Integrated diagnostics: the future of laboratory medicine?

Myositis and Its Mimics: Guideline Updates, MRI Characteristics, and New Horizons

Myositis is defined as inflammation within skeletal muscle and is a subcategory of myopathy, which is more broadly defined as any disorder affecting skeletal muscle. Myositis may be encountered as a component of autoimmune and connective tissue disease, where it is described as idiopathic inflammatory myopathy. Myositis can also be caused by infections, as well as toxins and drugs, including newer classes of medications. MRI plays an important role in the diagnosis and evaluation of patients with suspected myositis, but many entities may have imaging features similar to myositis and can be considered myositis mimics. These include muscular dystrophies, denervation, deep venous thrombosis, diabetic myonecrosis, muscle injury, heterotopic ossification, and even neoplasms. In patients with suspected myositis, definitive diagnosis may require integrated analysis of imaging findings with clinical, laboratory, and pathology data. The objectives of this article are to review the fundamental features of myositis, including recent updates in terminology and consensus guidelines for idiopathic inflammatory myopathies, the most important MRI differential diagnostic considerations for myositis (i.e., myositis mimics), and new horizons, including the potential importance of artificial intelligence and multimodal integrated diagnostics in the evaluation of patients with muscle disorders.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Harmonizing the post-analytical phase: focus on the laboratory report

The final, post-analytical, phase of laboratory testing is increasingly recognized as a fundamental step in maximizing quality and effectiveness of laboratory information. There is a need to close the loop of the total testing cycle by improving upon the laboratory report, and its notification to users. The harmonization of the post-analytical phase is somewhat complicated, mainly because it calls for communication that involves parties speaking different languages, including laboratorians, physicians, information technology specialists, and patients. Recently, increasing interest has been expressed in integrated diagnostics, defined as convergence of imaging, pathology, and laboratory tests with advanced information technology (IT). In particular, a common laboratory, radiology and pathology diagnostic reporting system that integrates text, sentinel images and molecular diagnostic data to an integrated, coherent interpretation enhances management decisions and improves quality of care.

Machine learning-based clinical decision support using laboratory data

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Computational pathology: an evolving concept

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.

How to prepare for a bright future of radiology in Europe

Because artificial intelligence (AI)-powered algorithms allow automated image analysis in a growing number of diagnostic scenarios, some healthcare stakeholders have raised doubts about the future of the entire radiologic profession. Their view disregards not only the role of radiologists in the diagnostic service chain beyond reporting, but also the many multidisciplinary and patient-related consulting tasks for which radiologists are solicited. The time commitment for these non-reporting tasks is considerable but difficult to quantify and often impossible to fulfil considering the current mismatch between workload and workforce in many countries. Nonetheless, multidisciplinary, and patient-centred consulting activities could move up on radiologists' agendas as soon as AI-based tools can save time in daily routine. Although there are many reasons why AI will assist and not replace radiologists as imaging experts in the future, it is important to position the next generation of European radiologists in view of this expected trend. To ensure radiologists' personal professional recognition and fulfilment in multidisciplinary environments, the focus of training should go beyond diagnostic reporting, concentrating on clinical backgrounds, specific communication skills with referrers and patients, and integration of imaging findings with those of other disciplines. Close collaboration between the European Society of Radiology (ESR) and European national radiologic societies can help to achieve these goals. Although each adequate treatment begins with a correct diagnosis, many health politicians see radiologic procedures mainly as a cost factor. Radiologic research should, therefore, increasingly investigate the imaging impact on treatment and outcome rather than focusing mainly on technical improvements and diagnostic accuracy alone.Critical relevance statement Strategies are presented to prepare for a successful future of the radiologic profession in Europe, if AI-powered tools can alleviate the current reporting overload: engaging in multidisciplinary activities (clinical and integrative diagnostics), enhancing the value and recognition of radiologists' role through clinical expertise, focusing radiological research on the impact on diagnosis and outcome, and promoting patient-centred radiology by enhancing communication skills.Key points • AI-powered tools will not replace radiologists but hold promise to reduce the current reporting burden, enabling them to reinvest liberated time in multidisciplinary clinical and patient-related tasks.• The skills and resources for these tasks should be considered when recruiting and teaching the next generation of radiologists, when organising departments and planning staffing.• Communication skills will play an increasing role in both multidisciplinary activities and patient-centred radiology.• The value and importance of a correct and integrative diagnosis and the cost of an incorrect imaging diagnosis should be emphasised when discussing with non-medical stakeholders in healthcare.• The radiologic community in Europe should start now to prepare for a bright future of the profession for the benefit of patients and medical colleagues alike.

