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Enregistrement W4407348681 · doi:10.1093/jalm/jfae159

Abstracts from the 29th ADLM International CPOCT Symposium: Quality Beyond the Lab: Navigating POCT Excellence in Patient Care

2024· article· en· W4407348681 sur OpenAlexaffabout
Julie Shaw, Adil I. Khan, Matthias Orth, Zahra Shajani-Yi, Nam K. Tran, Allison A. Venner

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCardiac, Anesthesia and Surgical Outcomes
Établissements canadiensUniversity of CalgaryUniversity of OttawaCanadian Electricity AssociationAlberta Health ServicesCalgary Laboratory ServicesOttawa Hospital
Organismes subventionnairesnon disponible
Mots-clésExcellencePoint-of-care testingQuality (philosophy)MedicinePolitical sciencePathologyPhysics

Résumé

récupéré en direct d'OpenAlex

The Association for Diagnostics & Laboratory Medicine (ADLM) International Critical and Point-of-Care Testing (CPOCT) Symposium is an engaging opportunity for those involved in all aspects of point-of-care testing (POCT) to come together to share success, challenges, ideas, and future insights related to this important and growing field. This year, the ADLM International CPOCT Symposium was held in beautiful San Diego, California. The symposium theme, “Quality Beyond the Lab,” was aimed at ensuring practical topics with tangible takeaway messages for attendees from the sessions. Topics were chosen to include a variety of perspectives, from individuals with diverse roles related to POCT and from different geographic locations. The future of POCT was addressed, considering growth of direct-to-consumer (DTC) testing and artificial intelligence (AI) technologies. There is no doubt that DTC testing and AI will change the way POCT is performed and managed. As laboratory professionals, we must acknowledge these trends and understand how best to navigate oversight of POCT to ensure quality. The symposium had the pleasure of many high-quality abstracts being submitted and accepted for poster presentation, with 9 abstracts selected by the planning committee for oral presentation during the symposium. Themes emerging from the oral and poster presentations included mobile POCT, risk assessment for POCT, validation of POCT coagulation and viscoelastic devices, identification of hemolysis for blood gas testing, and novel testing technologies in the pipeline. The opening keynote by Dr. Gerald Kost provided attendees with a glimpse into opportunities for use of POCT in challenging environments and geographical locations, particularly in resource-limited settings. Dr. Kost spoke of the role POCT can play in ensuring more equitable access to testing in all areas and in ensuring delivery of public healthcare in diverse settings. The first session focused on the POCT coordinator role, how laboratory directors can choose effective POCT coordinators, and how to best support POCT coordinators. The session included talks from Jeanne Mumford, a seasoned POCT coordinator from the United States and 2 POCT laboratory directors, Dr. Annette Thomas from the United Kingdom and Dr. Julie Shaw from Canada. The session offered 3 perspectives with common themes emerging. The role of POCT coordinator requires excellent time management and critical thinking skills to manage constant requests and challenges. Laboratory directors can best support POCT coordinators by advocating for resources, encouraging opportunities for networking, liaising with clinical teams, and creating standardized materials for all aspects of quality assurance. The second scientific session challenged attendees to consider POCT performed in non-traditional environments. Dr. Nichole Korpi-Steiner discussed verification of POCT complete blood count (CBC) instruments for testing in psychiatry clinics for patients taking clozapine. Jamie Acero presented on validation of blood gas and lactate testing in helicopter emergency medical services and the specific considerations for operating POCT in the aeromedical environment, such as vibration, barometric pressure, temperature, and potential electronic interference from flight instrumentation. Dr. Ronald Henriquez walked us through his real-life experiences in setting up POCT for the US military in austere environments. Development of a quality assurance framework in these environments must include flexibility for situations that may arise and incorporate risk assessment to guide decision making. Friday morning began with a debate between Dr. Kristin Hauff and Dr. Mattias Orth on whether DTC testing should be considered POCT. The lively debate raised several points for consideration, such as ethical considerations, reporting of DTC testing results in the electronic health record and potential benefits of DTC testing in improving access to