Contribution of laboratory medicine and emerging technologies to cardiovascular risk reduction via exposome analysis: an opinion of the IFCC Division on Emerging Technologies.

This opinion article highlights the critical role of laboratory medicine and emerging technologies in cardiovascular risk reduction through exposome analysis. The exposome encompasses all external and internal exposures an individual faces throughout their life, influencing the onset and progression of cardiovascular diseases (CVD). Integrating exposome data with genetic information allows for a comprehensive understanding of the multifactorial causes of CVD, facilitating targeted preventive interventions. Laboratory medicine, enhanced by advanced technologies such as metabolomics and artificial intelligence (AI), plays a pivotal role in identifying and mitigating these exposures. Metabolomics provides detailed insights into metabolic changes triggered by environmental factors, while AI efficiently processes complex datasets to uncover patterns and associations. This integration fosters a proactive approach in public health and personalized medicine, enabling earlier detection and intervention. The article calls for global implementation of exposome technologies to improve population health, emphasizing the need for robust technological platforms and policy-driven initiatives to seamlessly integrate environmental data with clinical diagnostics. By harnessing these innovative technologies, laboratory medicine can significantly contribute to reducing the global burden of cardiovascular diseases through precise and personalized risk mitigation strategies.

Validation and verification framework and data integration of biosensors and in vitro diagnostic devices: a position statement of the IFCC Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MBHLM) and the IFCC Scientific Division.

Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome.

A new door to a different world: opportunities from the metaverse and the raise of meta-medical laboratories.

OBJECTIVES: In the digital age, the metaverse has emerged with impressive potential for many segments of society. The metaverse could be presented as a parallel dimension able to enhance the physical world as well as our actions and decisions in it with the objective to use a coalition between the natural and virtual worlds for value creation. Our aim was to elaborate on the impact of the metaverse on laboratory medicine. METHODS: Based on the available evidence, literature and reports, we analyzed the different perspectives of the metaverse on laboratory medicine and the needs for an efficient transition. RESULTS: The convergence and integration of technologies in the metaverse will participate to the reimagination of laboratory medicine services with augmented services, users' experiences, efficiency, and personalized care. The revolution around the metaverse offers different opportunities for laboratory medicine but also open multiple related challenges that are presented in this article. CONCLUSIONS: Scientific societies, multidisciplinary teams and specialists in laboratory medicine must prepare the integration metaverse and meta-medical laboratories, raise the awareness, educate, set guidance to obtain a maximum of value and mitigate potential adverse consequences.

Target association rule mining to explore novel paediatric illness patterns in emergency settings.

BACKGROUND AND AIMS: To assess the hospitalized sick children admitted to the pediatric emergency department (ED) and to find new patterns of clinical and laboratory attributes using association rule mining (ARM). METHODS: In this observational study, 158 children with median (IQR) age 11 months and a PRISM III score of 5 (2-9) were enrolled. Hotspot data mining method was applied to assess clinical attributes, lab investigations and pre-defined outcome parameters of children and their association in sick hospitalized children aged 1 month to 12 years. RESULTS: We obtained 30 rules with value for outcome as discharge is given attributes as follows: duration of hospitalization > 4 days, lactate > 1.2 mmol/L, platelet = 3.67/µL, dur_ventil = 0 h, serum K = 5.2 mmol/L, SBP = 120 mmHg, pCO2 = 41.9 mmHg, PaO2 = 163 mmHg, age = 92 months, heart rate > 114-159 per minute, temperature > 98 °F, GCS (Glasgow Coma Scale) > 7-14, gas K = 4.14 mmol/L, gas Na = 138.1 mmol/L, BUN (Blood Urea Nitrogen) = 18.69 mg/dL, Diagnosis > 1-718, Creatinine = 1.2 mg/dL, serum Na = 148 mmol/L, shock = 2, Glucose = 144 mg/dL, Mg(i) > 0.23 meq/L, BUN > 6.54 mg/dL. CONCLUSION: ARM is an effective data analysis technique to find meaningful patterns using clinical features with actual numbers in pediatric critical illness. It can prove to be important while analysing the association of clinical attributes with disease pattern, its features, and therapeutic or intervention success patterns.

