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Maria Stella Graziani

Deputy Director
Martina Zaninotto

Associate Editors
Ferruccio Ceriotti
Davide Giavarina
Bruna Lo Sasso
Giampaolo Merlini
Martina Montagnana
Andrea Mosca
Paola Pezzati
Rossella Tomaiuolo
Matteo Vidali

EIC Assistant
Francesco Busardò

International Advisory Board Khosrow Adeli Canada
Sergio Bernardini Italy
Marcello Ciaccio Italy
Eleftherios Diamandis Canada
Philippe Gillery France
Kjell Grankvist Sweden
Hans Jacobs The Netherlands
Eric Kilpatrick UK
Magdalena Krintus Poland
Giuseppe Lippi Italy
Mario Plebani Italy
Sverre Sandberg Norway
Ana-Maria Simundic Croatia
Tommaso Trenti Italy
Cas Weykamp The Netherlands
Maria Willrich USA
Paul Yip Canada

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Via L. Temolo 4, 20126 Milano

Responsible Editor
Giuseppe Agosta

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Chiara Riva
Biomedia srl
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ISSN print: 0393 – 0564
ISSN digital: 0392- 7091

BC: Articoli scritti da R. Guerranti

Tecniche di apprendimento automatico basato sui risultati di esami di medicina di laboratorio nella diagnosi e prognosi per i pazienti COVID-19: una revisione sistematica
Machine Learning based on laboratory medicine test results in diagnosis and prognosis for COVID-19 patients: a systematic review
<p>The rapid detection of SARS-CoV-2 infections is essential for both diagnostic and prognostic reasons: however, the current gold standard for COVID-19 diagnosis, that is the rRT-PCR test, is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Thus, Machine Learning (ML) based methods have recently attracted increasing interest, especially when applied to digital imaging (x-rays and CT scans).<br />In this article, we review the literature on ML-based diagnostic and prognostic methods grounding on hematochemical parameters. In doing so, we address the gap in the existing literature, which has so far neglected the application of ML to laboratory medicine. We surveyed 20 research articles, extracted from the Scopus and PubMed indexes. These studies were characterized by a large heterogeneity, in terms of considered laboratory and clinical parameters, sample size, reference population, employed ML methods and validation procedures. Most studies were found to be affected by reporting and replicability issues: among the surveyed studies, only three reported complete information regarding the analytic methods (units of measure, analyzing equipment), while nine studies reported no information at all. Furthermore, only six studies reported results on independent external validation. In light of these results, we discuss the importance of a tighter collaboration between data scientists and medicine laboratory professionals, so as to correctly characterize the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the correct interpretation of the results, and gain actual usefulness by applying ML methods in clinical practice.</p>
Biochimica Clinica ; 45(4) 348-364
Rassegne - Reviews
Introduzione ai Big Data e all’Intelligenza Artificiale in Medicina di Laboratorio
Introduction to Big Data and Artificial Intelligence in Laboratory Medicine
<p>Currently, thanks to the growing computing capacity and the increasing availability of digital data, Data Science is playing an important role in the future development of Laboratory Medicine. However, the concepts of Big Data (BD) and Artificial Intelligence (AI) can still be interpreted in various ways. Clinical laboratories are certainly among the health care organizations producing an important number of data that can be considered BD and it is certainly not a coincidence that they are among the first health organizations to have implemented computer systems within their workflows. Through a process called Data Mining it is possible to extract useful information from BD using automatic or semi-automatic methods that must be preceded by Data Cleaning in order to ensure the cleanliness and correctness of the data themself. Regarding Data Analysis, several Machine Learning or Deep Learning techniques based on different algorithms or on the functioning principle of neural networks can be used; for the development of these techniques, R and Python programming languages are really useful. Although many applications can be useful in Laboratory Medicine, there are still some obstacles to overcome, including poor harmonization of data or fragmentation of sources; moreover, the issue of data accessibility must be managed considering patient&rsquo;s privacy as a priority. Finally, there is an increase apprehension related to the awareness of the inevitable innovation in the Laboratory Medicine field in the near future, because of these new approaches. To face these challenges, it is necessary that these topics become familiar to the professionals of Laboratory Medicine. Aim of this Document is to share information about BD and AI in order to contribute to the introduction and development of these methodologies in the field of Laboratory Medicine.</p>