care. Examples of DTC testing success stories, such as self-testing for glucose monitoring and urine pregnancy tests, were also shared. The lack of DTC testing regulation in most jurisdictions was raised as a primary concern when considering DTC as POCT, with laboratories encouraged to play a role in developing a regulatory framework to ensure DTC testing is safe and effective for the public. The last scientific session challenged attendees to consider the role of artificial intelligence and machine learning (ML) in POCT. Dr. Hooman Rashidi provided an introduction to AI and ML concepts in medicine, with potential use cases. He walked us through challenges in developing AI/ML models related to the large amount of data required to build and validate models, ethical approval for data usage, and regulatory compliance. An example of AI use in medicine for predicting acute kidney injury in burn patients was presented. Use of a combination of biomarkers in an ML model improved sensitivity and specificity over traditional diagnostic methods. Adil Khan discussed potential practical applications of AI for POCT. For example, interpretation of lateral flow immunoassays could be especially helpful for POCT performed outside hospital settings and in resource-limited settings such as in malaria diagnosis, which has always been a challenge due to traditional testing requiring well-trained and experienced microscopists that can be hard to find in resource-limited settings. Developing POCT hematology devices has always been a challenge for manufacturers because not only does the device need to be able to count cells, but there is also a need to identify the different cell morphologies and their different stages of development. Application of AI to a new generation of FDA-approved POCT hematology analyzers is overcoming these challenges with demonstrated accuracy when compared to traditional laboratory CBC instruments (1). The closing keynote talk was given by Dr. Naqi Khan, a physician leader in AI and ML at Amazon Web Services. Dr. Khan discussed the US federal government's quintuple aim, which includes utilization of AI to improve health outcomes. Important considerations when integrating AI technologies into healthcare settings are evidence-based studies as well as reimbursement. AI has the potential to improve clinical and operational efficiency within the healthcare system, improve the patient experience, and may have the potential to create a more holistic experience for a patient engaging with the healthcare system. Furthermore, AI/ML could have a transformative role in evaluating data from continuous measurement devices, ideally with connectivity to the electronic health record (EHR) for identification of relevant events that result in a tailored treatment plan, such as with continuous glucose monitoring devices, which show promise for use in the hospital setting, but can be challenging from a data analysis point of view, especially with healthcare workforce shortages (2). Integration of AI and POCT devices to offer decision support may be helpful to improve accuracy and tailor downstream treatments. The 29th ADLM International CPOCT Symposium offered attendees a wide variety of topics that were both practical and thought-provoking. The aim of the planning committee was to provide attendees with tangible takeaways that could be implemented in their home institutions. I wish to thank the planning committee for all their contributions and insights. I would also like to thank all the faculty who presented at the meeting. The sessions were rich with knowledge and initiated engaged discussions from attendees. I would also like to thank the sponsors for their support and the ADLM staff who worked tirelessly to ensure a seamlessly run conference. These abstracts have been reproduced without editorial alteration from the materials supplied by the authors. Infelicities of preparation, grammar, spelling, style, syntax, and usage are the authors’. Please refer to the Supplemental Information for the full abstracts presented. Poster #1 Kingston 3-year Study of Thrice Daily Testing of Blood Samples on Dual Blood Gas/Electrolyte Analyzers Demonstrates 16–24 Hour New Cartridge Instability Poster #2 Making Sense of College of American Pathologists External Quality Assessments of the GEM 5000 Poster #3 Mathematical Design Specifications and Strategic Point-of-care Requirements for COVID-19 Tests, New Infectious Diseases Threats, and 21st Century Pandemics Poster #4 Bridging the Gap: A Point-of-care Testing Telehealth Training Program for Enhanced Patient Care Poster #5 Assessing the Impact of Micro Clot Errors on GEM Premier 5000 Analyzer Results in a Point of Care Setting Poster #7 Mobile HbA1c Testing: Can a Point-of-Care Analyzer Still Work