Multimodal analysis and the oncology patient: Creating a hospital system for integrated diagnostics and discovery

We propose that an information technology and computational framework that would unify access to hospital digital information silos, and enable integration of this information using machine learning methods, would bring a new paradigm to patient management and research. This is the core principle of Integrated Diagnostics (ID): the amalgamation of multiple analytical modalities, with evolved information technology, applied to a defined patient cohort, and resulting in a synergistic effect in the clinical value of the individual diagnostic tools. This has the potential to transform the practice of personalized oncology at a time at which it is very much needed. In this article we present different models from the literature that contribute to the vision of ID and we provide published exemplars of ID tools. We briefly describe ongoing efforts within a universal healthcare system to create national clinical datasets. Following this, we argue the case to create "hospital units" to leverage this multi-modal analysis, data integration and holistic clinical decision-making. Finally, we describe the joint model created in our institutions.

Integrative diagnostics: the time is now-a report from the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Disruption vs. evolution in laboratory medicine. Current challenges and possible strategies, making laboratories and the laboratory specialist profession fit for the future

Since beginning of medical diagnostics, laboratory specialists have done an amazing job, continuously improving quality, spectrum and speed of laboratory tests, currently contributing to the majority of medical decision making. These improvements are mostly of an incremental evolutionary fashion, meaning improvements of current processes. Sometimes these evolutionary innovations are of a radical fashion, such as the invention of automated analyzers replacing manual testing or the implementation of mass spectrometry, leading to one big performance leap instead of several small ones. In few cases innovations may be of disruptive nature. In laboratory medicine this would be applicable to digitalization of medicine or the decoding of the human genetic material. Currently, laboratory medicine is again facing disruptive innovations or technologies, which need to be adapted to as soon as possible. One of the major disruptive technologies is the increasing availability and medical use of artificial intelligence. It is necessary to rethink the position of the laboratory specialist within healthcare settings and the added value he or she can provide to patient care. The future of the laboratory specialist profession is bright, as it the only medical profession comprising such vast experience in patient diagnostics. However, laboratory specialists need to develop strategies to provide this expertise, by adopting to the quickly evolving technologies and demands. This opinion paper summarizes some of the disruptive technologies as well as strategies to secure and/or improve the quality of diagnostic patient care and the laboratory specialist profession.

Design and Validation of a Custom Next-Generation Sequencing Panel in Pediatric Acute Lymphoblastic Leukemia

The molecular landscape of acute lymphoblastic leukemia (ALL) is highly heterogeneous, and genetic lesions are clinically relevant for diagnosis, risk stratification, and treatment guidance. Next-generation sequencing (NGS) has become an essential tool for clinical laboratories, where disease-targeted panels are able to capture the most relevant alterations in a cost-effective and fast way. However, comprehensive ALL panels assessing all relevant alterations are scarce. Here, we design and validate an NGS panel including single-nucleotide variants (SNVs), insertion-deletions (indels), copy number variations (CNVs), fusions, and gene expression (ALLseq). ALLseq sequencing metrics were acceptable for clinical use and showed 100% sensitivity and specificity for virtually all types of alterations. The limit of detection was established at a 2% variant allele frequency for SNVs and indels, and at a 0.5 copy number ratio for CNVs. Overall, ALLseq is able to provide clinically relevant information to more than 83% of pediatric patients, making it an attractive tool for the molecular characterization of ALL in clinical settings.

