Abstract
On the occasion of the 60th anniversary of Clinical Chemistry and Laboratory Medicine (CCLM) we present a review of recent developments in the discipline of laboratory hematology as these are reflected by papers published in CCLM in the period 2012–2022. Since data on CCLM publications from 1963 to 2012 are also available, we were able to make a comparison between the two periods. This interestingly revealed that the share of laboratory hematology papers has steadily increased and reached now 16% of all papers published in CCLM. It also became evident that blood coagulation and fibrinolysis, erythrocytes, platelets and instrument and method evaluation constituted the ‘hottest’ topics with regard to number of publications. Some traditional, characteristic CCLM categories like reference intervals, standardization and harmonization, were more stable and probably will remain so in the future. With the advent of important newer topics, like new coagulation assays and drugs and cell population data generated by hematology analyzers, laboratory hematology is anticipated to remain a significant discipline in CCLM publications.
Introduction
When Clinical Chemistry and Laboratory Medicine (CCLM) was launched in 1963 under the name ‘Zeitschrift für Klinische Chemie’, it was entirely devoted to clinical chemistry in the strict sense. The first paper that currently is regarded as belonging to the domain of laboratory hematology appeared in 1966 [1]. With the expansion of the journal, not only the number of published papers steadily increased, also the share of papers on laboratory hematology subjects has risen, to approximately 10% in 2012 [2]. And this upward trend has continued until present, since during the 6th decade of CCLM’s existence, almost 16% of all published papers were in the domain of laboratory hematology. The journal’s 60th anniversary is a good moment to review recent developments in laboratory hematology, their impact on CCLM publications and indirectly their impact on patient care. This review is focused on a number of selected subjects, where CCLM has played a key role in developing the area of interest or which are otherwise characteristic for the journal.
Materials and methods
For the current review we have used the same definitions for laboratory hematology as in a previous overview in order to enable comparisons with the first 50 years of CCLM [2]. Included in the field are the categories given in Table 1, while excluded were papers on iron metabolism, blood gas analysis, glycated Hb, monoclonal gammopathy, vitamin B12, folate and homocysteine. Search strategy, categorization and citation analysis were also identical to the previous report [2]. Analyses included the July 2012 until the June 2022 issues of the journal.
Numbers of papers in the domain of laboratory hematology published in CCLM between 2012 and 2022, compared with the journal’s first 50 years 1963–2012.
| Category | 2012–2022 | 1963–2012 |
|---|---|---|
| Biological variability | 10 (1.7%) | 4 (1.0%) |
| Blood cell isolation | 1 (0.2%) | 2 (0.5%) |
| Blood cell morphology | 20 (3.4%) | 2 (0.5%) |
| Blood transfusion | 9 (1.5%) | 5 (1.3%) |
| Cell population data | 12 (2.0%) | – |
| Cells in body fluids and bone marrow | 12 (2.0%) | 6 (1.6%) |
| Cellular immunology | 5 (0.8%) | 4 (1.0%) |
| Coagulation and fibrinolysis | 151 (25.4%) | 91 (23.5%) |
| Covid-19 | 23 (3.9%) | – |
| Cytokines and growth factors | 0 (0.0%) | 17 (4.4%) |
| Doping | 0 (0.0%) | 7 (1.8%) |
| Eosinophils | 0 (0.0%) | 4 (1.0%) |
| Erythroblasts | 4 (0.7%) | 7 (1.8%) |
| Erythrocyte sedimentation rate | 3 (0.5%) | 11 (2.8%) |
| Erythrocytes and RBC parameters | 40 (6.7%) | 13 (3.4%) |
| Flow cytometry | 6 (1.0%) | 6 (1.6%) |
| Hemoglobin and hematocrit | 9 (1.5%) | 22 (5.7%) |
| Hemoglobinopathy | 48 (8.1%) | 20 (5.2%) |
| Infection, inflammation and sepsis | 16 (2.7%) | 9 (2.3%) |
| Instrument and method evaluation | 44 (7.4%) | 28 (7.2%) |
| Leukocytes | 21 (3.5%) | 15 (3.9%) |
| Lymphocytes | 8 (1.3%) | 3 (0.8%) |
| Molecular diagnostics | 19 (3.2%) | 19 (4.9%) |
| Platelet count and platelet function | 42 (7.1%) | 27 (7.0%) |
| Pre-analytical phase | 24 (4.0%) | 22 (5.7%) |
| Quality control | 25 (4.2%) | 17 (4.4%) |
| Reference values | 18 (3.0%) | 9 (2.3%) |
| Reticulocytes | 2 (0.3%) | 12 (3.1%) |
| Standardization, harmonization | 14 (2.4%) | 5 (1.3%) |
| Various | 8 (1.3%) | – |
| All | 594 (100.0%) | 387 (100.0%) |
Results and discussion
In its 6th decade, CCLM published 594 papers on laboratory hematology subjects (Table 1), which is over 1½ times the amount achieved in the 5 decades before (387), implying a very significant increase. As the journal published 3,733 papers over the last 10 years, also the relative amount of laboratory hematology papers rose to an impressive 15.9%. The steady increase that was observed over the first 50 years (see Figure 1 in [2]) clearly continued in the last decade. This reflects the increasing importance of laboratory hematology within the wide field of laboratory medicine. Factors that undoubtedly play a role here are the progressing level of automation, including the availability of new research parameters, a shift of molecular diagnostics from research to routine settings and, last but not least, a higher degree of professionalism among clinical laboratory specialists due to improved education and specialization in hematology. Or in other words, laboratory hematology continues maturing as a discipline, next to and separate from traditional clinical chemistry.
When comparing the subjects addressed in these two periods they show much similarity, but some striking differences can be noted (Table 1). In recent years, no papers were published on cytokines and growth factors, doping and eosinophils. New categories were cell population data (CPD) from hematology analyzers and of course Covid-19. Categories that showed an evident increase in relative occurrence were blood cell morphology, erythrocytes and hemoglobinopathy. The most popular field remained coagulation and fibrinolysis with the relative contribution more or less constant. Also the characteristic CCLM subjects like quality control and reference values appeared to be relatively stable. The most notable topics will be discussed in detail below.
Blood cell morphology
At first sight it might seem a bit remarkable that the number of papers on blood cell morphology increased, now we are living in the era of laboratory automation. Yet, this growth becomes understandable when one reads the papers and sees that many of them were initiated by an alert or abnormal scatter pattern in the hematology analyzer, which prompted further microscopic examination [3, 4]. In the past, when hematology analyzers had a lower level of technological sophistication, some of these cases undoubtedly would have gone unnoticed and never led to a publication.
Cell population data
A relatively new topic in the literature is cell population data (CPD). These are actually the raw measurement signals produced by modern hematology analyzers for every single white blood cell (WBC) analyzed. In hematology analyzers with multiple detectors, every leukocyte generates a set of signals, which the software uses for characterizing the cell. Once the various WBC populations are defined, the mean and dispersion of all signals in each cell population is calculated. Actually, a set of CPD can be regarded as the fingerprint of a patient’s WBC at a given moment and CPD harbor information that is directly or indirectly associated with cell morphology. For example, neutrophil volume is increased in case of immature granulocytes in blood, toxic granulation may increase the neutrophil side scatter signal (granularity), while viral infection can be associated with increased lymphocyte volume.
CPD were first accessible in the Beckman Coulter LH750 analyzer and some neutrophil CPD appeared to be useful indicators of acute bacterial infection [5]. Later this observation was extended to lymphocyte parameters and used for characterizing the malignant cells of chronic lymphatic leukemia and myelodysplastic syndrome [6]. Another early CCLM paper reported neutrophil CPD for defining asthma phenotypes in an Abbott hematology analyzer [7]. Over the next years, CPD were made available in more analyzer brands [8], [9], [10] and the number of disease states where neutrophil CPD are considered useful nowadays includes neonatal and adult sepsis [8, 11], [12], [13], postoperative infection [14], tuberculosis [15], myelodysplastic syndrome [16, 17], acute and chronic leukemia [18], [19], [20] and transplant engraftment [21, 22]. Lymphocyte CPD can provide additional information in viral infection [23], lymphocytosis [24], [25], [26] and tropical infections like dengue [27, 28]. The significance of monocyte CPD was boosted by the Covid-19 pandemic, as monocyte volume distribution width (MDW) was found to have prognostic value for Covid-19 severity [29], [30], [31], [32], [33] and MDW also appeared to be an early indicator of sepsis in general [32, 34], [35], [36]. From these references it is clear that CCLM played an important role in disseminating information on MDW.
In spite of the positive findings of CPD in a variety of clinical conditions, most authors seem to have overlooked the potential drawbacks of CPD and their analysis. Firstly, CPD values are sensitive to preanalytical storage conditions [37]. Further, they are specific for a certain hematology analyzer and even analyzers of the same type do show variation in CPD [38]. This is because the raw measurement signals depend on the individual optical and electronic components in the analyzer and their exact alignment in the optical bench. Thus, CPD are not only the fingerprint of a specific blood sample, but of a blood sample in combination with an individual hematology analyzer. CPD can be harmonized between different analyzers only by optical adjustments [38]. Practically all studies on the clinical utility of CPD were performed on a single hematology analyzer and this constitutes a serious limitation for applying the findings to other analyzers. Moreover, there are no data as yet on the long-term stability of CPD measured with a single or multiple analyzers. In this respect, the practical usefulness of CPD reference intervals is questionable and they need to be prudently interpreted [39, 40].
Coagulation and fibrinolysis
With approximately one quarter of all laboratory hematology papers in CCLM, this category continues to play the main role. Obviously, the subjects are now different from the past, reflecting the progress in diagnostic tests and drugs development. Major topics in the past 10 years include lupus anticoagulant and the antiphospholipid syndrome [41], [42], [43], [44], [45], congenital thrombophilia testing [46], [47], [48], [49], the new direct oral anticoagulants [50], [51], [52], [53], [54], [55], [56] and D-dimer, most recently as an indicator of the intravascular coagulation associated with severe COVID-19 disease [57], [58], [59], [60]. Apart from original research, the journal also published discussion papers that highlight pros and cons, helping the readers forming their own opinion on the subject [44], [45], [46], [47], [48].
