commonly used laboratory tests among 64 million individuals with Medicare and 168 million with commercial insurance. Tests included 25-hydroxy vitamin D, prostate-specific antigen, lipid panel, and hemoglobin A1c. Clinical guidelines, medical literature, and payer recommendations were used to determine appropriate testing frequencies. Costs of excessive testing were calculated using the 2019 clinical lab fee schedule. A targeted analysis of 2022 data confirmed 2019 trends.

Results.—: Analysis of ∼84 million tests from ∼1 billion outpatient test claim records revealed that 7% to 51% of tests exceeded recommended frequencies, with some egregious overuse: for example hemoglobin-A1c or prostate-specific antigen every week. The conservative cost estimate for 4 excess tests surpassed $350 million.

Conclusions.—: This extensive study, involving 232 million people, found that 14.4 million of 60.5 million individuals (23.8%) tested had undergone excessive laboratory testing, with likely little benefit and possible harm. Extrapolating findings to all laboratory testing suggests that Medicare alone may have incurred direct excess expenses from $1.95 to $3.28 billion in 2019, without factoring the hidden costs of excessive testing (eg, downstream care). Addressing unnecessary testing is crucial to lowering costs and redirecting resources for greater patient benefit.

Context.—: The joint College of American Pathologists/American College of Medical Genetics and Genomics Cytogenetics Committee works to ensure competency and proficiency of clinical cytogenetics testing laboratories through proficiency testing programs for various clinical tests offered by such laboratories, including the evaluation of constitutional abnormalities.

Objective.—: To review and analyze 20 years of constitutional chromosome analysis proficiency testing results (2003-2022), primarily utilizing G-banded karyograms.

Design.—: A retrospective review of results from 2003 through 2022 was performed, identifying challenges addressing constitutional disorders. The chromosomal abnormalities and overall performance were evaluated.

Results.—: A total of 184 cases from 161 proficiency testing challenges were administered from 2003 through 2022. Challenges consisted of metaphase images and accompanying clinical history for evaluation of numerical and/or structural abnormalities. Of the 184 cases, only 2 (1%) failed to reach an 80% grading consensus for recognition of the abnormality. Both cases illustrated the limitations of correctly characterizing some chromosomal abnormalities, including recombinant chromosomal abnormalities and isochromosome identification. In addition, 2 cases failed to reach a consensus for nomenclature reporting: 1 with an isochromosome and another with a duplication.

Conclusions.—: This 20-year review illustrates the high rate of competency and proficiency of cytogenetic laboratories in the correct identification of constitutional chromosome abnormalities.

Context.—: Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia distinguished by its rapidly progressive and fatal clinical course. Measurable/minimal residual disease (MRD) monitoring is vital for the prognosis and clinical management of acute myeloid leukemia.

Objective.—: To examine the immunophenotypes of the residual leukemic cells, evaluate the performance of multiparametric flow cytometry (FCM) measuring MRD, and compare it with molecular monitoring in patients diagnosed with APL.

Design.—: Two hundred seventy-seven patients with APL were enrolled. Immunophenotypes were prospectively analyzed by a 1-tube-10-color antibody panel via FCM. MRD of APL with PML::RARα was detected by real-time quantitative polymerase chain reaction (RQ-PCR). The clinical value of MRD as an indicator of survival was also examined.

Results.—: APL showed 5 distinct patterns of residual leukemic cells, based on CD45 and side-scatter scattergram, all with CD9 positivity and a previously unrealized loss of CD117. FCM-based MRD evaluation showed a concordance rate of 87.7% with PCR. At the end of the consolidation therapy, MRD measured by both PCR and FCM could differentiate patients with longer and shorter overall survival (OS) (P = .04 and P = .03, respectively). Patients with APL variant had a shorter OS than patients with APL who harbored PML::RARα (P < .001).

Conclusions.—: CD9 is a reliable marker to differentiate residual leukemic cells from normally differentiating myeloid cells. FCM demonstrated a high comparability to PCR-MRD and an excellent performance in predicting OS, and thus could potentially be used as a routine indicator in the clinical management of patients with APL.

Context.—: Robotic-assisted navigation bronchoscopy (R-ANB) is used to target peripheral pulmonary nodules that are difficult to biopsy using conventional approaches. Frozen sections are requested to confirm these lesions have been localized and/or to diagnose neoplasms that can be immediately resected.

Objective.—: To estimate diagnostic concordance between frozen section diagnosis (FSD) and formalin-fixed tissue diagnosis (FFTD) in biopsies obtained with R-ANB, calculate the sensitivity and specificity of FSD and FFTD for a diagnosis of malignancy, and evaluate whether the residual tissue that can be fixed in formalin after frozen section still has sufficient material for molecular studies.

Data sources.—: The results of consecutive FSD rendered on biopsies performed with R-ANB during a 30-month period were used to calculate the metrics listed above. FFTD and/or the diagnoses rendered on computed tomography-guided core biopsy subsequently performed in patients with negative R-ANB and/or lung resections in patients with malignancies were used as true-positive results. The overall concordance between FSD and FFTD in 226 lesions from 203 patients was 72%. Frozen section diagnosed 76 of 123 malignancies with 100% specificity and 68% sensitivity. Adequate material was available in 92% of biopsies where next-generation sequencing and other molecular studies were requested.

Conclusions.—: Intraoperative consultations are helpful to diagnose a variety of lung lesions and help surgeons confirm that targets have been accurately reached by R-ANB. Malignancies can be diagnosed with 100% specificity but only 68% sensitivity. The performance of frozen section did not interfere with the subsequent analysis of tissue with molecular studies in most cases.

Context.—: The identification of autoantibodies associated with autoimmune liver disease (ALD) is crucial for diagnosis and management. Various laboratory methods have been introduced to detect autoantibody profiles. However, the variable performance of these assays may create challenges for clinicians and patients.

Objective.—: To investigate the concordance rates and diagnostic performance of 2 commercially available assays, line immunoassay (LIA) and digital liquid chip method (DLCM), in patients with ALD.

Design.—: A total of 291 serum samples were collected, consisting of 180 sera from patients with ALD and 111 sera from controls. The samples were detected through LIA and DLCM. The agreement and diagnostic performance of each assay were analyzed.

Results.—: There was substantial to almost perfect agreement among prevalent autoantibodies (anti-mitochondrial antibody M2, antibodies against gp210, Sp100, and Ro52). Nevertheless, the Cohen κ coefficient of some uncommon autoantibodies (anti-LKM-1, anti-LC-1, and anti-SLA/LP) between the 2 methods was not ideal. LIA showed slightly better sensitivity, accuracy, and negative predictive value, while DLCM exhibited slightly higher specificity and positive predictive value.

Conclusions.—: LIA and DLCM demonstrated comparable performance for the detection of common ALD-related autoantibodies. LIA seemed to be more sensitive, while DLCM displayed more specificity. However, standardization of ALD autoantibody detection still faces challenges between these diverse detection systems. Comprehensive interlaboratory validation is essential to mitigate potential misunderstanding and confusion among patients and clinicians.