Evaluation of performance in preanalytical phase EQA: can laboratories mitigate common pitfalls? DOI
Anna Linko-Parvinen, Jonna Pelanti,

Tanja Vanhelo

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Abstract Objectives Preanalytical phase is an elemental part of laboratory diagnostics, but prone to humane errors. The aim this study was evaluate performance in preanalytical external quality assessment (EQA) cases. We also suggest preventive actions for risk mitigation. Methods included 12 EQA rounds (Labquality Ltd.) with three patient cases (36 cases, 54–111 participants, 7–15 countries) published 2018–2023. graded according percentage correct responses each case as ≥900 % excellent, 70–89 good, 50–69 satisfactory, 30–49 fair and <30 poor. Performance simultaneously failed ≥10 leading harmful events. Results Overall excellent 7, good 12, satisfactory 10, 4 poor 3 Additionally, 7 showed performance. Routine requests incorrect sample tubes or handling were detected Lower seen sudden abnormal results, rare requests, false identification (never-events) test requests. Information technology (IT) solutions (preanalytical checklists, autoverification rules specific notifications) could have prevented 33 36 Conclusions While most common errors performance, samples those requiring individualised consideration are vulnerable human misinterpretation. In many instances, should been identified rejected before reaching the being directed analysis. Optimising IT effectively detect these allows focus on infrequent events demanding accessible professional consultation. may help education occasions.

Language: Английский

Guidance-Based Appropriateness of Hemostasis Testing in the Acute Setting DOI
Luigi Devis, Deepa J. Arachchillage, Michaël Hardy

et al.

Hämostaseologie, Journal Year: 2025, Volume and Issue: 45(01), P. 024 - 048

Published: Feb. 1, 2025

Abstract In this review, we aim to highlight the extent of inappropriate hemostasis testing and provide practical guidance on how prevent it. We will focus acute setting, including but not limited emergency department intensive care unit. To end, first discuss significance inappropriateness, in general context laboratory medicine. This includes acknowledging importance phenomenon attempting define Next, describe harmful consequences testing. Finally, use setting. The second section describes interventions―in particular, implementation for testing—can efficiently reduce inappropriateness. third section, summarize available recommendations rational (platelet count, activated partial thromboplastin time, prothrombin time/international normalized ratio, fibrinogen, thrombin D-dimer, anti-Xa assay, antithrombin, ADAMTS13 activity, antiheparin-PF4 antibodies, viscoelastometric tests, coagulation factors, platelet function testing), as supported by guidelines, recommendations, and/or expert opinions. Overall, review is intended be a toolkit effort promote appropriate Hopefully, new Vitro Diagnostic Medical Device Regulation (EU) 2017/746 (IVDR) should help improving availability evidence regarding clinical performance assays.

Language: Английский

Citations

2

Harnessing AI for enhanced evidence-based laboratory medicine (EBLM) DOI Creative Commons
Tahir S. Pillay, Deniz İlhan Topçu, Sedef Yenice

et al.

Clinica Chimica Acta, Journal Year: 2025, Volume and Issue: 569, P. 120181 - 120181

Published: Feb. 3, 2025

The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing computer vision) advance evidence-based medicine (EBLM) by automating optimizing critical processes(formulating clinical questions, conducting literature searches, appraising evidence, developing guidelines). These reduce the time for systematic reviews, ensuring consistency in appraisal, enabling real-time updates to guidelines. supports analyzing large datasets, genetic information electronic health records (EHRs), tailor treatment plans profiles. Predictive analytics enhance outcomes leveraging historical data ongoing monitoring predict responses optimize care pathways. Despite transformative potential, there are challenges. transparency, explainability algorithms gaining trust ethical deployment. Integration existing workflows requires collaboration between developers users ensure seamless user-friendly adoption. Ethical considerations, such as privacy,data security, algorithmic bias, must also be addressed mitigate risks equitable healthcare delivery. Regulatory frameworks, eg. EU Regulation, emphasize governance, human oversight, particularly high-risk systems. economic benefits cost savings, improved precision, enhanced outcomes. Future trends (federated learning self-supervised learning), will scalability applicability EBLM, paving way a new era precision medicine. EBLM has potential transform delivery, improve outcomes, personalized/precision

Language: Английский

Citations

1

Is it feasible for European laboratories to use SI units in reporting results? DOI Creative Commons

Martina Zaninotto,

Luisa Agnello, Lora Dukić

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Abstract The ultimate goal of harmonization, crucial to quality in laboratory medicine, is improve patient outcomes by providing accurate, actionable information. Patients and healthcare professionals assume that clinical tests performed different laboratories at times on the same type sample are comparable, results can be reliably consistently interpreted. In this context reporting units for have a considerable influence numeric result. harmonization measurement report, leads provision interchangeable comparable results, thus maximizing validity information, assuring more accurate diagnosis better treatment patient. However, although efforts been made recent years, criticisms continue. This opinion paper, prepared jointly EFLM Committee Harmonization (C-H) Postanalytical phase (C-POST), describes “general pragmatic approach” proposed drafting guidelines order ensure they used as widely possible.

