APS calculator: a data-driven tool for setting outcome-based analytical performance specifications for measurement uncertainty using specific clinical requirements and population data

Author:

Çubukçu Hikmet Can12ORCID,Vanstapel Florent34ORCID,Thelen Marc56ORCID,van Schrojenstein Lantman Marith567ORCID,Bernabeu-Andreu Francisco A.8ORCID,Meško Brguljan Pika9ORCID,Milinkovic Neda10ORCID,Linko Solveig11ORCID,Panteghini Mauro12ORCID,Boursier Guilaine13ORCID

Affiliation:

1. General Directorate of Health Services, Rare Diseases Department , Turkish Ministry of Health , Ankara , Türkiye

2. Hacettepe University Institute of Informatics , Ankara , Türkiye

3. Laboratory Medicine , University Hospital Leuven , Leuven , Belgium

4. Department of Public Health, Biomedical Sciences Group , Catholic University Leuven , Leuven , Belgium

5. SKML, Foundation for Quality Assurance in Laboratory Medicine , Nijmegen , The Netherlands

6. Department of Laboratory Medicine , Radboud University Medical Centre , Nijmegen , The Netherlands

7. Result Laboratory for Clinical Chemistry , Amphia Hospital Breda , Breda , The Netherlands

8. Servicio Bioquímica – Análisis Clínicos Hospital Universitario Puerta de Hierro , Madrid , Spain

9. Department of Clinical Chemistry , University Clinic for Respiratory and Allergic Deseases , Golnik , Slovenia

10. Department of Medical Biochemistry, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia

11. Linko Q-Solutions , Helsinki , Finland

12. Research Centre for Metrological Traceability in Laboratory Medicine (CIRME) , University of Milan , Milano , Italy

13. Department of Molecular Genetics and Cytogenomics, Rare Diseases and Autoinflammatory Unit, CHU Montpellier , University of Montpellier , Montpellier , France

Abstract

Abstract Objectives According to ISO 15189:2022, analytical performance specifications (APS) should relate to intended clinical use and impact on patient care. Therefore, we aimed to develop a web application for laboratory professionals to calculate APS based on a simulation of the impact of measurement uncertainty (MU) on the outcome using the chosen decision limits, agreement thresholds, and data of the population of interest. Methods We developed the “APS Calculator” allowing users to upload and select data of concern, specify decision limits and agreement thresholds, and conduct simulations to determine APS for MU. The simulation involved categorizing original measurand concentrations, generating measured (simulated) results by introducing different degrees of MU, and recategorizing measured concentrations based on clinical decision limits and acceptable clinical misclassification rates. The agreements between original and simulated result categories were assessed, and values that met or exceeded user-specified agreement thresholds that set goals for the between-category agreement were considered acceptable. The application generates contour plots of agreement rates and corresponding MU values. We tested the application using National Health and Nutrition Examination Survey data, with decision limits from relevant guidelines. Results We determined APS for MU of six measurands (blood total hemoglobin, plasma fasting glucose, serum total and high-density lipoprotein cholesterol, triglycerides, and total folate) to demonstrate the potential of the application to generate APS. Conclusions The developed data-driven web application offers a flexible tool for laboratory professionals to calculate APS for MU using their chosen decision limits and agreement thresholds, and the data of the population of interest.

Publisher

Walter de Gruyter GmbH

Subject

Biochemistry (medical),Clinical Biochemistry,General Medicine

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