After Ground Transportation? Poster #8 Clinical Performance of a Novel Point-of-Care Coagulometer for the DOACs Poster #9 Evaluation of the Roche Cobas Pulse Point of Care Glucose Meter Poster #10 Evaluation of Pro-QCP, a Process Approach for Clinical Labs to Develop Risk-Based Quality Control Plans Poster #11 Clinical Concordance Between Viscoelastic Testing Methods for Transfusion Indication Poster #12 Evaluation of Hemolysis Rates in Adult and Pediatric Whole Blood Specimens at the Point of Care Poster #13 Assessment of In Vitro Hemolysis Using Three Commercially Available Methods Poster #14 Accuracy and Precision Evaluation a Novel Point-of-Care Coagulometer for the DOACs Poster #15 Hemolysis Detection and Flagging of Potassium Results on GEM Premier 7000 with iQM3 Poster #16 Novel In-line Hemolysis Detection on a Blood Gas Analyzer for Capillary Samples Poster #17 Critical Limits and Critical Values for Urgent Clinician Notification at Major US Medical Centers Poster #18 Detection of Human Immunodeficiency Virus (HIV) Proteins in Extracellular Vesicles (EVs) by Immunocapture Lateral Flow Method Poster #19 Validation of Capillary Blood Gas Specimen Delivery by a Pneumatic Tube System Poster #20 Validation of The Quantra® Hemostasis System with the QPlus® Cartridge at an Academic Hospital Poster #21 Delivering Better and Timely Diagnosis for Critical Patients: POC Clinical Performance vs Central Laboratory Assays in Key Cardiac Markers Poster #22 Clinical and Analytical Performance Evaluation of Savanna RVP4 Panel vs Three Methodologies Poster #23 Improving Diagnostic Support for Preeclampsia Diagnosis: Clinical Evaluation of Triage PLGF Assay in Gynecology Department at Clinica Redsalud Santiago Poster #24 Evaluation of the Impact of Heat exposure on i-STAT CHEM8+ Cartridges and a Repurposed i-STAT Transport Case for Use in Mobile Health Settings Poster #25 Comparison of Whole Blood And Plasma Measurements Using Roche B221, Abbott Istat and Beckman Au5800 Poster #26 POCT Whole Blood Creatinine Testing For Assessing Radiology Intravenous Contrast Risk Poster #27 Validation of the Haemonetics® TEG® 6s Analyzer and the Benefit of Point of Care Testing Expert Participation Poster #28 Observational Assessment of the Trends in Glycemic Control Observed through the Utilization of Donated Point of Care HbA1c Testing at Free and Charitable Clinic Poster #29 Visualizing Critical Limits and Critical Values Facilitates Understanding of Life-Threatening Test Results, Medical Decision Thresholds, and Pathophysiological Interpretation Poster #30 From Lab to Bedside: Comparative Evaluation of Blood Gas Testing With i-STAT Alinity System Poster #31 Benchmarking the miniDxR: Enhancing Lateral Flow Assay Performance for Point-of-Care Diagnostics Poster #32 Electronic Documentation of Point of Care Testing Waived and Non-Waived Training and Competency Assessment Forms using Microsoft SharePoint Software and Applications Poster #33 Evaluation of Novel Colloids to Enhance Assay Sensitivity in POC Testing Supplemental material is available at The Journal of Applied Laboratory Medicine online. Nonstandard Abbreviations: POCT, point-of-care testing; DTC, direct-to-consumer; ML, machine learning. Author Contributions: The corresponding author takes full responsibility that all authors on this publication have met the following required criteria of eligibility for authorship: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Nobody who qualifies for authorship has been omitted from the list. Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Research Funding: None declared. Disclosures: A.I. Khan has received funding from the IFCC and Abbott visiting lectureship program for chairing, attending, and speaking at IFCC-related conferences. Z. Shajani-Yi has received funding from ADLM, CAP, and ASCP to support travel and attendance at meetings and received salary and stock from Labcorp. J. Shaw has received instruments and reagents in-kind from Roche POCT for research studies. N. Tran is a consultant for Roche Diagnostics and Roche Molecular Systems. He has received funding from Roche Diagnostics and Roche Molecular Systems. He has received honoraria from Roche Diagnostics and Nova Biomedical. N. Tran has received study funding from Qiagen and Radiometer and has a provisional patent for Machine Intelligence Learning Optimization (MILO).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,557
Score d'incertitude au seuil0,552

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,296
Écart entre enseignants0,285 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

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Même revueThe Journal of Applied Laboratory MedicineMême sujetCardiac, Anesthesia and Surgical OutcomesTravaux en français237 207