Language: Английский

Citations

0

Digital metrology in laboratory medicine: a call for bringing order to chaos to facilitate precision diagnostics DOI
Madeleen Bosma, Christa M. Cobbaert

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Abstract Laboratory medicine is faced with rapid developments in data exchange, secondary use of and artificial intelligence. Safe exchange laboratory requires a suitable terminology standard. NPU, LOINC SNOMED CT are increasingly used for this purpose, but none these standards can currently accommodate safe across the full spectrum conventional data. Furthermore, technological advances in, amongst others, ‘omics’ area will enforce shift towards precision diagnostics. These emerging technologies demand an appropriate future-proof Given current future challenges terminologies, we here present concept digital metrology medicine. Terminology should be adjusted to state science allow interpretation. Essential test information entails pre-pre-analysis post-post-analysis. Major improvements needed include sufficient coding detail molecular form measurand on metrological traceability. especially given diagnostics, it become essential indicate interrelationships between measurands. Herefore, integration established taxonomies would improved identification measurands linkage scientific multidisciplinary science. Hence, further gain specificity value. The time has come lay basis era global focus. A consensus move forward health within Europe beyond.

Language: Английский

Citations

0

New insights in preanalytical quality DOI
Mario Plebani, Sheri Scott, Ana-Maria Šimundić

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Abstract The negative impact of preanalytical errors on the quality laboratory testing is now universally recognized. Nonetheless, recent technological advancements and organizational transformations in healthcare – catalyzed by still ongoing coronavirus disease 2019 (COVID-19 pandemic) have introduced new challenges promising opportunities for improvement. integration value-based scoring systems clinical laboratories growing evidence linking to patient outcomes costs underscore critical importance this phase. Emerging topics phase include pursuit a “greener” more sustainable environment, innovations self-sampling automated blood collection, strategies minimize loss. Additionally, efforts reduce enhance sustainability through management gained momentum. Digitalization artificial intelligence (AI) offer transformative potential, with applications sample labeling, recording collection events, monitoring conditions during transportation. AI-driven tools can also streamline workflow mitigate errors. Specific managing hemolysis developing its impact, addressing issues related urine designing robust protocols stability studies. rise decentralized presents unique hurdles, while emerging areas such as liquid biopsy anti-doping introduce novel complexities. Altogether, these highlight dynamic evolution need continuous innovation standardization. This collective opinion paper, which summarizes abstracts lectures delivered at two-day European Federation Laboratory Medicine (EFLM) Preanalytical Conference entitled “New Insight Quality” (Padova, Italy; December 12–13, 2025), provides comprehensive overview errors, offers some important insights into less obvious sources vulnerability proposes efficient

Language: Английский

Citations

0

Beyond test results: the strategic importance of metadata for the integration of AI in laboratory medicine DOI Creative Commons
Fabio Del Ben

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Language: Английский

Citations

0

Non-targeted detection of cathinones by high-resolution mass spectrometry based on their fragmentation pattern prediction. Application to pyrrolidine analogues in a hair case of PV8 DOI
José Manuel Matey, Luis Manuel Menéndez-Quintanal, Félix Zapata

et al.

Forensic Chemistry, Journal Year: 2024, Volume and Issue: 42, P. 100630 - 100630

Published: Dec. 10, 2024

Language: Английский

Citations

0

Evaluation of performance in preanalytical phase EQA: can laboratories mitigate common pitfalls? DOI
Anna Linko-Parvinen, Jonna Pelanti,

Tanja Vanhelo

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Abstract Objectives Preanalytical phase is an elemental part of laboratory diagnostics, but prone to humane errors. The aim this study was evaluate performance in preanalytical external quality assessment (EQA) cases. We also suggest preventive actions for risk mitigation. Methods included 12 EQA rounds (Labquality Ltd.) with three patient cases (36 cases, 54–111 participants, 7–15 countries) published 2018–2023. graded according percentage correct responses each case as ≥900 % excellent, 70–89 good, 50–69 satisfactory, 30–49 fair and <30 poor. Performance simultaneously failed ≥10 leading harmful events. Results Overall excellent 7, good 12, satisfactory 10, 4 poor 3 Additionally, 7 showed performance. Routine requests incorrect sample tubes or handling were detected Lower seen sudden abnormal results, rare requests, false identification (never-events) test requests. Information technology (IT) solutions (preanalytical checklists, autoverification rules specific notifications) could have prevented 33 36 Conclusions While most common errors performance, samples those requiring individualised consideration are vulnerable human misinterpretation. In many instances, should been identified rejected before reaching the being directed analysis. Optimising IT effectively detect these allows focus on infrequent events demanding accessible professional consultation. may help education occasions.

Language: Английский

Citations

0