Covid-19
Obviously, SARS-CoV-2 and Covid-19 are new topics for all biomedical journals. In a period of 2.5 years the scientific community produced an impressive total of nearly 280,000 papers on the virus and the disease (PubMed search, accessed 23 September 2022). The contribution of CCLM was quite moderate with a total of 195 papers, of which we classified 23 in the laboratory hematology domain (Table 1). Notable Covid-19 related subjects were coagulation (D-dimer, thrombin formation) and the CPD mentioned above. Despite this modest number of publications, their impact was very impressive, judging by the high citation rates of some of them (see below).
Erythrocytes: RDW
Red cell distribution width (RDW) is a quantitative measure of red blood cell (RBC) heterogeneity. The concept was developed by Cecil Price-Jones, who measured the diameter of RBC in blood smears using a calibrated eyepiece in a microscope [61]. The RBC distribution was useful to differentiate between pernicious anemia and anemia after blood loss, but his method was too cumbersome and time-consuming to gain widespread practical use. After Wallace Coulter had applied electrical impedance measurement for counting blood cells in suspension, it became possible to determine RDW, now defined as the dispersion of RBC volume. Since the 1980’s most hematology analyzers are able to report RDW, albeit that the parameter is not standardized and thus varies between analyzers and technologies [62, 63]. RDW is traditionally expressed as the coefficient of variation of the RBC volume histogram (RDW-CV), making the RDW MCV-dependent. For example, increased RDW can be obscured by increased MCV. Therefore, expressing RDW in absolute volume units (RDW-SD) is to be preferred, as it is MCV-independent. Some modern hematology analyzers do report RDW-SD along with RDW-CV and indeed, there are indications that the utility of RDW-SD exceeds that of RDW-CV [64], [65], [66], [67].
Initially RDW was used as a diagnostic tool in anemia workup and the yearly number of publications mentioning RDW was less than 300 (Figure 1). In 2007, Felker and colleagues published their paper on RDW as an independent prognostic marker in heart failure [68] and this report stimulated other researchers examining RDW as a risk indicator in a large variety of disorders. As a consequence, the number of papers on RDW has dramatically increased: in the last few years, over 1,000 papers on RDW are published annually (Figure 1). Increased RDW has been statistically associated not only with cardiovascular events, but also with cancer, acute illnesses, ischemic stroke, kidney disease, pulmonary hypertension, mortality from community-acquired pneumonia, Behçet’s disease, post-surgery mortality, thrombo-embolic disorders, migraine attacks, Covid-19 and numerous other diseases. In all these conditions RDW reflects erythropoietic dynamics in response to anemia or inflammatory stimuli [69]. So, just like the erythrocyte sedimentation rate, RDW is a fully non-specific marker of inflammation, with some prognostic value for highly diverse situations.
Number of all publications on RDW per year since 1960 (source: PubMed).
Apart from some papers on RDW as a prognostic marker, CCLM has published papers on RDW reference intervals [70], [71], [72] and biological variation [73, 74], which are important for correct interpretation.
Erythrocytes: discriminant functions in microcytic anemias
Over the years, many discriminant formulas have been developed for the differential diagnosis of microcytic anemia. Nearly all of these formulas include basic hematology parameters only, which are available on all hematology analyzers, including the less sophisticated ones. Many evaluation studies on the performance of these discriminant formulas have been published, but most of them were on small patient cohorts, not independently validated and the results are rather contradictory. We have undertaken a retrospective study using a large database of well-characterized individuals with microcytic anemia and compared the 25 most frequently used formulas [75]. We could identify three formulas with superior sensitivity and specificity: the Green & King, Jayabose and Janel 11T score, demonstrating that complex mathematical formulas are not necessary for screening purposes. Notably, all these three high-performance formulas include RDW, suggesting that this is a critical parameter for distinguishing between iron deficiency anemia and thalassemia trait. And as mentioned above, using the RDW-SD gives even better results than the RDW-CV [65, 76]. However, despite their good performance for screening, the diagnostic utility of even the best formulas remains insufficient for basing a final thalassemia diagnosis on [75].
Erythrocytes: microcytic and hypochromic cells
Fundamental research on optical characteristics of blood cells made it possible to accurately measure RBC volume and refractive index (representing cellular hemoglobin content), using flow cytometry, based on the Mie principle of light scattering [77]. The Technicon company (later Bayer, now Siemens) was the first to apply this principle in a hematology analyzer [78]. Later this or similar technology was made available in other hematology analyzers, enabling the enumeration of microcytic, macrocytic, hypochromic and hyperchromic RBC [79]. We were able to demonstrate that the ratio of microcytic and hypochromic RBC (the M/H ratio) was a highly useful tool for the differential diagnosis between iron deficiency anemia and β-thalassemia trait in persons with microcytic anemia [80, 81]. Later CCLM published a meta-analysis, confirming the superior performance of the M/H ratio for distinguishing these conditions [82].
Platelet count and platelet function: reticulated platelets
Just like reticulocytes provide information on the erythropoietic activity in bone marrow, reticulated platelets do that for megakaryopoiesis. Until the late 1990s flow cytometry was the only technique for measuring reticulated or immature platelets, obviously impeding clinical use. The TOA (now Sysmex) R-3000 reticulocyte analyzer was the first with the capacity to enumerate reticulated platelets [83]. This parameter was shown to be mainly useful for the differential diagnosis of thrombocytopenia: reduced thrombopoiesis vs. increased platelet turnover. Nowadays, reticulated or immature platelets analysis is integrated in some high-end automated hematology analyzers and patients with thrombocytopenia can benefit from their measurement [84], [85], [86]. Papers devoted to (pre-)analytical aspects of reticulated platelets are available thanks to CCLM, too [85, 87, 88].
Hemoglobinopathy
The number of CCLM papers on hemoglobinopathy has significantly increased over the last 10 years, both in absolute and relative sense (Table 1). This can be mainly attributed to two factors: the growth in use of automated HbA1c analyzers and the increase in the technological level of hematology analyzers. As a laboratory journal, CCLM has become an important forum for reporting known or newly discovered Hb variants that interfere with HbA1c measurement on one or more HbA1c analyzers, as some selected examples demonstrate [89], [90], [91], [92], [93]. Additional peaks, sometimes overlapping with the normal Hb peaks, are generally a sign that an abnormal Hb may be present and that additional investigations are necessary.
Another source of detection of Hb variants is the hematology analyzer that produces abnormal scatterplots in one of more channels. The phenomenon of an abnormal Hb variant incidentally detected in a hematology analyzer was first described in a patient with Hb-Köln, presenting as pseudo-reticulocytosis in the Abbott Cell-Dyn 4000 analyzer [94]. All other reports are more recent and CCLM has published the majority of them [95], [96], [97], [98], [99], [100], [101], including findings of Hb M Dothan [97], Hb Leiden [95, 99] and Hb Mozhaisk [100]. All but one cases were found during analysis with Sysmex XE- or XN-analyzers, presenting as lowered fluorescence intensity of the WBC in the DIFF or WDF scattergrams, respectively. Recently, the Urrechaga group reported in this journal the first case of abnormal Hb incidentally detected in a Mindray BC-6800 hematology analyzer, namely Hb Johnstown [101]. A second report in a Mindray BC-6800 identified Hb Shelby, giving comparable abnormal scattergrams [102]. These three analyzers use highly similar reagents for the WBC differential and a plausible hypothesis is that the abnormal Hb released from lysed RBC binds the fluorescent dye with high affinity, so that there is insufficient dye left for staining the nucleated cells, resulting in the low or very low fluorescence signal in the scattergrams [95], [96], [97]. Alternatively, the low signals might be explained by fluorescence quenching due to the abnormal Hb or RBC ghosts [98]. Currently, no other hematology analyzer types than the three mentioned above have been reported to be associated with incidental abnormal Hb variants, strongly suggesting that the phenomenon is reagent-specific.
Instrument and method evaluation
The relative share of CCLM publications in this category has remained stable (Table 1) and the papers do reflect the development and introduction of new analyzers by the various manufacturers. Not surprisingly, papers on hematology analyzers are the most frequent in this category, approximately 80%, and the number of papers roughly represent the market shares of the main manufacturers. Some papers are of direct practical interest for the journal’s readers: early evaluation reports of newly released analyzer models [103], [104], [105], side-by-side comparison studies of various analyzers [84, 106], [107], [108] and case reports on interfering factors that compromise the accuracy of reported results [98, 109], [110], [111], [112]. A critical paper on unreliable basophil counts reported by hematology analyzers belongs to this group, too [113]. Remarkable in this category is the relatively high number of papers of cell enumeration and differentiation in other body fluids than blood [103, 109, 114], [115], [116], [117], [118].
Next to hematology analyzers, CCLM has published papers on coagulation analyzers, both for central laboratory and point-of-care use [119], [120], [121], [122], [123], [124], analyzers for digital image analysis of blood cell smears [125], [126], [127], [128], [129] and recently also on automated analyzers for measuring erythrocyte sedimentation rate [130, 131]. The most singular paper in this category was probably the one devoted to a multicenter ektacytometer evaluation study [132]; few labs will consider this instrument for their key inventory, but the number of citations (about six per year) shows that this very analyzer attracted the interest of other researchers.
