Short- and medium-term biological variation estimates of leukocytes extended to differential count and morphology-structural parameters (cell population data) in blood samples obtained from healthy people

Small-molecule CBP/p300 histone acetyltransferase inhibition mobilizes leukocytes from the bone marrow via the endocrine stress response

Mutations of the CBP/p300 histone acetyltransferase (HAT) domain can be linked to leukemic transformation in humans, suggestive of a checkpoint of leukocyte compartment sizes. Here, we examined the impact of reversible inhibition of this domain by the small-molecule A485. We found that A485 triggered acute and transient mobilization of leukocytes from the bone marrow into the blood. Leukocyte mobilization by A485 was equally potent as, but mechanistically distinct from, granulocyte colony-stimulating factor (G-CSF), which allowed for additive neutrophil mobilization when both compounds were combined. These effects were maintained in models of leukopenia and conferred augmented host defenses. Mechanistically, activation of the hypothalamus-pituitary-adrenal gland (HPA) axis by A485 relayed shifts in leukocyte distribution through corticotropin-releasing hormone receptor 1 (CRHR1) and adrenocorticotropic hormone (ACTH), but independently of glucocorticoids. Our findings identify a strategy for rapid expansion of the blood leukocyte compartment via a neuroendocrine loop, with implications for the treatment of human pathologies.

It's in the blood: a review of the hematological system in SARS-CoV-2-associated COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global healthcare crisis. While SARS-CoV-2-associated COVID-19 affects primarily the respiratory system, patients with COVID-19 frequently develop extrapulmonary manifestations. Notably, changes in the hematological system, including lymphocytopenia, neutrophilia and significant abnormalities of hemostatic markers, were observed early in the pandemic. Hematological manifestations have since been recognized as important parameters in the pathophysiology of SARS-CoV-2 and in the management of patients with COVID-19. In this narrative review, we summarize the state-of-the-art regarding the hematological and hemostatic abnormalities observed in patients with SARS-CoV-2-associated COVID-19, as well as the current understanding of the hematological system in the pathophysiology of acute and chronic SARS-CoV-2-associated COVID-19.

Diagnostic performance of machine learning models using cell population data for the detection of sepsis: a comparative study

OBJECTIVES

To compare the artificial intelligence algorithms as powerful machine learning methods for evaluating patients with suspected sepsis using data from routinely available blood tests performed on arrival at the hospital. Results were compared with those obtained from the classical logistic regression method.

METHODS

The study group consisted of consecutive patients with fever and suspected infection admitted to the Emergency Department. The complete blood counts (CBC) were acquired using the Mindray BC-6800 Plus analyser (Mindray Diagnostics, Shenzhen, China). Cell Population Data (CPD) were also recorded. The ML and artificial intelligence (AI) models were developed; their performance was evaluated using several indicators, such as the area under the receiver operating curve (AUC), calibration plots and decision curve analysis (DCA).

RESULTS

Overall, all the tested approaches obtained an AUC>0.90. The logistic regression (LR) performed well compared to the ML/AI models. The naïve Bayes and the K-nearest neighbour (KNN) methods did not show good calibration properties. The multi-layer perceptron (MLP) model was the best in terms of discrimination, calibration and clinical usefulness.

CONCLUSIONS

The best performance in the early detection of sepsis was achieved using the ML and AI models. However, external validation studies are needed to strengthen model derivation and procedure updating.

Biological variation: recent development and future challenges

Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.

Automated Detection of Dysplasia: Data Mining from Our Hematology Analyzers

Myelodysplastic syndromes (MDSs) are clonal hematopoietic diseases of the elderly, characterized by chronic cytopenia, ineffective and dysplastic hematopoiesis, recurrent genetic abnormalities and increased risk of progression to acute myeloid leukemia. Diagnosis on a complete blood count (CBC) can be challenging due to numerous other non-neoplastic causes of cytopenias. New generations of hematology analyzers provide cell population data (CPD) that can be exploited to reliably detect MDSs from a routine CBC. In this review, we first describe the different technologies used to obtain CPD. We then give an overview of the currently available data regarding the performance of CPD for each lineage in the diagnostic workup of MDSs. Adequate exploitation of CPD can yield very strong diagnostic performances allowing for faster diagnosis and reduction of time-consuming slide reviews in the hematology laboratory.