Primary spinal intramedullary anaplastic ganglioglioma in a pediatric patient

Background

Gangliogliomas (GGs) are rare tumors of the central nervous system composed of neoplastic neural and glial cells and are typically low-grade. Intramedullary spinal anaplastic GGs (AGG) are rare, poorly understood, and often aggressive tumors that can result in widespread progression along the craniospinal axis. Due to the rarity of these tumors, data are lacking to guide clinical and pathologic diagnosis and standard of care treatment. Here, we present a case of pediatric spinal AGG to provide information on our institutional approach to work-up and to highlight unique molecular pathology.

Case Description

A 13-year-old female presented with signs of spinal cord compression including right sided hyperreflexia, weakness, and enuresis. Magnetic resonance imaging (MRI) revealed a C3-C5 cystic and solid mass which was treated surgically with osteoplastic laminoplasty and tumor resection. Histopathologic diagnosis was consistent with AGG, and molecular testing identified mutations in H3F3A (K27M), TP53, and NF1. She received adjuvant radiation therapy and her neurological symptoms improved. However, at 6-month follow-up, she developed new symptoms. MRI revealed metastatic recurrence of tumor with leptomeningeal and intracranial spread.

Conclusion

Primary spinal AGGs are rare tumors, but a growing body of literature shows some trends that may improve diagnosis and management. These tumors generally present in adolescence and early adulthood with motor/sensory impairment and other spinal cord symptoms. They are most commonly treated by surgical resection but frequently recur due to their aggressive nature. Further reports of these primary spinal AGGs along with characterization of their molecular profile will be important in developing more effective treatments.

Integrative Diagnostics: The Time Is Now-A Report From the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Preanalytical quality improvement - an interdisciplinary journey, on behalf of the European Federation for Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE)

Since the beginning of laboratory medicine, the main focus was to provide high quality analytics. Over time the importance of the extra-analytical phases and their contribution to the overall quality became evident. However, as the initial preanalytical processes take place outside of the laboratory and mostly without its supervision, all professions participating in these process steps, from test selection to sample collection and transport, need to engage accordingly. Focusing solely on intra-laboratory processes will not be sufficient to achieve the best possible preanalytical quality. The Working Group for the Preanalytical Phase (WG-PRE) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has provided several recommendations, opinion papers and scientific evidence over the past years, aiming to standardize the preanalytical phase across Europe. One of its strategies to reach this goal are educational efforts. As such, the WG-PRE has organized five conferences in the past decade with the sole focus on preanalytical quality. This year's conference mainly aims to depict the views of different professions on preanalytical processes in order to acquire common ground as basis for further improvements. This article summarizes the content of this 6th preanalytical conference.

The value proposition of integrative diagnostics for (early) detection of cancer. On behalf of the EFLM interdisciplinary Task and Finish Group "CNAPS/CTC for early detection of cancer"

Disruptive imaging and laboratory technologies can improve clinical decision processes and outcomes in oncology. However, certain obstacles must be overcome before these technologies can be fully implemented as part of the standard for care. An integrative diagnostic approach represents a unique opportunity to unleash the full diagnostic potential and paves the way towards personalized cancer diagnostics. To meet this demand, an interdisciplinary Task Force of the EFLM was initiated as a consequence of an EFLM/ESR during the CELME 2019 meeting in order to evaluate the clinical value of CNAPS/CTC (circulating nucleic acids in plasma and serum/circulating tumor cells) in early detection of cancer. Here, an overview of current disruptive techniques, their clinical implications and potential value of an integrative diagnostic approach is provided. Furthermore, requirements such as the establishment of diagnostic tumor boards, development of adequate software solutions and a change of mindset towards a new generation of diagnosticians providing actionable health information are presented. This development has the potential to elevate the position and clinical recognition of diagnosticians.

Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration

Medical image interpretation is central to detecting, diagnosing, and staging cancer and many other disorders. At a time when medical imaging is being transformed by digital technologies and artificial intelligence, understanding the basic perceptual and cognitive processes underlying medical image interpretation is vital for increasing diagnosticians’ accuracy and performance, improving patient outcomes, and reducing diagnostician burn-out. Medical image perception remains substantially understudied. In September of 2019, the National Cancer Institute convened a multidisciplinary panel of radiologists and pathologists together with researchers working in medical image perception and adjacent fields of cognition and perception for the “Cognition and Medical Image Perception Think Tank.” The Think Tank’s key objectives were: to identify critical unsolved problems related to visual perception in pathology and radiology from the perspective of diagnosticians; to discuss how these clinically relevant questions could be addressed through cognitive and perception research; to identify barriers and solutions for transdisciplinary collaborations; to define ways to elevate the profile of cognition and perception research within the medical image community; to determine the greatest needs to advance medical image perception; and to outline future goals and strategies to evaluate progress. The Think Tank emphasized diagnosticians’ perspectives as the crucial starting point for medical image perception research, with diagnosticians describing their interpretation process and identifying perceptual and cognitive problems that arise. This paper reports the deliberations of the Think Tank participants to address these objectives and highlight opportunities to expand research on medical image perception.

Interferograms plotted with reference change value (RCV) may facilitate the management of hemolyzed samples

Background

The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE) have recommended an algorithm based on the reference change value (RCV) to evaluate hemolysis. We utilized this algorithm to analyze hemolysis-sensitive parameters.

Methods

Two tubes of blood were collected from each of the 10 participants, one of which was subjected to mechanical trauma while the other was centrifuged directly. Subsequently, the samples were diluted with the participant's hemolyzed sample to obtain the desired hemoglobin concentrations (0, 1, 2, 4, 6, 8, and 10 g/L). ALT, AST, K, LDH, T. Bil tests were performed using Beckman Coulter AU680 analyzer. The analytical and clinical cut-offs were based on the biological variation for the allowable imprecision and RCV. The algorithms could report the values directly below the analytical cut-off or those between the analytical and clinical cut-offs with comments. If the change was above the clinical cut-off, the test was rejected. The linear regression was used for interferograms, and the hemoglobin concentrations corresponding to cut-offs were calculated via the interferograms.

Results

The RCV was calculated as 29.6% for ALT. Therefore, ALT should be rejected in samples containing >5.9 g/L hemoglobin. The RCVs for AST, K, LDH, and T. Bil were calculated as 27.9%, 12.1%, 19.2%, and 61.2%, while the samples' hemoglobin concentrations for test rejection were 0.8, 1.6, 0.5, and 2.2 g/L, respectively.

Conclusions

Algorithms prepared with RCV could provide evidence-based results and objectively manage hemolyzed samples.

Laboratory Demand Management Strategies-An Overview

Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools.

Laboratory medicine in the COVID-19 era: six lessons for the future

The lockdown due to the coronavirus disease 2019 (COVID-19), a major healthcare challenge, is a worldwide threat to public health, social stability, and economic development. The pandemic has affected all aspects of society, dramatically changing our day-to-day lives and habits. It has also changed clinical practice, including practices of clinical laboratories. After one year, it is time to rethink what has happened, and is still happening, in order to learn lessons for the future of laboratory medicine and its professionals. While examining this issue, I was inspired by Italo Calvino's famous work, "Six memos for the next millennium".But I rearranged the Author's six memos into "Visibility, quickness, exactitude, multiplicity, lightness, consistency".

Virtual Multidisciplinary Review of a Complex Case Using a Digital Clinical Decision Support Tool to Improve Workflow Efficiency

Objective

Integration of distinct clinical perspectives in multi-disciplinary tumor board meetings is critical to determine optimal patient care. Digital tools can support the data consolidation needed for meeting preparation and data sharing during complex case reviews. In this paper, we assessed the value of a clinical decision support tool on workflow efficiency and conducting a complex case review of a dermatofibrosarcoma protuberans (DFSP) tumor.