Reference intervals
Traditionally, CCLM is one of the few journals that provide ample space for papers on reference intervals of clinical laboratory measurements and the theoretical concepts behind it. Also the domain of laboratory hematology is well represented in this category, with 18 papers over the last 10 years. Whereas many papers still use CLSI (Clinical & Laboratory Standards Institute) and ISLH (International Society for Laboratory Hematology) recommended procedures for establishing reference intervals, there is growing interest in indirect methods using data mining of large datasets by mathematical techniques [72, 133], [134], [135], [136], [137]. Although indirect methods generally yield reliable data and have gained wide acceptance [138], some caveats need to be taken into account [139].
Standardization and harmonization
Papers on standardization and harmonization in laboratory hematology remain relatively scarce in comparison with clinical chemistry. Without any doubt this is due to the lack of international standards and reference materials. Yet, in the last decade CCLM published 14 papers in this category, almost double the percentage of prior years (Table 1). Coagulation assays clearly constitute the largest part [49, 140], [141], [142], [143], [144], [145], whereas initiatives in hemocytometry standardization are lagging far behind [146], [147], [148]. The recommendations by the Italian Society of Clinical Chemistry for harmonization of interpretative comments added to hematology reports form a rare, but very welcome exception that hopefully will be followed in other countries [149].
Highly cited CCLM papers in laboratory hematology
Table 2 shows the top-20 highest cited CCLM papers in the domain of laboratory hematology published during the last decade. Two publications on Covid-19 and SARS-CoV-2 stand out with 300–400 citations per year since they were printed [59, 150]; the impact and the urgency of the pandemic are doubtlessly explaining the very high citation rate. To a lesser extent this was also the case for a third Covid-19 related paper [58]. All other papers in Table 2 reached an annual citation rate between 5 and 13 and they all belong to one of the categories mentioned above. In our opinion this indicates that recent advances in laboratory hematology published in CCLM are well perceived by other researchers.
The 20 most cited CCLM papers in the domain of laboratory hematology 2012–2022 (www.scopus.com, retrieved 13-09-2022).
| Author(s) | Title | Publication year-month | Citations, n | Average citations per year, n |
|---|---|---|---|---|
| Henry et al. [150] | Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. | 2020–07 | 896 | 413.5 |
| Han et al. [59] | Prominent changes in blood coagulation of patients with SARS-CoV-2 infection | 2020–07 | 676 | 312.0 |
| Hoffmann [85] | Reticulated platelets: analytical aspects and clinical utility | 2014–08 | 105 | 13.4 |
| Lippi and Favaloro [51] | Recent guidelines and recommendations for laboratory assessment of the direct oral anticoagulants (DOACs): is there consensus? | 2015–02 | 90 | 12.3 |
| Lippi and Plebani [63] | Red blood cell distribution width (RDW) and human pathology. One size fits all. | 2014–09 | 89 | 11.5 |
| Lippi and Plebani [151] | EDTA-dependent pseudothrombocytopenia: further insights and recommendations for prevention of a clinically threatening artifact | 2012–08 | 89 | 8.9 |
| Eller et al. [56] | Dabigatran, rivaroxaban, apixaban, argatroban and fondaparinux and their effects on coagulation POC and platelet function tests | 2014–06 | 86 | 10.8 |
| Hu et al. [152] | Red blood cell distribution width is a potential prognostic index for liver disease | 2013–07 | 72 | 8.0 |
| Hoffmann et al. [70] | Effect of age and gender on reference intervals of red blood cell distribution width (RDW) and mean red cell volume (MCV) | 2015–12 | 62 | 8.8 |
| Stotz et al. [153] | The lymphocyte to monocyte ratio in peripheral blood represents a novel prognostic marker in patients with pancreatic cancer | 2015–03 | 62 | 8.5 |
| Franchini and Liumbruno [154] | ABO blood group: old dogma, new perspectives | 2013–08 | 62 | 7.0 |
| Halbmayer et al. [155] | Interference of the new oral anticoagulant dabigatran with frequently used coagulation tests | 2012–09 | 60 | 6.2 |
| Favaloro and Thachill [58] | Reporting of D-dimer data in COVID-19: some confusion and potential for misinformation | 2020–08 | 59 | 30.0 |
| Hoffmann et al. [156] | Discriminant indices for distinguishing thalassemia and iron deficiency in patients with microcytic anemia: a meta-analysis | 2015–12 | 59 | 9.0 |
| Bruegel et al. [107] | Comparison of five automated hematology analyzers in a university hospital setting: Abbott Cell-Dyn Sapphire, Beckman Coulter DxH 800, Siemens Advia 2120i, Sysmex XE-5000, and Sysmex XN-2000 | 2015–07 | 57 | 8.2 |
| Fleming et al. [103] | Validation of the body fluid module on the new Sysmex XN-1000 for counting blood cells in cerebrospinal fluid and other body fluids | 2012–10 | 55 | 5.6 |
| Shi et al. [157] | Antiphosphatidylserine/prothrombin antibodies (aPS/PT) as potential diagnostic markers and risk predictors of venous thrombosis and obstetric complications in antiphospholipid syndrome | 2018–04 | 52 | 12.0 |
| Lippi et al. [158] | Red blood cell distribution width is significantly associated with aging and gender | 2014–09 | 51 | 6.5 |
| Magrini et al. [159] | Comparison between white blood cell count, procalcitonin and C-reactive protein as diagnostic and prognostic biomarkers of infection or sepsis in patients presenting to emergency department | 2014–10 | 50 | 6.5 |
| Zierk et al. [134] | Indirect determination of pediatric blood count reference intervals | 2013–04 | 50 | 5.3 |
Conclusions
Over the last decade, CCLM has seen a steady increase of laboratory hematology publications, both in absolute and relative sense. Almost 16% of all CCLM papers now belongs to the laboratory hematology discipline. In addition, these papers are generally well received by the scientific community as indicated by their citation rates. Also, laboratory hematology papers are very likely to have contributed to the very significant rise in impact factor that CCLM recently experienced [160]. Moreover, newer topics in the domain of the discipline are emerging and are very likely to form the subject of future papers in CCLM, for example in the field of RBC parameters, hemoglobinopathy, coagulation assays and drugs and WBC cell population data generated by hematology analyzers. Taken together, this proves that the journal represents a valuable medium for dissipating information on new developments in the laboratory hematology discipline.
Acknowledgments
The authors highly appreciate the bibliographical assistance by CCLM’s journal manager, Mrs. Heike Jahnke.
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Research funding: None declared.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors stated that there are no conflicts of interest regarding the publication of this article.
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Informed consent: Not applicable.
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Ethical approval: Not applicable.
References
1. Weicker, H, Kuhn, D. Beeinflussung der Erythrozyten-Sedimentationsrate und der enzymatischen Neuraminsäure-Abspaltung durch Zusatz eines kleinmolekularen Glykoproteids [Effect on the erythrocyte sedimentation rate and the enzymatic splitting of neuraminic acid by addition of small-molecular glycoproteins]. Z Klin Chem Klin Biochem 1966;4:281–90.10.1515/cclm.1966.4.6.281Search in Google Scholar
2. Hoffmann, JJ. Laboratory hematology in the history of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2013;51:119–27. https://doi.org/10.1515/cclm-2012-0464.Search in Google Scholar PubMed
3. Robier, C, Hoefler, G, Egger, M. Simultaneous occurrence of EDTA-dependent lymphoagglutination and agglutination of myeloid cells in a patient with chronic myelomonocytic leukemia. Clin Chem Lab Med 2021;59:e458–60. https://doi.org/10.1515/cclm-2021-0616.Search in Google Scholar PubMed
4. Lucis, R, Poz, D, Poletto, M, Puzzolante, L, Sartor, A, Curcio, F. Early detection of Candida parapsilosis in peripheral blood as a result of a peripheral blood smear performed after cytographic changes on the Beckman Coulter UniCel DxH 800 hematology. Clin Chem Lab Med 2022;60:e207–9. https://doi.org/10.1515/cclm-2022-0448.Search in Google Scholar PubMed