Artificial intelligence and thyroid disease management: considerations for thyroid function tests.

Artificial intelligence (AI) is transforming healthcare and offers new tools in clinical research, personalized medicine, and medical diagnostics. Thyroid function tests represent an important asset for physicians in the diagnosis and monitoring of pathologies. Artificial intelligence tools can clearly assist physicians and specialists in laboratory medicine to optimize test prescription, tests interpretation, decision making, process optimization, and assay design. Our article is reviewing several of these aspects. As thyroid AI models rely on large data sets, which often requires distributed learning from multi-center contributions, this article also briefly discusses this issue.

Lessons Learned from the COVID-19 Pandemic: Emphasizing the Emerging Role and Perspectives from Artificial Intelligence, Mobile Health, and Digital Laboratory Medicine.

SARS-CoV-2, the new coronavirus causing COVID-19, is one of the most contagious disease of past decades. COVID-19 is different only in that everyone is encountering it for the first time during this pandemic. The world has gone from complete ignorance to a blitz of details in a matter of months. The foremost challenge that the scientific community faces is to understand the growth and transmission capability of the virus. As the world grapples with the global pandemic, people are spending more time than ever before living and working in the digital milieu, and the adoption of Artificial Intelligence (AI) is propelled to an unprecedented level especially as AI has already proven to play an important role in counteracting COVID-19. AI and Data Science are rapidly becoming important tools in clinical research, precision medicine, biomedical discovery and medical diagnostics. Machine learning (ML) and their subsets, such as deep learning, are also referred to as cognitive computing due to their foundational basis and relationship to cognition. To date, AI based techniques are helping epidemiologists in projecting the spread of virus, contact tracing, early detection, monitoring, social distancing, compiling data and training of healthcare workers. Beside AI, the use of telemedicine, mobile health or mHealth and the Internet of Things (IOT) is also emerging. These techniques have proven to be powerful tools in fighting against the pandemic because they provide strong support in pandemic prevention and control. The present study highlights applications and evaluations of these technologies, practices, and health delivery services as well as regulatory and ethical challenges regarding AI/ML-based medical products.

Digital Diagnostics and Mobile Health in Laboratory Medicine: An International Federation of Clinical Chemistry and Laboratory Medicine Survey on Current Practice and Future Perspectives.

BACKGROUND: A survey of IFCC members was conducted to determine current and future perspectives on digital innovations within laboratory medicine and healthcare sectors. METHODS: Questions focused on the relevance of digital diagnostic solutions, implementation and barriers to adopting digital technologies, and supplier roles in supporting innovation. Digital diagnostic market segments were defined by solution recipient (laboratory, clinician, patient/consumer, payor) and proximity to core laboratory operations. RESULTS: Digital solutions were of active interest for >90% of respondents. Although solutions to improve core operations were ranked as the most relevant currently, a future shift to technologies beyond core laboratory expertise is expected. A key area of potential differentiation for laboratory customers was clinical decision support. Currently, laboratories collaborate strongly with suppliers of laboratory integration software and information systems, with high expectations for future collaboration in clinical decision support, disease self-management, and population health management. Asia Pacific countries attributed greater importance to adopting digital solutions than those in other regions. Financial burden was the most commonly cited challenge in implementing digital solutions. CONCLUSIONS: Specialists in laboratory medicine are proactively approaching digital innovations and transformation, and there is high enthusiasm and expectation for further collaboration with suppliers and healthcare professionals beyond current core laboratory expertise.

Sustainability in Healthcare: Perspectives and Reflections Regarding Laboratory Medicine.

Healthcare structures serve to protect and improve public health; however, they can have negative effects on human well-being and the environment. Thus, sustainability is an important target in a rapidly changing healthcare environment. We analyzed the state of the art in research on healthcare and sustainability by exploring literature on different healthcare systems and their relations with the environment. Our review presents conceptual and practical developments regarding sustainability, as well as an overview of their evolution in the healthcare sector over time. We also discuss how sustainability could be applied to reduce the environmental impact of clinical laboratories by ensuring that resources are used efficiently and responsibly. Finally, we describe how laboratory medicine can contribute to a sustainable healthcare system through integration of innovation and emerging technologies while providing high-quality services to patients and caregivers.

Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine.

Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision­making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enablecost-effectiveness with reducereadmissionand mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.

Key questions about the future of laboratory medicine in the next decade of the 21st century: A report from the IFCC-Emerging Technologies Division.

This review advances the discussion about the future of laboratory medicine in the 2020s. In five major topic areas: 1. the "big picture" of healthcare; 2. pre-analytical factors; 3. Analytical factors; 4. post-analytical factors; and 5. relationships, which explores a next decade perspective on laboratory medicine and the likely impact of the predicted changes by means of a number of carefully focused questions that draw upon predictions made since 2013. The "big picture" of healthcare explores the effects of changing patient populations, the brain-to-brain loop, direct access testing, robots and total laboratory automation, and green technologies and sustainability. The pre-analytical section considers the role of different sample types, drones, and biobanks. The analytical section examines advances in point-of-care testing, mass spectrometry, genomics, gene and immunotherapy, 3D-printing, and total laboratory quality. The post-analytical section discusses the value of laboratory medicine, the emerging role of artificial intelligence, the management and interpretation of omics data, and common reference intervals and decision limits. Finally, the relationships section explores the role of laboratory medicine scientific societies, the educational needs of laboratory professionals, communication, the relationship between laboratory professionals and clinicians, laboratory medicine financing, and the anticipated economic opportunities and outcomes in the 2020's.

Documenting metrological traceability as intended by ISO 15189:2012: A consensus statement about the practice of the implementation and auditing of this norm element.

ISO15189:2012 requires medical laboratories to document metrological traceability of their results. While the ISO17511:2003 standard on metrological traceability in laboratory medicine requires the use of the highest available level in the traceability chain, it recognizes that for many measurands there is no reference above the manufacturer's selected measurement procedure and the manufacturer's working calibrator. Some immunoassays, although they intend to measure the same quantity and may even refer to the same reference material, unfortunately produce different results because of differences in analytical selectivity as manufacturers select different epitopes and antibodies for the same analyte. In other cases, the cause is the use of reference materials, which are not commutable. The uncertainty associated with the result is another important aspect in metrological traceability implementation. As the measurement uncertainty on the clinical samples is influenced by the uncertainty of all steps higher in the traceability chain, laboratories should be provided with adequate and appropriate information on the uncertainty of the value assignment to the commercial calibrators that they use. Although the between-lot variation in value assignment will manifest itself as part of the long-term imprecision as estimated by the end-user, information on worst-case to be expected lot-lot variation has to be communicated to the end-user by the IVD provider. When laboratories use ancillary equipment that potentially could have a critical contribution to the reported results, such equipment needs verification of its proper calibration and criticality to the result uncertainty could be assessed by an approach based on risk analysis, which is a key element of ISO15189:2012 anyway. This paper discusses how the requirement for metrological traceability as stated in ISO15189 should be met by the medical laboratory and how this should be assessed by accreditation bodies.

A position paper of the EFLM Committee on Education and Training and Working Group on Distance Education Programmes/E-Learning: developing an e-learning platform for the education of stakeholders in laboratory medicine.

The progress of information and communication technologies has strongly influenced changes in healthcare and laboratory medicine. E-learning, the learning or teaching through electronic means, contributes to the effective knowledge translation in medicine and healthcare, which is an essential element of a modern healthcare system and for the improvement of patient care. E-learning also represents a great vector for the transfer knowledge into laboratory practice, stimulate multidisciplinary interactions, enhance continuing professional development and promote laboratory medicine. The European Federation of Laboratory Medicine (EFLM) has initiated a distance learning program and the development of a collaborative network for e-learning. The EFLM dedicated working group encourages the organization of distance education programs and e-learning courses as well as critically evaluate information from courses, lectures and documents including electronic learning tools. The objectives of the present paper are to provide some specifications for distance learning and be compatible with laboratory medicine practices.