How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.

Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes

OBJECTIVES

A large number of people undergo annual health checkup but accurate laboratory criterion for evaluating their health status is limited. The present study determined annual biological variation (BV) and derived parameters of common laboratory analytes in order to accurately evaluate the test results of the annual healthcare population.

METHODS

A total of 43 healthy individuals who had regular healthcare once a year for six consecutive years, were enrolled using physical, electrocardiogram, ultrasonography and laboratory. The annual BV data and derived parameters, such as reference change value (RCV) and index of individuality (II) were calculated and compared with weekly data. We used annual BV and homeostatic set point to calculate personalized reference intervals (RI_per) which were compared with population-based reference intervals (RI_pop).

RESULTS

We have established the annual within-subject BV (CV_I), RCV, II, RI_per of 24 commonly used clinical chemistry and hematology analytes for healthy individuals. Among the 18 comparable measurands, CV_I estimates of annual data for 11 measurands were significantly higher than the weekly data. Approximately 50% measurands of II were <0.6, the utility of their RI_pop were limited. The distribution range of RI_per for most measurands only copied small part of RI_pop with reference range index for 8 measurands <0.5.

CONCLUSIONS

Compared with weekly BV, for annual healthcare individuals, annual BV and related parameters can provide more accurate evaluation of laboratory results. RI_per based on long-term BV data is very valuable for "personalized" diagnosis on annual health assessments.

External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count

Purpose

The rRT-PCR for COVID-19 diagnosis is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Routine hematochemical tests are a faster and less expensive alternative for diagnosis. Thus, Machine Learning (ML) has been applied to hematological parameters to develop diagnostic tools and help clinicians in promptly managing positive patients. However, few ML models have been externally validated, making their real-world applicability unclear.

Methods

We externally validate 6 state-of-the-art diagnostic ML models, based on Complete Blood Count (CBC) and trained on a dataset encompassing 816 COVID-19 positive cases. The external validation was performed based on two datasets, collected at two different hospitals in northern Italy and encompassing 163 and 104 COVID-19 positive cases, in terms of both error rate and calibration.

Results and Conclusion

We report an average AUC of 95% and average Brier score of 0.11, out-performing existing ML methods, and showing good cross-site transportability. The best performing model (SVM) reported an average AUC of 97.5% (Sensitivity: 87.5%, Specificity: 94%), comparable with the performance of RT-PCR, and was also the best calibrated. The validated models can be useful in the early identification of potential COVID-19 patients, due to the rapid availability of CBC exams, and in multiple test settings.

Complete Blood Count as point of care testing QBC STAR™: Preliminary evaluation

INTRODUCTION

Point of care testing (POCT) represents a valuable option when laboratory data shall be urgently available for timely clinical management, with a turnaround time (TAT) that is unfeasible using conventional laboratory instrumentation. This study was aimed to compare the performance of QBC STAR™ compared to Sysmex XN-module and the reference optical microscopy (OM) assessment.

MATERIAL AND METHODS

One hundred peripheral blood samples, collected in K₃ EDTA tubes, and 50 capillary blood samples obtained by finger stick were analyzed with QBC STAR™, Sysmex XN-module, and OM. Data were compared with Passing-Bablok regression and Bland-Altman plots.

RESULTS

The Passing-Bablok regression analysis (QBC STAR™ capillary sample vs XN-module) yielded slopes comprised between 0.30 and 1.37, while the intercepts ranged between -17.57 and 232.6. Bland-Altman plots yielded relative bias comprised between -4.87% (for MN QBC STAR™ capillary sample vs XN-module) and 27% (PLT QBC STAR™ capillary sample vs XN-module). A significant bias was found for all parameters except MN and WBC, RBC in all and pediatric samples, and HB in adults samples.