5. Chaves, F, Tierno, B, Xu, D. Quantitative determination of neutrophil VCS parameters by the Coulter automated hematology analyzer: new and reliable indicators for acute bacterial infection. Am J Clin Pathol 2005;124:440–4. https://doi.org/10.1309/llf75w0fwqq8tcc5.Search in Google Scholar
6. Haschke-Becher, E, Vockenhuber, M, Niedetzky, P, Totzke, U, Gabriel, C. A new high-throughput screening method for the detection of chronic lymphatic leukemia and myelodysplastic syndrome. Clin Chem Lab Med 2008;46:85–8. https://doi.org/10.1515/cclm.2008.012.Search in Google Scholar
7. Velthove, KJ, Van Solinge, WW, Lammers, JWJ, Leufkens, HGM, Schweizer, RC, Bracke, M. Hematocytometry analysis as discriminative marker for asthma phenotypes. Clin Chem Lab Med 2009;47:573–8. https://doi.org/10.1515/cclm.2009.139.Search in Google Scholar
8. Park, SH, Park, CJ, Lee, BR, Nam, KS, Kim, MJ, Han, MY, et al.. Sepsis affects most routine and cell population data (CPD) obtained using the Sysmex XN-2000 blood cell analyzer: Neutrophil-related CPD NE-SFL and NE-WY provide useful information for detecting sepsis. Int J Lab Hematol 2015;37:190–8. https://doi.org/10.1111/ijlh.12261.Search in Google Scholar PubMed
9. Buoro, S, Seghezzi, M, Vavassori, M, Dominoni, P, Apassiti Esposito, S, Manenti, B, et al.. Clinical significance of cell population data (CPD) on Sysmex XN-9000 in septic patients with our without liver impairment. Ann Transl Med 2016;4:418. https://doi.org/10.21037/atm.2016.10.73.Search in Google Scholar PubMed PubMed Central
10. Urrechaga, E, Ponga, C, Fernández, M, España, PP, Haider, RZ, Aguirre, U. Diagnostic potential of leukocyte differential and cell population data in prediction of COVID-19 among related viral and bacterial infections at Emergency Department. Clin Chem Lab Med 2022;60:e104–7. https://doi.org/10.1515/cclm-2021-1309.Search in Google Scholar PubMed
11. Raimondi, F, Ferrara, T, Capasso, L, Sellitto, M, Landolfo, F, Romano, A, et al.. Automated determination of neutrophil volume as screening test for late-onset sepsis in very low birth infants. Pediatr Infect Dis J 2010;29:288. https://doi.org/10.1097/inf.0b013e3181c37fb4.Search in Google Scholar
12. Urrechaga, E, Bóveda, O, Aguirre, U. Improvement in detecting sepsis using leukocyte cell population data (CPD). Clin Chem Lab Med 2019;57:918–26. https://doi.org/10.1515/cclm-2018-0979.Search in Google Scholar PubMed
13. Urrechaga, E, Bóveda, O, Aguirre, U. Role of leucocytes cell population data in the early detection of sepsis. J Clin Pathol 2018;71:259–66. https://doi.org/10.1136/jclinpath-2017-204524.Search in Google Scholar PubMed
14. Zhu, Y, Cao, X, Chen, Y, Zhang, K, Wang, Y, Yuan, K, et al.. Neutrophil cell population data: Useful indicators for postsurgical bacterial infection. Int J Lab Hematol 2012;34:295–9. https://doi.org/10.1111/j.1751-553x.2011.01394.x.Search in Google Scholar PubMed
15. Park, J, Lee, H, Kim, YK, Kim, KH, Lee, W, Lee, KY, et al.. Automated screening for tuberculosis by multiparametric analysis of data obtained during routine complete blood count. Int J Lab Hematol 2014;36:156–64. https://doi.org/10.1111/ijlh.12148.Search in Google Scholar PubMed
16. Raess, PW, van de Geijn, GJM, Njo, TL, Klop, B, Sukhachev, D, Wertheim, G, et al.. Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters. Am J Hematol 2014;89:369–74. https://doi.org/10.1002/ajh.23643.Search in Google Scholar PubMed
17. Kim, SY, Park, Y, Kim, H, Kim, J, Kwon, GC, Koo, SH. Discriminating myelodysplastic syndrome and other myeloid malignancies from non-clonal disorders by multiparametric analysis of automated cell data. Clin Chim Acta 2018;480:56–64. https://doi.org/10.1016/j.cca.2018.01.029.Search in Google Scholar PubMed
18. Yang, JH, Kim, Y, Lim, J, Kim, M, Oh, EJ, Lee, HK, et al.. Determination of acute leukemia lineage with new morphologic parameters available in the complete blood cell count. Ann Clin Lab Sci 2014;44:19–26.Search in Google Scholar
19. Gaspar, BL, Sharma, P, Varma, N, Sukhachev, D, Bihana, I, Naseem, S, et al.. Unique characteristics of leukocyte volume, conductivity and scatter in chronic myeloid leukemia. Biomed J 2019;42:93–8.10.1016/j.bj.2018.12.004Search in Google Scholar PubMed PubMed Central
20. Virk, H, Varma, N, Naseem, S, Bihana, I, Sukhachev, D. Utility of cell population data (VCS parameters) as a rapid screening tool for Acute Myeloid Leukemia (AML) in resource-constrained laboratories. J Clin Lab Anal 2019;33:e22679. https://doi.org/10.1002/jcla.22679.Search in Google Scholar PubMed PubMed Central
21. Golubeva, V, Mikhalevich, J, Novikova, J, Tupizina, O, Trofimova, S, Zueva, Y. Novel cell population data from a haematology analyzer can predict timing and efficiency of stem cell transplantation. Transfus Apher Sci 2014;50:39–45. https://doi.org/10.1016/j.transci.2013.12.004.Search in Google Scholar PubMed
22. Kahng, J, Yahng, SA, Lee, JW, Kim, Y, Kim, M, Oh, EJ, et al.. Novel markers of early neutrophilic and monocytic engraftment after hematopoietic stem cell transplantation. Ann Lab Med 2014;34:92–7. https://doi.org/10.3343/alm.2014.34.2.92.Search in Google Scholar PubMed PubMed Central
23. Zhu, Y, Cao, X, Tao, G, Xie, W, Hu, Z, Xu, D. The lymph index: a potential hematological parameter for viral infection. Int J Infect Dis 2013;17:e490–3. https://doi.org/10.1016/j.ijid.2012.12.002.Search in Google Scholar PubMed
24. Rastogi, P, Sharma, P, Varma, N, Sukhachev, D, Kaushal, N, Bihana, I, et al.. Leukocyte cell population data for hematology analyzer-based distinction of clonal-versus-non-clonal lymphocytosis: a real-world testing experience. Ind J Hematol Blood Transf 2018;34:623–31. https://doi.org/10.1007/s12288-018-0921-5.Search in Google Scholar PubMed PubMed Central
25. Bigorra, L, Larriba, I, Gutiérrez-Gallego, R. Machine learning algorithms for accurate differential diagnosis of lymphocytosis based on cell population data. Br J Haematol 2019;184:1035–7. https://doi.org/10.1111/bjh.15230.Search in Google Scholar PubMed
26. Seghezzi, M, Previtali, G, Moioli, V, Dominoni, P, Guerra, G, Buoro, S. Clinical significance of cell population data (CPD) on Sysmex XN in the differential diagnosis of the lymphocytosis. Clin Chim Acta 2022;530:S174. https://doi.org/10.1016/j.cca.2022.04.723.Search in Google Scholar
27. Muthunatarajan, S, Basavaiah, SH, Shenoy, SM, Natarajan, A, Mithra, P, Suresh, PK, et al.. Discriminant value of automated leucocyte VCS parameters in the detection of tropical infections. J Clin Lab Anal 2021;35:e23723. https://doi.org/10.1002/jcla.23723.Search in Google Scholar PubMed PubMed Central
28. Chhabra, G, Das, B, Mishra, S, Mishra, B. Rapid screening of dengue fever using research parameters from new generation hematological analyzers. Int J Lab Hematol 2022;44:477–82. https://doi.org/10.1111/ijlh.13782.Search in Google Scholar PubMed
29. Lin, H-A, Lin, S-F, Chang, H-W, Lee, Y-J, Chen, R-J, Hou, S-K. Clinical impact of monocyte distribution width and neutrophil-to-lymphocyte ratio for distinguishing COVID-19 and influenza from other upper respiratory tract infections: a pilot study. PLoS One 2020;15:e0241262. https://doi.org/10.1371/journal.pone.0241262.Search in Google Scholar PubMed PubMed Central
30. Ognibene, A, Lorubbio, M, Magliocca, P, Tripodo, E, Vaggelli, G, Iannelli, G, et al.. Elevated monocyte distribution width in COVID-19 patients: the contribution of the novel sepsis indicator. Clin Chim Acta 2020;509:22–4. https://doi.org/10.1016/j.cca.2020.06.002.Search in Google Scholar PubMed PubMed Central
31. Martens, RJH, van Adrichem, AJ, Mattheij, NJA, Brouwer, CG, van Twist, DJL, Broerse, J, et al.. Hemocytometric characteristics of COVID-19 patients with and without cytokine storm syndrome on the Sysmex XN-10 hematology analyzer. Clin Chem Lab Med 2021;59:783–93. https://doi.org/10.1515/cclm-2020-1529.Search in Google Scholar PubMed
32. Piva, E, Zuin, J, Pelloso, M, Tosato, F, Fogar, P, Plebani, M. Monocyte distribution width (MDW) parameter as a sepsis indicator in intensive care units. Clin Chem Lab Med 2021;59:1307–14. https://doi.org/10.1515/cclm-2021-0192.Search in Google Scholar PubMed
33. Hossain, R, Ayub, S, Tarabichi, Y. Monocyte distribution width adds prognostic value in detection of COVID-19 respiratory failure. Int J Lab Hematol 2022;44:e64–6. https://doi.org/10.1111/ijlh.13712.Search in Google Scholar PubMed PubMed Central