Biochimica Clinica ; 45(1) 057-067
Documenti - Documents
Sicurezza del paziente e rischio clinico nel processo ematologico di laboratorio
Patient safety and clinical risk in the clinical laboratory haematological process
<p>&nbsp;Patient safety, defined as the prevention of harm to patients, is the ultimate goal for medical laboratories. Risk management principles should therefore be considered an integral part of laboratory processes, especially of those activities directly impacting on patient care. This work aims to identify the most critical phases of haematological process and the risk reduction actions that improve patient safety. Risk analysis of the laboratory haematological process was carried out through Failure Mode, Effects and Analysis Criticality methodology. A form including the phases of the process, error modes and their possible effects, errors occurrence, detectability and severity scores and risk index (RI), has been prepared and sent to eight Italian laboratories. A multidisciplinary team performed the analysis in each laboratory, then two team leaders of the project comprehensively analysed the collected data. The process was divided in 8 phases (medical prescription, request acceptance, sample collection, transportation, reception and processing, results reporting and validation), 25 activities (17 pre-analytical, 4 intra-analytical, 4 post-analytical) and 43 failure modes. RI, calculated for each activity, ranged from 11 to 33. The most critical topics (RI &gt;25) were: patient identification, peripheral blood smear review, interpretative comments and report validation. Staff training plays a central role in the entire laboratory haematological process and in the phases identified as critical. An effective management related to the attainment and maintenance of skills represents the best action in order to reduce risks of adverse events for patients. The promotion of procedures aimed to harmonize the interpretative comments and peripheral blood smear review is also pivotal</p><p>&nbsp;</p>
Biochimica Clinica ; 42(4) 300-312
Contributi Scientifici - Scientific Paper
Ruolo del laboratorio nella valutazione di un donatore di organi con sospetta emofilia A
Role of the laboratory in the evaluation of an organ donor with reported haemophilia A
<p>The case concerns a 82-year-old patient, organ donor, affected by diabetes mellitus, hypertension and reported type A haemophilia, showing a traumatic severe cerebral haemorrhage. The Medical Committee started the donor evaluation process: the liver was compatible for a recipient in life-threatening conditions. Although the first level coagulation tests were within the normal range, the Regional Center for Organ and Tissue Allocation of the Tuscany Region - Italy requested further investigations in order to clarify the reported diagnosis of haemophilia and to exclude the presence of a specific FVIII inhibitor. FVII activity was evaluated to assess the protein synthesis of the liver, and FVIII for suspected haemophilia; both of them were normal. Considering the importance of the diagnosis, the parallelisms of both FVII and FVIII were performed; the tests were negative for the presence of inhibitors. Second-level tests therefore rejected the diagnosis of haemophilia and excluded the presence of a specific inhibitor of FVIII. The absence of coagulative alterations allowed the liver explant, which was successfully transplanted on a 59-year-old male recipient.</p>
Biochimica Clinica ; 42(3) e37-e39
Casi clinici - Case report
La gestione del rischio clinico in medicina di laboratorio: risultati del questionario congiunto SIBioC-Medicina di Laboratorio e Società Italiana di Ergonomia (SIE) inviato ai laboratori della Regione Toscana
Misidentification in laboratory medicine: results of the Tuscany survey of the Clinical Risk Management Study Group SIBioC and the Italian Society of Ergonomy (SIE)
<p>In the year 2016 the Study Group on Clinical Risk Management of the SIBioC-Laboratory Medicine Society issued a joint survey with the Italian Society of Ergonomics (SIE); the survey was sent to all the clinical laboratories of the Tuscany Region in Italy. This survey had the aim to understand the level of awareness of the clinical laboratory about the clinical risk management, particularly in the patient misidentification field. The results show a very variable consciousness of the problem among different laboratories, with a very multi-faced approach to this important topic. More than the 93% of the participants state that the errors on misidentification are always registered and in the 80% the consequent actions are tracked.</p>
Biochimica Clinica ; 42(2) 141-145
Contributi Scientifici - Scientific papers
Echinocitosi associata a diminuita espressione della banda 3 eritrocitaria in un bambino con epilessia idiopatica
Echinocytosis and decreased expression of erythrocyte band 3 in a child with idiopathic epilepsy: a case report