CONCLUSION

The results of this analytical evaluation suggest that QBC STAR™ may not be the ideal tool for performing complete blood count analysis for diagnostic purposes, while it could be more useful in urgent/emergent conditions, such as for rapid monitoring of some hematological parameters (eg, WBC and HB).

Standardization and harmonization in hematology: Instrument alignment, quality control materials, and commutability issue

INTRODUCTION

In the hub and spoke laboratory network, the number of hematology analyzers (HAs) within each core center has increased, and the control of HAs alignment is becoming necessary requirement to ensure analytical quality. In this scenario, HA alignment can be assessed by analyzing the same control material used for internal quality control on multiple HAs, assuming its commutability. The aim of the study was to verify the applicability of a protocol for the alignment of HAs based on control material rather than on fresh whole-blood samples.

METHODS

The alignment of five HAs was evaluated for red (RBC, Hb, MCV, RET), white (WBC, NE, LY, MO, EO, BA, IG), and platelet (PLT) series parameters, following a protocol by SIBioC, using human sample (HS) and quality control material (QC), after the verification of commutability, according to the IFCC protocol. Maximum bias was derived from biological variation data.

RESULTS

A complete alignment between instruments was confirmed for the majority of the parameters investigated both for HS and QC material. Partial misalignments or inconcludent results were instead evident for MCV, MO, EO, BA, and IG. Interestingly, QC material was found to be not commutable for LY, MO, and BA.

CONCLUSION

The alignment of hematologic analyzers for main cell population parameters may be verified with both QC and HS, displaying consistent results and interpretation. The evaluation for some white series parameters (EO, BA, and IG) is critical, and particular attention must be paid to the values of the material used for the alignment.

Short-term biological variation of differential count in healthy subjects in a South Asian population

Estimates of Within-Subject and between subject biological variation for the white blood cell differential count (DC) have not been reported in South Asia. Therefore, we attempted to measure the short-term biological variation estimates for DC. The study was conducted on 28 healthy volunteers (15 males and 13 females). Blood from the volunteers was collected in the morning in K3-EDTA vials and analyzed in triplicate on the Sysmex XN-1000 analyzer, for six consecutive days. The Within subject, between subject and analytical coefficient of variation of the DC was calculated from the results by nested repeated measures ANOVA after outlier exclusion. The Reference change values (RCV) were also calculated. The within-subject variation for eosinophil Count and between subject variation for basophils in our study from South Asia was greater than the published European and American studies. Males and females showed similar biological variation for DC. The within-subject variation of other parameters (Neutrophils, Lymphocytes, Monocytes and Basophils) were similar or showed only mild differences to the published studies. The markedly different within-subject variation for Eosinophil counts suggest that the RCV for DC in South Asians need to be different from the published data in order to have clinical relevance. The Within-subject variation values of the other parameters seem transportable from the published European and American studies, but the small differences found mean that further regional estimates need to be reported for robust evidence of the same.

Multicentric evaluation of analytical performances digital morphology with respect to the reference methods by manual optical microscopy

AIMS

Optical microscopic (OM) evaluation of peripheral blood (PB) cells is still a crucial step of the laboratory haematological workflow. The morphological cell analysis is time-consuming and expensive and it requires skilled operator. To address these challenges, automated image-processing systems, as digital morphology (DM), were developed in the last few years. The aim of this multicentre study, performed according to international guidelines, is to verify the analytical performance of DM compared with manual OM, the reference method.

METHODS

Four hundred and ninety PB samples were evaluated. For each sample, two May Grunwald-stained and Giemsa-stained smears were performed and the morphological evaluation of cells was analysed with both DM and OM. In addition, the assessment times of both methods were recorded.

RESULTS

Comparison of DM versus OM methods was assessed with Passing-Bablok and Deming fit regression analysis: slopes ranged between 0.17 for atypical, reactive lymphocytes and plasma cells (LY(AT)) and 1.24 for basophils, and the intercepts ranged between -0.09 for blasts and 0.40 for LY(AT). The Bland-Altman bias ranged between -6.5% for eosinophils and 21.8% for meta-myemielocytes. The diagnostic agreement between the two methods was 0.98. The mean of assessment times were 150 s and 250 s for DM and OM, respectively.