34. Agnello, L, Iacona, A, Lo Sasso, B, Scazzone, C, Pantuso, M, Giglio, RV, et al.. A new tool for sepsis screening in the Emergency Department. Clin Chem Lab Med 2021;59:1600–5. https://doi.org/10.1515/cclm-2021-0208.Search in Google Scholar PubMed
35. Agnello, L, Vidali, M, Lo Sasso, B, Giglio, RV, Gambino, CM, Scazzone, C, et al.. Monocyte distribution width (MDW) as a screening tool for early detecting sepsis: a systematic review and meta-analysis. Clin Chem Lab Med 2022;60:786–92. https://doi.org/10.1515/cclm-2021-1331.Search in Google Scholar PubMed
36. Poz, D, Crobu, D, Sukhacheva, E, Rocchi, MBL, Anelli, MC, Curcio, F. Monocyte distribution width (MDW): a useful biomarker to improve sepsis management in Emergency Department. Clin Chem Lab Med 2022;60:433–40. https://doi.org/10.1515/cclm-2021-0875.Search in Google Scholar PubMed
37. Sun, T, Li, J, Wu, B, Huang, H, Luo, J, Xu, D, et al.. Effects of blood storage on cell population data. Clin Lab 2020;66:1501–8. https://doi.org/10.7754/clin.lab.2020.191135.Search in Google Scholar
38. Seghezzi, M, Buoro, S, Previtali, G, Moioli, V, Manenti, B, Simon-Lopez, R, et al.. A preliminary proposal for quality control assessment and harmonization of leukocytes morphology-structural parameters (cell population data parameters). J Med Biochem 2018;37:1–13. https://doi.org/10.2478/jomb-2018-0005.Search in Google Scholar PubMed PubMed Central
39. Park, SH, Park, CJ, Lee, BR, Kim, MJ, Han, MY, Cho, YU, et al.. Establishment of age-and gender-specific reference ranges for 36 routine and 57 cell population data items in a new automated blood cell analyzer, sysmex XN-2000. Ann Lab Med 2016;36:244–9. https://doi.org/10.3343/alm.2016.36.3.244.Search in Google Scholar PubMed PubMed Central
40. van Pelt, JL, Klatte, S, Hwandih, T, Barcaru, A, Riphagen, IJ, Linssen, J, et al.. Reference intervals for Sysmex XN hematological parameters as assessed in the Dutch Lifelines cohort. Clin Chem Lab Med 2022;60:907–20. https://doi.org/10.1515/cclm-2022-0094.Search in Google Scholar PubMed
41. Froom, P, Barak, M. Testing for lupus anticoagulants – fresh or frozen? Clin Chem Lab Med 2012;50:1607–9. https://doi.org/10.1515/cclm-2011-0961.Search in Google Scholar PubMed
42. Depreter, B, Devreese, KM. Differences in lupus anticoagulant final conclusion through clotting time or Rosner index for mixing test interpretation. Clin Chem Lab Med 2016;54:1511–6.10.1515/cclm-2015-0978Search in Google Scholar PubMed
43. Depreter, B, Devreese, KM. Dilute Russell’s viper venom time reagents in lupus anticoagulant testing: a well-considered choice. Clin Chem Lab Med 2017;55:91–101. https://doi.org/10.1515/cclm-2016-0245.Search in Google Scholar PubMed
44. Favaloro, E. Mixing studies for lupus anticoagulant: mostly yes, sometimes no. Clin Chem Lab Med 2020;58:487–91. https://doi.org/10.1515/cclm-2019-1240.Search in Google Scholar PubMed
45. Moore, GW. Mixing studies for lupus anticoagulant: mostly no, sometimes yes. Clin Chem Lab Med 2020;58:492–5. https://doi.org/10.1515/cclm-2019-1248.Search in Google Scholar PubMed
46. Favaloro, EJ. The futility of thrombophilia testing. Clin Chem Lab Med 2014;52:499–503. https://doi.org/10.1515/cclm-2013-0560.Search in Google Scholar PubMed
47. Franchini, M. The utility of thrombophilia testing. Clin Chem Lab Med 2014;52:495–7. https://doi.org/10.1515/cclm-2013-0559.Search in Google Scholar PubMed
48. Lippi, G. Thrombophilia testing. Useful or hype? Clin Chem Lab Med 2014;52:467–9. https://doi.org/10.1515/cclm-2013-0561.Search in Google Scholar PubMed
49. Favaloro, EJ, Mohammed, S, Vong, R, Chapman, K, Swanepoel, P, Kershaw, G, et al.. A multi-laboratory assessment of congenital thrombophilia assays performed on the ACL TOP 50 family for harmonisation of thrombophilia testing in a large laboratory network. Clin Chem Lab Med 2021;59:1709–18. https://doi.org/10.1515/cclm-2021-0499.Search in Google Scholar PubMed
50. Tripodi, A, Di Iorio, G, Lippi, G, Testa, S, Manotti, C. Position paper on laboratory testing for patients taking new oral anticoagulants. Consensus document of FCSA, SIMeL, SIBioC and CISMEL1. Clin Chem Lab Med 2012;50:2137–40. https://doi.org/10.1515/cclm-2012-0327.Search in Google Scholar PubMed
51. Lippi, G, Favaloro, EJ. Recent guidelines and recommendations for laboratory assessment of the direct oral anticoagulants (DOACs): is there consensus? Clin Chem Lab Med 2015;53:185–97. https://doi.org/10.1515/cclm-2014-0767.Search in Google Scholar PubMed
52. Tripodi, A, Padovan, L, Testa, S, Legnani, C, Chantarangkul, V, Scalambrino, E, et al.. How the direct oral anticoagulant apixaban affects hemostatic parameters. Results of a multicenter multiplatform study. Clin Chem Lab Med 2015;53:265–73. https://doi.org/10.1515/cclm-2014-0531.Search in Google Scholar PubMed
53. Exner, T, Ahuja, M, Ellwood, L. Effect of an activated charcoal product (DOAC Stop) intended for extracting DOACs on various other APTT-prolonging anticoagulants. Clin Chem Lab Med 2019;57:690–6. https://doi.org/10.1515/cclm-2018-0967.Search in Google Scholar PubMed
54. Zabczyk, M, Kopytek, M, Natorska, J, Undas, A. The effect of DOAC-Stop on lupus anticoagulant testing in plasma samples of venous thromboembolism patients receiving direct oral anticoagulants. Clin Chem Lab Med 2019;57:1374–81. https://doi.org/10.1515/cclm-2018-1197.Search in Google Scholar PubMed
55. Kopytek, M, Zabczyk, M, Malinowski, KP, Undas, A, Natorska, J. DOAC-Remove abolishes the effect of direct oral anticoagulants on activated protein C resistance testing in real-life venous thromboembolism patients. Clin Chem Lab Med 2020;58:430–7. https://doi.org/10.1515/cclm-2019-0650.Search in Google Scholar PubMed
56. Eller, T, Busse, J, Dittrich, M, Flieder, T, Alban, S, Knabbe, C, et al.. Dabigatran, rivaroxaban, apixaban, argatroban and fondaparinux and their effects on coagulation POC and platelet function tests. Clin Chem Lab Med 2014;52:835–44. https://doi.org/10.1515/cclm-2013-0936.Search in Google Scholar PubMed
57. Lippi, G, Cervellin, G, Casagranda, I, Morelli, B, Testa, S, Tripodi, A. D-dimer testing for suspected venous thromboembolism in the emergency department. Consensus document of AcEMC, CISMEL, SIBioC, and SIMeL. Clin Chem Lab Med 2014;52:621–8. https://doi.org/10.1515/cclm-2013-0706.Search in Google Scholar PubMed
58. Favaloro, EJ, Thachil, J. Reporting of D-dimer data in COVID-19: some confusion and potential for misinformation. Clin Chem Lab Med 2020;58:1191–9. https://doi.org/10.1515/cclm-2020-0573.Search in Google Scholar PubMed
59. Han, H, Yang, L, Liu, R, Liu, F, Wu, KL, Li, J, et al.. Prominent changes in blood coagulation of patients with SARS-CoV-2 infection. Clin Chem Lab Med 2020;58:1116–20. https://doi.org/10.1515/cclm-2020-0188.Search in Google Scholar PubMed
60. Harenberg, J, Favaloro, E. COVID-19: progression of disease and intravascular coagulation – present status and future perspectives. Clin Chem Lab Med 2020;58:1029–36. https://doi.org/10.1515/cclm-2020-0502.Search in Google Scholar PubMed
61. Price-Jones, C. The diameters of red cells in pernicious anæmia and in anæmia following hæmorrhage. J Pathol Bacteriol 1922;25:487–504. https://doi.org/10.1002/path.1700250410.Search in Google Scholar
62. Lippi, G, Pavesi, F, Bardi, M, Pipitone, S. Lack of harmonization of red blood cell distribution width (RDW). Evaluation of four hematological analyzers. Clin Biochem 2014;47:1100–3. https://doi.org/10.1016/j.clinbiochem.2014.06.003.Search in Google Scholar PubMed
63. Lippi, G, Plebani, M. Red blood cell distribution width (RDW) and human pathology. One size fits all. Clin Chem Lab Med 2014;52:1247–9. https://doi.org/10.1515/cclm-2014-0585.Search in Google Scholar PubMed
64. Kai, Y, Ying, P, Bo, Y, Furong, Y, Jin, C, Juanjuan, F, et al.. Red blood cell distribution width-standard deviation but not red blood cell distribution width-coefficient of variation as a potential index for the diagnosis of iron-deficiency anemia in mid-pregnancy women. Open Life Sci 2021;16:1213–8. https://doi.org/10.1515/biol-2021-0120.Search in Google Scholar PubMed PubMed Central
65. Hoffmann, JJML, Urrechaga, E. Role of RDW in mathematical formulas aiding the differential diagnosis of microcytic anemia. Scand J Clin Lab Invest 2020;80:464–9. https://doi.org/10.1080/00365513.2020.1774800.Search in Google Scholar PubMed
66. Helleman, PW, Bartels, PC, van Waveren Hogervorst, GD. Screening for thalassaemia using the width of the Technicon H6000/H601 erythrocyte size histograms. Scand J Clin Lab Invest 1988;48:697–704. https://doi.org/10.3109/00365518809085793.Search in Google Scholar