Methods

Case presentation was performed by each unique clinical specialty that had relevant information about the patient; an oncologist, a pathologist, and a radiologist. Virtual discussion was completed online with case presentation and documentation with NAVIFY Tumor Board. Workflow efficiency assessment was done through interviews and observation of the # of steps across different team members involved in preparing and conducting cancer multidisciplinary team (MDT) meetings before and after the implementation of the NAVIFY Tumor Board solution.

Results

Case review consisted of surgical and therapeutic intervention history, distinct histological and sequencing patterns representative of DFSP, with radiological review to determine areas for surgical intervention. Consolidation of clinical input led to a recommendation of a formal external hemipelvectomy with potential chemotherapy. Workflow assessment demonstrated a 46% total reduction in the # of steps for meeting preparation (from 69 to 37), with specific changes based on role: data manager (33 to 15), pathologist (26 to 13), radiologist (no change), and logistics (5 to 4). There was a 31% total reduction in the # of steps for conducting the meeting (from 51 to 35).

Conclusion

Utilizing a digital clinical decision support tool helped to consolidate patient data and improved case presentation through workflow efficiency. This allowed for improved interdisciplinary discussion on a complex DFSP case and supported the determination of a clinical decision.

The role of laboratory hematology between technology and professionalism: the paradigm of basophil counting

Starting from the discussion topics triggered by Hoffmann about the past and current basophil counting, a broader view of the role and future of laboratory hematology, passing through some general considerations concerning the idea of laboratory medicine in the healthcare pathway between technology and professionalism, is here provided.

Integrating infection and sepsis management through holistic early warning systems and heuristic approaches: a concept proposal

This is a first attempt to integrate the three pillars of infection management: the infection prevention and control (IPC), and surveillance (IPCS), antimicrobial stewardship (AMS), and rapid identification and management of sepsis (RIMS). The new 'Sepsis-3' definition extrapolates the diagnosis of sepsis from our previously slightly naïve concept of a stepwise evolving pattern. In doing so, however, we have placed the transition from infection toward sepsis in the domain of uncertainty and time-dependency. This now demands that clinical judgment be used in the risk stratification of patients with infection, and that pragmatic local solutions be used to prompt clinicians to evaluate formally for sepsis. We feel it is necessary to stimulate the development of a new generation of concepts and models aiming at embracing uncertainty. We see the opportunity for a heuristic approach focusing on the relevant clinical predictors at hand allowing to navigate the uncertainty of infection diagnosis under time constraints. The diverse and situated clinical approaches eventually emerging need to focus on the understanding of infection as the unbalanced interactions of host, pathogen, and environment. In order extend such approach throughout the patient journey we propose a holistic early warning system underpinned by the risk-based categories of hazards and vulnerabilities iteratively fostered by the information gathered by the infection prevention control and surveillance, clinical microbiology, and clinical chemistry services.

Survey on request form content and result reporting in therapeutic drug monitoring service among laboratories in Czechia and Slovakia

Introduction

The aim of the study was to investigate current practice and policies of therapeutic drug monitoring (TDM) service requesting and result reporting in Czechia and Slovakia.

Materials and methods

All 149 laboratories that measure plasma drug concentrations were given an online questionnaire during a regular external quality assessment TDM cycle. The questionnaire consisted of 17 questions. The optimal TDM practice was defined as the application of all elements (age, body weight, time of sampling, date of the first administration, time of the last dose administration, the dose, the dosing interval, the route of administration, information on reason of testing, and information on other co–administered drugs) needed for reporting a recommendation for further drug dosing (positive response to question number 16).

Results

The response rate was 69%, 103 out of 149 laboratories measuring drug concentrations. Only 12% (12 out of 103 laboratories) of the laboratories implemented all elements needed for optimal TDM practice and reported a recommendation. Both paper and electronic request forms were used by 77 out of 103 (75%) laboratories. A total of 69 out of 103 laboratories (67%) specified the type of sampling tube on their request form. Cystatin C was used for prediction of renal drug elimination by 24% (25 out of 103) of participants.