<p>Echinocytosis (EC) is a morphologic change of the erythrocytes usually linked to electrolyte exchange abnormalities, energy depletion and cell dehydration. Herein, we report a case of a child presenting with complex partial epilepsy, consistent peripheral EC, mild unexplained microcitemia and a significantly decreased expression of band 3. No pathogenic mutations were found on the band 3 encoding gene, i.e., solute carrier family 4 (anion exchanger), member 1 (SLC4A1). The observed changes in band 3 expression likely originated at the transcriptional and/or post-transcriptional level. To date, band 3 is considered as a key protein in several neurodevelopmental diseases. The described modifications probably explain the observed clinical phenotype. The likelihood that an alteration in band 3 function could contribute to an erythrocyte morphological abnormality and neurological symptoms represents a fascinating and intriguing hypothesis.</p>
Biochimica Clinica ; 40(4) e27-e30
Casi clinici - Case report
La stima dell'incertezza delle misure nel laboratorio clinico
Measurement uncertainty calculation in clinical laboratories
<p>The result of a measurement is only an estimate of&nbsp;the value of the measurand and it is complete only when accompanied by a statement of the measurement uncertainty.&nbsp;The ISO 15189 standard requires that &ldquo;the laboratory shall determine measurement uncertainty for each measurement&nbsp;procedure&rdquo;. The approach to calculate the measurement uncertainty proposed by the &ldquo;Guide to the expression of&nbsp;uncertainty in measurement&rdquo; (GUM) requires the quantification of every source of variability to sum them up for the&nbsp;final calculation (&ldquo;bottom-up&rdquo; approach). To overcome inherent difficulties in the systematic application of this approach&nbsp;in a clinical laboratory, a &quot;top-down&quot; approach is proposed, through the calculation of measurement uncertainty from&nbsp;already existing data of IQC. The proposed approach is checked by applying it to the IQC data for serum glucose and&nbsp;creatinine measurements collected from 19 clinical laboratories. Different approaches to cope with the issue of the&nbsp;estimate of systematic error (bias) are proposed, based either on value-assigned trueness control/reference materials,&nbsp;on the mean value of the employed material defined by the laboratory at the start of the IQC program or on the peer&nbsp;group mean of an interlaboratory program material. The availability of a standardized way to estimate the measurement&nbsp;uncertainty provides a tool to evaluate the analytical quality of results and it allows comparison of the quality of results&nbsp;made available by different laboratories.</p>
Biochimica Clinica ; 39(2) 108-115
Contributi scientifici - Scientific Papers
Errori di identificazione del paziente: un progetto SIBioC orientato alla gestione di un problema persistente
Wrong blood in tube: a SIBioC project for a persistent problem
A. Aita  |  A. Padoan  |  R. Guerranti  |  M. Fiorini  |  C. Bellini  |  F. Tosato  |  M. Pelloso  |  E. Piva  |  R. Pajola  |  M. Lorubbio  |  B. Cremonesi  |  A. Bassi  |  R. Rolla  |  G. Introcaso  |  M. Plebani  |  S. Buoro  |  F. Balboni  | 
<p>Introduction: recently, multi-analytes delta-check (MDC) has been proposed as a more effective tool in identification errors (IE) prevention. In this context, &ldquo;Haematology&rdquo; and &ldquo;Clinical Risk&rdquo; SIBioC working groups launched a project aiming to develop a cell blood count (CBC) MDC. This work is aimed to describe the project and some preliminary results.<br />Methods: the project consists of four phases: collection of CBC results from 15 Italian laboratories to create an original dataset (OD); pilot study on a smaller dataset (SD) i.e., creation of an artificial mix-up dataset-MD containing IE by casual resampling of the SD and identification of the best statistical model to create a MDC; identification of the most accurate MDC on OD; testing the MDC in involved labs and verification of its effectiveness.<br />Results: the SD included 2,367 pair of consecutive results for the same patient (patients&rsquo; age: 0-100 years; the majority of repetitions were within days). The SD casual resampling generated a MD with 2,000 pair of patient-mixed consecutive results. When one of the most frequent used delta-check alert (&Delta;MCV=7fL) was applied to detect IE in MD, the method accuracy was low (AUC=0.542). On the contrary, testing of a multivariate model, obtained by a stepwise logistic analysis, allowed to obtain a more accurate MDC in IE detection (AUC=0.931, sensitivity=91.6%, specificity=94%).<br />Conclusions: MDC may offer a practical strategy to identify IE prior to test reporting, improving patient safety. However a good planning of project workflow, selection of methodology, tools and staff competence are key elements to reach the objectives.</p>
Biochimica Clinica ; 17(1)
Contributi Scientifici - Scientific Papers