67. Lin, CK, Lin, JS, Chen, SY, Jiang, ML, Chiu, CF. Comparison of hemoglobin and red blood cell distribution width in the differential diagnosis of microcytic anemia. Arch Pathol Lab Med 1992;116:1030–2.Search in Google Scholar
68. Felker, GM, Allen, LA, Pocock, SJ, Shaw, LK, McMurray, JJ, Pfeffer, MA, et al.. Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank. J Am Coll Cardiol 2007;50:40–7. https://doi.org/10.1016/j.jacc.2007.02.067.Search in Google Scholar PubMed
69. Higgins, JM. Red blood cell population dynamics. Clin Lab Med 2015;35:43–57. https://doi.org/10.1016/j.cll.2014.10.002.Search in Google Scholar PubMed PubMed Central
70. Hoffmann, JJ, Nabbe, KC, van den Broek, NM. Effect of age and gender on reference intervals of red blood cell distribution width (RDW) and mean red cell volume (MCV). Clin Chem Lab Med 2015;53:2015–9. https://doi.org/10.1515/cclm-2015-0155.Search in Google Scholar PubMed
71. Lippi, G, Salvagno, GL, Montagnana, M, Danese, E, Guidi, GC. Birth season predicts the values of red blood cell distribution width (RDW) in adulthood. Clin Chem Lab Med 2016;54:667–71. https://doi.org/10.1515/cclm-2015-0829.Search in Google Scholar PubMed
72. Mrosewski, I, Dahn, T, Hehde, J, Kalinowski, E, Lindner, I, Meyer, TM, et al.. Indirectly determined hematology reference intervals for pediatric patients in Berlin and Brandenburg. Clin Chem Lab Med 2022;60:408–32. https://doi.org/10.1515/cclm-2021-0853.Search in Google Scholar PubMed
73. Hilderink, JM, Klinkenberg, LJJ, Aakre, KM, de Wit, NCJ, Henskens, YMC, van der Linden, N, et al.. Within-day biological variation and hour-to-hour reference change values for hematological parameters. Clin Chem Lab Med 2017;55:1013–24. https://doi.org/10.1515/cclm-2016-0716.Search in Google Scholar PubMed
74. Buoro, S, Carobene, A, Seghezzi, M, Manenti, B, Dominoni, P, Pacioni, A, et al.. Short- and medium-term biological variation estimates of red blood cell and reticulocyte parameters in healthy subjects. Clin Chem Lab Med 2018;56:954–63. https://doi.org/10.1515/cclm-2017-0902.Search in Google Scholar PubMed
75. Urrechaga, E, Hoffmann, JJML. Critical appraisal of discriminant formulas for distinguishing thalassemia from iron deficiency in patients with microcytic anemia. Clin Chem Lab Med 2017;55:1582–91. https://doi.org/10.1515/cclm-2016-0856.Search in Google Scholar PubMed
76. Hoffmann, JJML, Urrechaga, E. Red blood cell distribution width has higher diagnostic performance in microcytic anemia when expressed in “absolute” units. Int J Lab Hematol 2020;42:e14–6. https://doi.org/10.1111/ijlh.13072.Search in Google Scholar PubMed
77. Kim, YR, Ornstein, L. Isovolumetric sphering of erythrocytes for more accurate and precise cell volume measurement by flow cytometry. Cytometry 1983;3:419–27. https://doi.org/10.1002/cyto.990030606.Search in Google Scholar PubMed
78. Smeets, EHJ, van Wersch, JW. Performance of a three-part Dif Impedance Cytometer (Sysmex E-4000) in comparison with a Cytochemical Cytometer (Technicon H 6000)). J Clin Chem Clin Biochem 1988;26:531–40. https://doi.org/10.1515/cclm.1988.26.8.531.Search in Google Scholar PubMed
79. d’Onofrio, G, Chirillo, R, Zini, G, Caenaro, G, Tommasi, M, Micciulli, G. Simultaneous measurement of reticulocyte and red blood cell indices in healthy subjects and patients with microcytic and macrocytic anemia. Blood 1995;85:818–23. https://doi.org/10.1182/blood.v85.3.818.bloodjournal853818.Search in Google Scholar
80. Urrechaga, E. Discriminant value of % microcytic/% hypochromic ratio in the differential diagnosis of microcytic anemia. Clin Chem Lab Med 2008;46:1752–8. https://doi.org/10.1515/cclm.2008.355.Search in Google Scholar
81. Urrechaga, E, Hoffmann, JJ, Izquierdo, S, Escanero, JF. Differential diagnosis of microcytic anemia: the role of microcytic and hypochromic erythrocytes. Int J Lab Hematol 2015;37:334–40. https://doi.org/10.1111/ijlh.12290.Search in Google Scholar PubMed
82. Hoffmann, JJML, Urrechaga, E, Aguirre, U. Discriminant indices for distinguishing thalassemia and iron deficiency in patients with microcytic anemia: a meta-analysis. Clin Chem Lab Med 2015;53:1883–94. https://doi.org/10.1515/cclm-2015-0179.Search in Google Scholar PubMed
83. Watanabe, K, Takeuchi, K, Kawai, Y, Ikeda, Y, Kubota, F, Nakamoto, H. Automated measurement of reticulated platelets in estimating thrombopoiesis. Eur J Hematol 1995;54:163–71. https://doi.org/10.1111/j.1600-0609.1995.tb00209.x.Search in Google Scholar PubMed
84. Meintker, L, Haimerl, M, Ringwald, J, Krause, SW. Measurement of immature platelets with Abbott CD-Sapphire and Sysmex XE-5000 in haematology and oncology patients. Clin Chem Lab Med 2013;51:2125–31. https://doi.org/10.1515/cclm-2013-0252.Search in Google Scholar PubMed
85. Hoffmann, JJ. Reticulated platelets: analytical aspects and clinical utility. Clin Chem Lab Med 2014;52:1107–17. https://doi.org/10.1515/cclm-2014-0165.Search in Google Scholar PubMed
86. Larruzea Ibarra, A, Munoz Marin, L, Perea Duran, G, Torra Puig, M. Evaluation of immature platelet fraction in patients with myelodysplastic syndromes. Association with poor prognosis factors. Clin Chem Lab Med 2019;57:e128–30. https://doi.org/10.1515/cclm-2018-0784.Search in Google Scholar PubMed
87. Ko, YJ, Hur, M, Kim, H, Choi, SG, Moon, HW, Yun, YM. Reference interval for immature platelet fraction on Sysmex XN hematology analyzer: a comparison study with Sysmex XE-2100. Clin Chem Lab Med 2015;53:1091–7. https://doi.org/10.1515/cclm-2014-0839.Search in Google Scholar PubMed
88. Söderström, AC, Nybo, M, Nielsen, C, Vinholt, PJ. The effect of centrifugation speed and time on pre-analytical platelet activation. Clin Chem Lab Med 2016;54:1913–20. https://doi.org/10.1515/cclm-2016-0079.Search in Google Scholar PubMed
89. Bots, M, Stroobants, AK, Delzenne, B, Soeters, MR, de Vries, JE, Weykamp, CW, et al.. Two novel haemoglobin variants that affect haemoglobin A1c measurement by ion-exchange chromatography. Clin Chem Lab Med 2015;53:1465–71. https://doi.org/10.1515/cclm-2015-0054.Search in Google Scholar PubMed
90. Ji, L, Yu, J, Zhou, Y, Xia, Y, Xu, A, Li, W, et al.. Erroneous HbA1c measurements in the presence of β-thalassemia and common Chinese hemoglobin variants. Clin Chem Lab Med 2015;53:1451–8. https://doi.org/10.1515/cclm-2014-0598.Search in Google Scholar PubMed
91. Kieffer, DM, Harteveld, CL, Lee, DH, Schiemsky, T, Desmet, KJ, Gillard, P. Hemoglobin A2-Leuven (alpha2delta2 143(H21) His>Asp): a novel delta-chain variant potentially interfering in hemoglobin A1c measurement using cation exchange HPLC. Clin Chem Lab Med 2016;54:e161–3.10.1515/cclm-2015-0718Search in Google Scholar PubMed
92. Yun, YM, Ji, M, Ko, DH, Chun, S, Kwon, GC, Lee, K, et al.. Hb variants in Korea: effect on HbA1c using five routine methods. Clin Chem Lab Med 2017;55:1234–42. https://doi.org/10.1515/cclm-2016-0865.Search in Google Scholar PubMed
93. Xu, A, Chen, W, Xie, W, Wang, Y, Ji, L. Hemoglobin variants in southern China: results obtained during the measurement of glycated hemoglobin in a large population. Clin Chem Lab Med 2020;59:227–32. https://doi.org/10.1515/cclm-2020-0767.Search in Google Scholar PubMed
94. Sato, S, Hirayama, K, Koyama, A, Harano, T, Nagasawa, T, Ninomiya, H. Pseudoreticulocytosis in a patient with hemoglobin Köln due to autofluorescent erythrocytes enumerated as reticulocytes by the Cell-Dyn 4000. Lab Hematol 2004;10:65–7. https://doi.org/10.1532/lh96.04011.Search in Google Scholar
95. Rosetti, M, Poletti, G, Sensi, A, Ravani, A, Rondoni, M, Baldrati, L, et al.. A rare case of Hemoglobin Leiden interfering with the DIFF channel of Sysmex XE-2100. Scand J Clin Lab Invest 2015;75:436–7. https://doi.org/10.3109/00365513.2015.1033743.Search in Google Scholar PubMed
96. Rosetti, M, Poletti, G, Ivaldi, G, Rondoni, M, Baldrati, L, Dorizzi, RM. Serendipitous detection of Hemoglobin G-Ferrara variant with Sysmex DIFF channel. Clin Biochem 2016;49:192–3. https://doi.org/10.1016/j.clinbiochem.2015.09.009.Search in Google Scholar PubMed
97. Mongelli, F, Barberio, G, Ivaldi, G. A rare and unstable hemoglobin variant, Hb M Dothan beta 25/26 (-GTG), detected by the anomalous cytogram on Sysmex XE-2100. Clin Chem Lab Med 2016;54:e31–3. https://doi.org/10.1515/cclm-2015-0566.Search in Google Scholar PubMed
98. Teixeira, C, Pina, D, Freitas, MI. Automated detection of unstable hemoglobin variants by Sysmex XE-Series analyzers. Clin Chem Lab Med 2017;55:e243–6. https://doi.org/10.1515/cclm-2017-0231.Search in Google Scholar PubMed