Conclusions

Small number of laboratories implemented all elements needed for optimal TDM practice and report a recommendation on further dosing. Further efforts in education on optimal TDM practice as well as harmonization of service are desirable.

Errors within the total laboratory testing process, from test selection to medical decision-making - A review of causes, consequences, surveillance and solutions

Laboratory analyses are crucial for diagnosis, follow-up and treatment decisions. Since mistakes in every step of the total testing process may potentially affect patient safety, a broad knowledge and systematic assessment of laboratory errors is essential for future improvement. In this review, we aim to discuss the types and frequencies of potential errors in the total testing process, quality management options, as well as tentative solutions for improvement. Unlike most currently available reviews on this topic, we also include errors in test-selection, reporting and interpretation/action of test results. We believe that laboratory specialists will need to refocus on many process steps belonging to the extra-analytical phases, intensifying collaborations with clinicians and supporting test selection and interpretation. This would hopefully lead to substantial improvements in these activities, but may also bring more value to the role of laboratory specialists within the health care setting.

Precision medicine in medical oncology: hope, disappointment and reality

During the past 20 years, targeted therapy based on the understanding of tumor biology has been complementing or even replacing cytotoxic agents that have dominated pharmacotherapy of cancer since the conception of medical oncology. Unfortunately, the fact that targeted therapies with potential to induce cure or at least substantially prolong survival are still not available for many common solid tumors results in skepticism or even nihilism. On the one hand, biomarker research is not keeping pace with the introduction of new agents, while on the other hand, effective drugs are still not available for many potential molecular targets associated with malignant transformation and tumor progression. However, targeted therapies have already transformed the natural history and clinical outcomes not only in patients with rare malignancies like gastrointestinal stromal tumor but also with many common tumors, e.g. breast cancer, malignant melanoma, non-small cell lung cancer or renal cell carcinoma. For further advances, a multidisciplinary effort is indispensible that should, above all, involve the collaboration of medical oncology and laboratory medicine.

Clotting Factors in COVID-19: Epidemiological Association and Prognostic Values in Different Clinical Presentations in an Italian Cohort

Introduction: A novel highly pathogenic human coronavirus able to induce severe acute respiratory syndrome (SARS) has been recently recognized as the cause of the coronavirus disease 2019 (COVID-19) outbreak, which has spread rapidly from China to other countries. Little is known about laboratory prognostic markers in COVID-19 patients. The aim of our study was to describe the basic clotting parameters in COVID-19 patients and their prognostic role in different clinical forms of the disease. Material and Methods: We enrolled 67 COVID-19 patients admitted to the Emergency Department. A cohort of 67 age- and sex-matched non-COVID-19 patients with acute respiratory illness was used as a control group. For all patients, platelet count (PLT), prothrombin time (PT), activated thromboplastin time (aPTT), C-reactive protein (PCR), fibrinogen, and D-dimer were determined. The COVID-19 population was divided in two groups according to the presence or absence of SARS. The clotting factors values were compared between the groups. Results: At admission, the COVID-19 patients showed statistically significant increased levels of fibrinogen (601.5 (480–747) vs. 455 (352.5–588.5) mg/dL; p = 0.0000064), and a higher percentage of patients had fibrinogen levels >400 mg/dL (86% vs.58%; p = 0.0054) compared to the control group. The levels of fibrinogen were higher in COVID-19 patients with SARS compared to those without SARS (747 (600.0–834.0) vs. 567 (472.5–644.50); p = 0.0003). Conclusion: Fibrinogen seems to increase early in COVID-19 patients and may be used as a risk stratification marker for the early detection of a subgroup of COVID-19 patient at increased risk to develop SARS, who might benefit from a different and thorough clinical surveillance and treatment.