CONCLUSION

DM shows excellent performance. Approximately only 1.6% of PB smears need the OM revision, giving advantages in terms of efficiency, standardisation and assessment time of morphological analysis of the cells. The findings of this study may provide useful information regarding the use of DM to improve the haematological workflow.

Systematic review and meta-analysis of within-subject and between-subject biological variation estimates of 20 haematological parameters

Background Interpretation of the complete blood count (CBC) parameters requires reliable biological variation (BV) data. The aims of this study were to appraise the quality of publications reporting BV data for CBC parameters by applying the BV Data Critical Appraisal Checklist (BIVAC) and to deliver global BV estimates based on BIVAC compliant studies. Methods Relevant publications were identified by a systematic literature search and evaluated for their compliance with the 14 BIVAC criteria, scored as A, B, C or D, indicating decreasing compliance. Global CVI and CVG estimates with 95% CI were delivered by a meta-analysis approach using data from BIVAC compliant papers (grades A-C). Results In total, 32 studies were identified; four received a BIVAC grade A, 2 B, 20 C and 6 D. Meta-analysis derived CVI and CVG estimates were generally lower or in line with those published in a historical BV database available online. Except for reticulocytes, CVI estimates of erythrocyte related parameters were below 3%, whereas platelet (except MPV and PDW) and leukocyte related parameters ranged from 5% to 15%. Conclusions A systematic review of CBC parameters has provided updated, global estimates of CVI and CVG that will be included in the newly published European Federation of Clinical Chemistry and Laboratory Medicine BV Database.

Reviewing the value of leukocytes cell population data (CPD) in the management of sepsis

Sepsis is a medical emergency that describes the body's systemic immune response to an infection and can lead to end-stage organic dysfunction and death. Despite the advances in understanding the pathophysiology of this syndrome and therapies, sepsis remains one of the leading causes of morbidity and mortality in critically ill patients. Early diagnosis and rapid intervention are essential to improve outcomes, which inspired the concept "golden hour," during which the correction of shock and organic dysfunction can improve the patients' outcomes. But the initial presentation of sepsis is often nonspecific and its severity is difficult to assess. Anomalies in temperature, heart and respiratory rates and leukocyte counts are manifestations of systemic inflammatory response syndrome (SIRS). Diagnosis, management and follow-up of patients with sepsis remains a challenge, and diverse biomarkers have been proposed for the timely diagnosis and prognosis of septic patients: lactic acid, procalcitonin (PCT), C-reactive protein, immature granulocytes. The host's initial response to infection is a humoral, cellular and neuroendocrine reaction to infection, and leukocytes interact with endothelial cells. The new generation of hematological analyzers incorporates technological innovations allowing to expand the information derived from the complete blood count: new leukocyte derived parameters are emerging as potentially useful markers in different clinical situations. Additional research parameters cell population data (CPD), characterizing different leukocyte populations have become available, and preliminary observations suggest their utility in the diagnosis of sepsis. This review emphasizes the value of CPD, reported by modern cellular counters for early recognition of sepsis, and therefore the potential improvement in patient outcomes.

Long-term biological variation estimates of 13 hematological parameters in healthy Chinese subjects

Background The complete blood count (CBC) is a basic test routinely ordered by physicians as a part of initial diagnostic work-up on their patients. To ensure safe clinical application of the CBC, reliable biological variation (BV) data are needed to establish analytical performance specifications. Our aim was to define the BV of CBC parameters using a rigorous protocol that is compliant with the Biological Variation Data Critical Appraisal Checklist (BIVAC) provided by the European Federation of Clinical Chemistry and Laboratory Medicine. Methods Blood samples drawn from 41 healthy Chinese subjects (22 females and 19 males; 23-59 years of age) once monthly for 6 consecutive months were analyzed using an ABX Pentra 80 instrument. The instrument was precisely calibrated. All samples were analyzed in duplicate for 13 CBC parameters. The data were assessed for outliers, normality, and variance homogeneity prior to nested ANOVA. Gender-stratified within-subject (CVI) and between-subject (CVG) BV estimates were calculated. Results The number of remaining data for each subject was 442-484 after removing outliers. No significant differences existed between female/male CVI estimates. Except for leukocytes, neutrophils, and lymphocytes, the mean values of 10 parameters differed significantly between genders, rendering partitioning of CVG data between genders. No significant differences were detected between most BV estimates and recently published estimates representing a Europid population. Conclusions Most BV estimates in BIVAC-compliant studies are similar. The turnover time of blood cells and age distribution of participants should be considered in a CBC BV study. Our study will contribute to global BV estimates and future studies.