99. Jongbloed, W, van Twillert, G, Schoorl, M, Schindhelm, RK. Unstable haemoglobin variant Hb Leiden is detected on Sysmex XN-Series analysers. Clin Chem Lab Med 2018;56:e249–50. https://doi.org/10.1515/cclm-2017-1171.Search in Google Scholar PubMed
100. Moioli, V, Seghezzi, M, Previtali, G, Baigorria, MDC, Dominoni, P, Michetti, L, et al.. Mozhaisk haemoglobin variant effects on leukocyte differential channel using the Sysmex XN series. Clin Chem Lab Med 2019;57:e324–7. https://doi.org/10.1515/cclm-2019-0373.Search in Google Scholar PubMed
101. Urrechaga, E, Fernandez, M, Orbe, RD. Hb Johnstown is detected on Mindray BC 6800 plus analyzer. Clin Chem Lab Med 2021;59:e386–8. https://doi.org/10.1515/cclm-2021-0314.Search in Google Scholar PubMed
102. Urrechaga, E, Merino, M, Fernández, M, Aguirrezabal, J, del Orbe, R. Hemoglobin Shelby interfered with the leukocyte differential channel of the Mindray BC-6800Plus hematology analyzer: a case report. J Lab Prec Med 2022;7. https://doi.org/10.21037/jlpm-21-56.Search in Google Scholar
103. Fleming, C, Brouwer, R, Lindemans, J, de Jonge, R. Validation of the body fluid module on the new Sysmex XN-1000 for counting blood cells in cerebrospinal fluid and other body fluids. Clin Chem Lab Med 2012;50:1791–8. https://doi.org/10.1515/cclm-2011-0927.Search in Google Scholar PubMed
104. Lippi, G, Cattabiani, C, Bonomini, S, Bardi, M, Pipitone, S, Aversa, F. Preliminary evaluation of complete blood cell count on Mindray BC-6800. Clin Chem Lab Med 2013;51:e65–7. https://doi.org/10.1515/cclm-2012-0620.Search in Google Scholar PubMed
105. Slim, CL, Wevers, BA, Demmers, M, Lakos, G, Hoffmann, J, Adriaansen, HJ, et al.. Multicenter performance evaluation of the Abbott Alinity hq hematology analyzer. Clin Chem Lab Med 2019;57:1988–98. https://doi.org/10.1515/cclm-2019-0155.Search in Google Scholar PubMed
106. Pipitone, S, Pavesi, F, Testa, B, Bardi, M, Perri, GB, Gennari, D, et al.. Evaluation of automated nucleated red blood cells counting on Sysmex XE5000 and Siemens ADVIA 2120. Clin Chem Lab Med 2012;50:1857–9. https://doi.org/10.1515/cclm-2012-0148.Search in Google Scholar PubMed
107. Bruegel, M, Nagel, D, Funk, M, Fuhrmann, P, Zander, J, Teupser, D. Comparison of five automated hematology analyzers in a university hospital setting: Abbott Cell-dyn sapphire, Beckman Coulter DxH 800, Siemens Advia 2120i, Sysmex XE-5000, and Sysmex XN-2000. Clin Chem Lab Med 2015;53:1057–71. https://doi.org/10.1515/cclm-2014-0945.Search in Google Scholar PubMed
108. Pipitone, S, Buonocore, R, Gennari, D, Lippi, G. Comparison of nucleated red blood cell count with four commercial hematological analyzers. Clin Chem Lab Med 2015;53:e315–8. https://doi.org/10.1515/cclm-2014-1211.Search in Google Scholar PubMed
109. Kyle, PB, Lawrence, TJ. Aberrant lamellar body counts noted on the Beckman Coulter Unicel DxH 800. Clin Chem Lab Med 2012;50:1631–3. https://doi.org/10.1515/cclm-2012-0026.Search in Google Scholar PubMed
110. Fleming, C, de Bruin, M, Russcher, H, Lindemans, J, de Jonge, R. Liposomal interference on Sysmex XN-series body fluid mode. Clin Chem Lab Med 2016;54:e19–23. https://doi.org/10.1515/cclm-2015-0441.Search in Google Scholar PubMed
111. La Gioia, A, Bombara, M, Fiorini, F, Dell’Amico, M, Devito, A, Isola, P, et al.. Earlier detection of sepsis by Candida parapsilosis using three-dimensional cytographic anomalies on the Mindray BC-6800 hematological analyzer. Clin Chem Lab Med 2016;54:e239–42. https://doi.org/10.1515/cclm-2015-1120.Search in Google Scholar PubMed
112. Seghezzi, M, Manenti, B, Previtali, G, Gianatti, A, Dominoni, P, Buoro, S. A specific abnormal scattergram of peripheral blood leukocytes that may suggest hairy cell leukemia. Clin Chem Lab Med 2018;56:e108–11. https://doi.org/10.1515/cclm-2017-0763.Search in Google Scholar PubMed
113. Hoffmann, JJML. Basophil counting in hematology analyzers: time to discontinue? Clin Chem Lab Med 2021;59:813–20. https://doi.org/10.1515/cclm-2020-1528.Search in Google Scholar PubMed
114. Fleming, C, Brouwer, R, Lindemans, J, de Jonge, R. Improved software on the Sysmex XE-5000 BF mode for counting leukocytes in cerebrospinal fluid. Clin Chem Lab Med 2013;51:e61–3. https://doi.org/10.1515/cclm-2012-0461.Search in Google Scholar PubMed
115. Keuren, JF, Hoffmann, JJ, Leers, MP. Analysis of serous body fluids using the CELL-DYN Sapphire hematology analyzer. Clin Chem Lab Med 2013;51:1285–90. https://doi.org/10.1515/cclm-2012-0549.Search in Google Scholar PubMed
116. Zhao, Z, Gronowski, AM, Beaudoin, DR. Discrepancy in lamellar body counts (LBCs) between the Sysmex XE-2100 and Sysmex XT-2000i instruments. Clin Chem Lab Med 2013;51:e57–9. https://doi.org/10.1515/cclm-2012-0405.Search in Google Scholar PubMed
117. Buoro, S, Seghezzi, M, Mecca, T, Vavassori, M, Crippa, A, La Gioia, A. Evaluation of Mindray BC-6800 body fluid mode for automated cerebrospinal fluid cell counting. Clin Chem Lab Med 2016;54:1799–810. https://doi.org/10.1515/cclm-2015-1092.Search in Google Scholar PubMed
118. Wong-Arteta, J, Merino, A, Torrente, S, Banales, JM, Bujanda, L. High fluorescence cell count in ascitic body fluids for carcinomatosis screening. Clin Chem Lab Med 2018;56:272–4. https://doi.org/10.1515/cclm-2018-0359.Search in Google Scholar PubMed
119. Ratzinger, F, Schmetterer, KG, Haslacher, H, Perkmann, T, Belik, S, Quehenberger, P. Evaluation of the automated coagulation analyzer CS-5100 and its utility in high throughput laboratories. Clin Chem Lab Med 2014;52:1193–202. https://doi.org/10.1515/cclm-2013-1094.Search in Google Scholar PubMed
120. Kemna, E, Jentink, W, te Molder, I, Straalman, I. International Normalized Ratio (INR) testing: analytical and clinical performance of four point-of-care devices versus central laboratory instrumentation analysis. Clin Chem Lab Med 2016;54:e89–92. https://doi.org/10.1515/cclm-2015-0534.Search in Google Scholar PubMed
121. Pruller, F, Munch, A, Preininger, A, Raggam, RB, Grinschgl, Y, Krumnikl, J, et al.. Comparison of functional fibrinogen (FF/CFF) and FIBTEM in surgical patients – a retrospective study. Clin Chem Lab Med 2016;54:453–8. https://doi.org/10.1515/cclm-2015-0345.Search in Google Scholar PubMed
122. Kemna, EW, Kuipers, C, Oude Luttikhuis-Spanjer, AM, Majoor, S, Boudrie, R, Speekenbrink, RG, et al.. A two site comparison of two point-of-care activated clotting time systems. Clin Chem Lab Med 2017;55:e13–6. https://doi.org/10.1515/cclm-2016-0317.Search in Google Scholar PubMed
123. Leon-Justel, A, Alvarez-Rios, AI, Noval-Padillo, JA, Gomez-Bravo, MA, Porras, M, Gomez-Sosa, L, et al.. Point-of-care haemostasis monitoring during liver transplantation is cost effective. Clin Chem Lab Med 2019;57:883–90. https://doi.org/10.1515/cclm-2018-0889.Search in Google Scholar PubMed
124. Valsami, S, Kollia, M, Mougiou, V, Sokou, R, Isaakidou, E, Boutsikou, M, et al.. Evaluation of PFA-100 closure times in cord blood samples of healthy term and preterm neonates. Clin Chem Lab Med 2020;58:e113–6. https://doi.org/10.1515/cclm-2019-0948.Search in Google Scholar PubMed
125. Tabe, Y, Yamamoto, T, Maenou, I, Nakai, R, Idei, M, Horii, T, et al.. Performance evaluation of the digital cell imaging analyzer DI-60 integrated into the fully automated Sysmex XN hematology analyzer system. Clin Chem Lab Med 2015;53:281–9. https://doi.org/10.1515/cclm-2014-0445.Search in Google Scholar PubMed
126. Horiuchi, Y, Tabe, Y, Kasuga, K, Maenou, I, Idei, M, Horii, T, et al.. The efficacy of an internet-based e-learning system using the CellaVision Competency Software for continuing professional development. Clin Chem Lab Med 2016;54:e127–31. https://doi.org/10.1515/cclm-2015-0641.Search in Google Scholar PubMed
127. Kim, HN, Hur, M, Kim, H, Kim, SW, Moon, HW, Yun, YM. Performance of automated digital cell imaging analyzer Sysmex DI-60. Clin Chem Lab Med 2017;56:94–102. https://doi.org/10.1515/cclm-2017-0132.Search in Google Scholar PubMed
128. Kim, HN, Hur, M, Kim, H, Park, M, Kim, SW, Moon, HW, et al.. Comparison of three staining methods in the automated digital cell imaging analyzer Sysmex DI-60. Clin Chem Lab Med 2018;56:e280–3. https://doi.org/10.1515/cclm-2018-0539.Search in Google Scholar PubMed
129. Yoon, S, Hur, M, Park, M, Kim, H, Kim, SW, Lee, T-H, et al.. Performance of digital morphology analyzer Vision Pro on white blood cell differentials. Clin Chem Lab Med 2021;59:1099–106. https://doi.org/10.1515/cclm-2020-1701.Search in Google Scholar PubMed
