Abstract
Pathology results are central to modern medical practice, informing diagnosis and patient management. To ensure high standards from pathology laboratories, regulators require compliance with international and local standards. In Australia, the monitoring and regulation of medical laboratories are achieved by conformance to ISO15189-National Pathology Accreditation Advisory Council standards, as assessed by the National Association of Testing Authorities (NATA), and an external quality assurance (EQA) assessment via the Royal College of Pathologists of Australasia Quality Assurance Program (RCPAQAP). While effective individually, integration of data collected by NATA and EQA testing promises advantages for the early detection of technical or management problems in the laboratory, and enhanced ongoing quality assessment. Random forest (RF) machine learning (ML) previously identified gamma-glutamyl transferase (GGT) as a leading predictor of NATA compliance condition reporting. In addition to further RF investigations, this study also deployed single decision trees and support vector machines (SVM) models that included creatinine, electrolytes and liver function test (LFT) EQA results. Across all analyses, GGT was consistently the top-ranked predictor variable, validating previous observations from Australian laboratories. SVM revealed broad patterns of predictive EQA marker interactions with NATA outcomes, and the distribution of GGT relative deviation suggested patterns by which to identify other strong EQA predictors of NATA outcomes. An integrated model of pathology quality assessment was successfully developed, via the prediction of NATA outcomes by EQA results. GGT consistently ranked as the best predictor variable, identified by combining recursive partitioning and SVM ML strategies.
Highlights
The introduction of the ISO 15189 International Standard is regarded as an advance for the maintenance of quality in laboratory medicine, providing a “Gold Standard” that applies to technical and management performance, while emphasizing the value of the duties undertaken by laboratory professionals [1,2]
This study further explores the relationship between National Association of Testing Authorities (NATA) and external quality assurance (EQA) results [6] through a larger laboratory sample from a separate quality assurance cycle, and which applied additional machine learning methods
The results of NATA site audits utilized in this study included three types of advice in relation to performance measured against ISO 15189 compliance—namely Conditions (C), Minor (M) and Observations (O)
Summary
The introduction of the ISO 15189 International Standard is regarded as an advance for the maintenance of quality in laboratory medicine, providing a “Gold Standard” that applies to technical and management performance, while emphasizing the value of the duties undertaken by laboratory professionals [1,2] Alongside these standards are additional discussions on quality improvement [3,4], suggesting a dynamic, ongoing process of clinical laboratory quality assurance. Complementing this approach, an external quality assurance (EQA) scheme is run by the Royal College of Pathologists of Australasia-Quality Assurance Programs (RCPAQAP). Drawing upon integrated NATA and EQA results will create opportunities for advances in the assessment of laboratory quality via predictive modelling, and performance monitoring improvements
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