Evaluation of the delta of immature platelet fraction as a predictive biomarker of inflammatory response after cardiac surgery

AIMS

Cardiac surgery (CS) can induce an inflammatory response (IR) that is associated with poorer outcomes. Immature platelets are among the factors that may be associated with IR development. We aimed to evaluate whether immature platelet fraction (IPF) could be a predictive biomarker for IR and whether IPF could improve the prognosis assessment of IR for Acute Physiologic and Chronic Health Evaluation (APACHE II) and Sequential Organ Failure Assessment (SOFA) following CS.

METHODS

Three-hundred and twenty-seven (327) patients who underwent CS were enrolled during the study period. IR was defined according to the need for vasopressor support (>48 hours). Perioperative variables and outcomes were registered in our database. IPF was measured immediately following CS and at 24 hours by Sysmex XN analyzer and the difference between both measurements (ΔIPF) was calculated. To assess the relationship between ΔIPF and IR, univariate and multivariate logistic regression were performed. To analyse the additive value of ΔIPF in APACHE II and SOFA scores in predicting IR, an area under the receiver operating characteristic (AUROC) curve was calculated.

RESULTS

Among 327 patients included, 60 patients (18.3%) developed IR. Multivariate analysis showed ΔIPF was significantly associated with IR (OR: 1.26; 95% CI: 1.01 to 1.56; p=0.038). The combination of ΔIPF with scores improved the AUROC for IR prediction: 0.629 vs 0.728 (p=0.010) for APACHE II and 0.676 vs 0.715 (p=0.106) for SOFA.

CONCLUSION

These findings suggested that ΔIPF may be a useful and low-cost biomarker for the early identification of patients at risk of IR development.

Preliminary evaluation of a new flow cytometry method for the routine hematology workflow

Background In a generalist laboratory, the integration of the data obtained from hematology analyzers (HAs) with those from multiparametric flow cytometry (FMC) could increase the specificity and sensitivity of first level screening to identify the pathological samples. The aim of this study was to perform a preliminary evaluation of a new simple hybrid method (HM). The method was obtained by integration between HAs reagents into FCM, with a basic monoclonal antibodies panel for the leukocytes differential count. Methods Eighty-one peripheral blood samples, collected in K3EDTA tubes, were analyzed by XN-module, and CyFlow Space System, using both standard MoAbs and HM method analysis, and with the optical microscopy (OM). Within-run imprecision was carried out using normal samples, the carryover was evaluated, data comparison was performed with Passing-Bablok regression and Bland-Altman plots. Results The within-run imprecision of HM methods ranged between 1.4% for neutrophils (NE) and 10.1% for monocytes (MO) always equal or lower to the OM. The comparison between HM methods vs. OM shows Passing-Bablok regression slopes comprised between 0.83 for lymphocyte (LY) and 1.14 for MO, whilst the intercepts ranged between -0.18 for NE and 0.25 for LY. Bland-Altman relative bias was comprised between -12.43% for NE, and 19.77% for eosinophils. In all 11 pathological samples the agreement between the methods was 100%. Conclusions The new hybrid method generates a leukocytes differential count suitable for routine clinical use and it is also useful for identifying morphological abnormalities with a reduction in cost and improvement of screening for first level hematology workflow.