130. Lapic, I, Piva, E, Spolaore, F, Tosato, F, Pelloso, M, Plebani, M. Automated measurement of the erythrocyte sedimentation rate: method validation and comparison. Clin Chem Lab Med 2019;57:1364–73. https://doi.org/10.1515/cclm-2019-0204.Search in Google Scholar PubMed
131. Piva, E, Stoppa, A, Pelloso, M, Plebani, M. The VES-Matic 5 system: performance of a novel instrument for measuring erythrocyte sedimentation rate. Clin Chem Lab Med 2022;60:1081–90. https://doi.org/10.1515/cclm-2022-0359.Search in Google Scholar PubMed
132. Lazarova, E, Gulbis, B, Oirschot, BV, van Wijk, R. Next-generation osmotic gradient ektacytometry for the diagnosis of hereditary spherocytosis: interlaboratory method validation and experience. Clin Chem Lab Med 2017;55:394–402. https://doi.org/10.1515/cclm-2016-0290.Search in Google Scholar PubMed
133. Hoffmann, JJ, van den Broek, NM, Curvers, J. Reference intervals of extended erythrocyte and reticulocyte parameters. Clin Chem Lab Med 2012;50:941–8. https://doi.org/10.1515/cclm-2011-0796.Search in Google Scholar PubMed
134. Zierk, J, Arzideh, F, Haeckel, R, Rascher, W, Rauh, M, Metzler, M. Indirect determination of pediatric blood count reference intervals. Clin Chem Lab Med 2013;51:863–72. https://doi.org/10.1515/cclm-2012-0684.Search in Google Scholar PubMed
135. Lee, S, Ong, CM, Zhang, Y, Wu, AHB. Narrowed reference intervals for complete blood count in a multiethnic population. Clin Chem Lab Med 2019;57:1382–7. https://doi.org/10.1515/cclm-2018-1263.Search in Google Scholar PubMed
136. Zierk, J, Arzideh, F, Haeckel, R, Rauh, M, Metzler, M, Ganslandt, T, et al.. Indirect determination of hematology reference intervals in adult patients on Beckman Coulter UniCell DxH 800 and Abbott CELL-DYN Sapphire devices. Clin Chem Lab Med 2019;57:730–9. https://doi.org/10.1515/cclm-2018-0771.Search in Google Scholar PubMed
137. Zierk, J, Hirschmann, J, Toddenroth, D, Arzideh, F, Haeckel, R, Bertram, A, et al.. Next-generation reference intervals for pediatric hematology. Clin Chem Lab Med 2019;57:1595–607. https://doi.org/10.1515/cclm-2018-1236.Search in Google Scholar PubMed
138. Jones, GRD, Haeckel, R, Loh, TP, Sikaris, K, Streichert, T, Katayev, A, et al.. Indirect methods for reference interval determination – review and recommendations. Clin Chem Lab Med 2018;57:20–9. https://doi.org/10.1515/cclm-2018-0073.Search in Google Scholar PubMed
139. Haeckel, R, Wosniok, W. The importance of correct stratifications when comparing directly and indirectly estimated reference intervals. Clin Chem Lab Med 2021;59:1628–33. https://doi.org/10.1515/cclm-2021-0353.Search in Google Scholar PubMed
140. Favaloro, EJ, Gosselin, R, Olson, J, Jennings, I, Lippi, G. Recent initiatives in harmonization of hemostasis practice. Clin Chem Lab Med 2018;56:1608–19. https://doi.org/10.1515/cclm-2018-0082.Search in Google Scholar PubMed
141. Favaloro, EJ, Jennings, I, Olson, J, Van Cott, EM, Bonar, R, Gosselin, R, et al.. Towards harmonization of external quality assessment/proficiency testing in hemostasis. Clin Chem Lab Med 2019;57:115–26. https://doi.org/10.1515/cclm-2018-0077.Search in Google Scholar PubMed
142. Favaloro, EJ, Lippi, G. On the complexity of hemostasis and the need for harmonization of test practice. Clin Chem Lab Med 2018;56:1568–74. https://doi.org/10.1515/cclm-2018-0174.Search in Google Scholar PubMed
143. Meijer, P, Kynde, K, van den Besselaar, A, Van Blerk, M, Woods, TAL. International normalized ratio (INR) testing in Europe: between-laboratory comparability of test results obtained by Quick and Owren reagents. Clin Chem Lab Med 2018;56:1698–703. https://doi.org/10.1515/cclm-2017-0976.Search in Google Scholar PubMed
144. van den Besselaar, AMPH, van Rijn CJJ, Hubbard, AR, Kitchen, S, Tripodi, A, Cobbaert, CM. Requirement of a reference measurement system for the tissue factor-induced coagulation time and the international normalized ratio. Clin Chem Lab Med 2019;57:e169–72. https://doi.org/10.1515/cclm-2018-1194.Search in Google Scholar PubMed
145. van den Besselaar, AMHP, Cobbaert, CM. Assignment of international normalized ratio to frozen and freeze-dried pooled plasmas. Clin Chem Lab Med 2020;58:2089–97. https://doi.org/10.1515/cclm-2019-1321.Search in Google Scholar PubMed
146. Buoro, S, Lippi, G. Harmonization of laboratory hematology: a long and winding journey. Clin Chem Lab Med 2018;56:1575–8. https://doi.org/10.1515/cclm-2018-0161.Search in Google Scholar PubMed
147. Li, C, Peng, M, Xu, D, Lu, H, Zhou, W, Liu, Y, et al.. Commutability assessment of reference materials for the enumeration of lymphocyte subsets. Clin Chem Lab Med 2019;57:697–706. https://doi.org/10.1515/cclm-2018-0915.Search in Google Scholar PubMed
148. Grote-Koska, D, Klauke, R, Kaiser, P, Kramer, U, Macdonald, R, Lerche, D, et al.. Total haemoglobin - a reference measuring system for improvement of standardisation. Clin Chem Lab Med 2020;58:1314–21. https://doi.org/10.1515/cclm-2019-1177.Search in Google Scholar PubMed
149. Buoro, S, Da Rin, G, Fanelli, A, Lippi, G. Harmonization of interpretative comments in laboratory hematology reporting: the recommendations of the Working Group on Diagnostic Hematology of the Italian society of Clinical Chemistry and Clinical Molecular Biology (WGDH-SIBioC). Clin Chem Lab Med 2018;57:66–77. https://doi.org/10.1515/cclm-2017-0972.Search in Google Scholar PubMed
150. Henry, BM, de Oliveira, MHS, Benoit, S, Plebani, M, Lippi, G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med 2020;58:1021–8. https://doi.org/10.1515/cclm-2020-0369.Search in Google Scholar PubMed
151. Lippi, G, Plebani, M. EDTA-dependent pseudothrombocytopenia: further insights and recommendations for prevention of a clinically threatening artifact. Clin Chem Lab Med 2012;50:1281–5. https://doi.org/10.1515/cclm-2012-0081.Search in Google Scholar PubMed
152. Hu, Z, Sun, Y, Wang, Q, Han, Z, Huang, Y, Liu, X, et al.. Red blood cell distribution width is a potential prognostic index for liver disease. Clin Chem Lab Med 2013;51:1403–8. https://doi.org/10.1515/cclm-2012-0704.Search in Google Scholar PubMed
153. Stotz, M, Szkandera, J, Stojakovic, T, Seidel, J, Samonigg, H, Kornprat, P, et al.. The lymphocyte to monocyte ratio in peripheral blood represents a novel prognostic marker in patients with pancreatic cancer. Clin Chem Lab Med 2015;53:499–506. https://doi.org/10.1515/cclm-2014-0447.Search in Google Scholar PubMed
154. Franchini, M, Liumbruno, GM. ABO blood group: old dogma, new perspectives. Clin Chem Lab Med 2013;51:1545–53. https://doi.org/10.1515/cclm-2013-0168.Search in Google Scholar PubMed
155. Halbmayer, WM, Weigel, G, Quehenberger, P, Tomasits, J, Haushofer, AC, Aspoeck, G, et al.. Interference of the new oral anticoagulant dabigatran with frequently used coagulation tests. Clin Chem Lab Med 2012;50:1601–5. https://doi.org/10.1515/cclm-2011-0888.Search in Google Scholar PubMed
156. Hoffmann, JJ, Urrechaga, E, Aguirre, U. Discriminant indices for distinguishing thalassemia and iron deficiency in patients with microcytic anemia: a meta-analysis. Clin Chem Lab Med 2015;53:1883–94. https://doi.org/10.1515/cclm-2015-0179.Search in Google Scholar PubMed
157. Shi, H, Zheng, H, Yin, YF, Hu, QY, Teng, JL, Sun, Y, et al.. Antiphosphatidylserine/prothrombin antibodies (aPS/PT) as potential diagnostic markers and risk predictors of venous thrombosis and obstetric complications in antiphospholipid syndrome. Clin Chem Lab Med 2018;56:614–24. https://doi.org/10.1515/cclm-2017-0502.Search in Google Scholar PubMed
158. Lippi, G, Salvagno, GL, Guidi, GC. Red blood cell distribution width is significantly associated with aging and gender. Clin Chem Lab Med 2014;52:e197–9.10.1515/cclm-2014-0353Search in Google Scholar PubMed
159. Magrini, L, Gagliano, G, Travaglino, F, Vetrone, F, Marino, R, Cardelli, P, et al.. Comparison between white blood cell count, procalcitonin and C reactive protein as diagnostic and prognostic biomarkers of infection or sepsis in patients presenting to emergency department. Clin Chem Lab Med 2014;52:1465–72. https://doi.org/10.1515/cclm-2014-0210.Search in Google Scholar PubMed
160. Lippi, G, Plebani, M. Clinical Chemistry and laboratory medicine: enjoying the present and assessing the future. Clin Chem Lab Med 2022;60:1313–5. https://doi.org/10.1515/cclm-2022-0627.Search in Google Scholar PubMed
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