Results of a Pilot External Quality Assessment Scheme for Genetic Testing of Newborns with Spinal Muscular Atrophy.

BACKGROUND: This study aims to evaluate the ability of laboratories to perform spinal muscular atrophy (SMA) genetic testing in newborns based on dried blood spot (DBS) samples, and to provide reference data and advance preparation for establishing the pilot external quality assessment (EQA) scheme for SMA genetic testing of newborns in China. METHODS: The pilot EQA scheme contents and evaluation principles of this project were designed by National Center for Clinical Laboratories (NCCL), National Health Commission. Two surveys were carried out in 2022, and 5 batches of blood spots were submitted to the participating laboratory each time. All participating laboratories conducted testing upon receiving samples, and test results were submitted to NCCL within the specified date. RESULTS: The return rates were 75.0% (21/28) and 95.2% (20/21) in the first and second surveys, respectively. The total return rate of the two examinations was 83.7% (41/49). Nineteen laboratories (19/21, 90.5%) had a full score passing on the first survey, while in the second survey twenty laboratories (20/20, 100%) scored full. CONCLUSIONS: This pilot EQA survey provides a preliminary understanding of the capability of SMA genetic testing for newborns across laboratories in China. A few laboratories had technical or operational problems in testing. It is, therefore, of importance to strengthen laboratory management and to improve testing capacity for the establishment of a national EQA scheme for newborn SMA genetic testing.

Result of a Pilot External Quality Assessment Scheme for Clinical Diagnosis of Inherited Metabolic Disorders in China.

BACKGROUND: We aimed to evaluate the diagnostic capabilities of Chinese laboratories for inherited metabolic disorders (IMDs) using gas chromatography-mass spectrometry (GC-MS) on urine samples. Meanwhile, based on the result of the pilot external quality assessment (EQA) scheme, we hope to establish a standardized and reliable procedure for future EQA practice. METHODS: We recruited laboratories that participated in the EQA of quantitative analysis of urinary organic acids with GC-MS before joining the surveys. In each survey, a set of five real urine samples was distributed to each participant. The participants should analyze the sample by GC-MS and report the "analytical result", "the most likely diagnosis", and "recommendation for further tests" to the NCCL before the deadline. RESULTS: A total of 21 laboratories participated in the scheme. The pass rates were 94.4% in 2020 and 89.5% in 2021. For all eight IMDs tested, the analytical proficiency rates ranged from 84.7% - 100%, and the interpretational performance rate ranged from 88.2% - 97.0%. The performance on hyperphenylalaninemia (HPA), 3-methylcrotonyl-CoA carboxylase deficiency (MCCD), and ethylmalonic encephalopathy (EE) samples were not satisfactory. CONCLUSIONS: In general, the participants of this pilot EQA scheme are equipped with the basic capability for qualitative organic acid analysis and interpretation of the results. Limited by the small size of laboratories and samples involved, this activity could not fully reflect the state of clinical practice of Chinese laboratories. NCCL will improve the EQA scheme and implement more EQA activities in the future.

Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies.

Importance: Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. Objective: To evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies. Design, Setting, and Participants: This diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists. Exposure: An AI-based assistive tool for Gleason grading of prostate biopsies. Main Outcomes and Measures: Agreement between pathologists and subspecialists with and without the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies. Results: Biopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses. Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement. Conclusions and Relevance: In this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.