Improvement in detecting sepsis using leukocyte cell population data (CPD)

Background

The cell population data (CPD) parameters reported by XN analyzers (Sysmex Corporation, Kobe, Japan) reflect the size and internal structure of leukocytes. We explored whether CPD values could contribute to recognize those patients with fever at risk to develop sepsis. A profile of sepsis was developed combining CPD parameters and other markers.

Methods

We recruited 295 patients at the onset of fever, with infection confirmed by positive cultures. We studied the diagnostic performance of the CPD parameters in the differential diagnosis of sepsis vs. non-systemic bacterial infection using receiver operating characteristic (ROC) curve analysis. Additionally, the K-means unsupervised clustering method was applied. Once the clusters had been defined, the relationship between them and the CPD parameter values was assessed with the non-parametric Wilcoxon test. Lastly, the relationship between the clusters obtained and the categorical variables was examined with the χ2-test (or Fisher’s exact test).

Results

ROC analysis demonstrated that NE-FSL, NE-WY, NE-WZ and MO-WZ had areas under the curve (AUCs) >0.700 for predicting sepsis. Using the K-means clustering algorithm, 80 patients (66.67%) were assigned to Cluster 1 and the others to Cluster 2. Out of 80 of patients in Cluster 1, 45 (56.25%) presented a PCT value ≥2 ng/mL, whereas almost 80% of Cluster 2 patients had a PCT <2 ng/mL. Cluster 1 was characterized by high NE-SFL, NE-WY, MO-X, MO-WX and MO-Z values (p<0.05).

Conclusions

CPD related to monocyte complexity and neutrophil activation were found to be significant, with high values suggesting sepsis.

A Preliminary Proposal for Quality Control Assessment and Harmonization of Leukocytes Morphology-structural Parameters (cell Population Data Parameters)

BACKGROUND

The cell population data (CPD) measured by Sysmex XN-9000 can be used for screening many hematological and non-hematological disorders. Since little information is available on harmonization of CPD among different instrumentation and clinical laboratories, this study aimed at assessing the current degree of CPD harmonization between separate Sysmex XN modules allocated to the same laboratory.

METHODS

A total number of 78291 data were used for verification of within-run imprecision, analyzers harmonization, reference ranges and assessment of blood sample stability of CPD parameters, including results of daily quality control testing and those generated in samples collected from blood donors and healthy volunteers.

RESULTS

Within-run imprecision of CPD parameters ranged between 0.4 and 14.1%. Good agreement was found among five different XN-modules, especially when values were adjusted after calculation of instrument-specific alignment factors. The bias of all parameters remained always lower than the reference change values in samples stored for up to 8 hours, regardless of storage temperature.

CONCLUSIONS

The imprecision of CPD parameters was acceptable, except for those reflecting the dispersion of cellular clusters. Due to the lack of reference control materials, we showed that the use of data generated on a large number of normal routine samples (i.e., a Moving Average population) may be a reliable approach for testing analyzers harmonization. Nevertheless, availability of both calibration and quality control materials for these parameters is highly advisable in the future. We finally showed that whole blood samples may be stable for up to 2-4 hours for most CPD parameters.

Short- and medium-term biological variation estimates of red blood cell and reticulocyte parameters in healthy subjects

Background:

The integrated evaluation of traditional and innovative red blood cell (RBC) and reticulocyte parameters is a rapid, inexpensive and non-invasive diagnostic tools for differential diagnosis and follow-up of anemia and other pathological conditions needing bone marrow erythropoiesis assessment. Therefore, estimating the biological variation (BV) of these parameters is essential for evaluating the analytical performance of hematological analyzers, and for enabling accurate data interpretation and appropriate clinical management. This study aims to define short- and medium-term BV estimates and reference change value (RCV) of RBC and reticulocyte parameters.

Methods:

Twenty-one healthy volunteers participated in the assessment of medium-term BV (blood sampling once/week, five consecutive weeks) and 22 volunteers in the assessment of short-term BV (blood sampling once/day, five consecutive days) using Sysmex XN. Outlier analysis was performed before CV-ANOVA, to determine BV estimates with confidence intervals (CI).

Results:

Medium- and short-term within-subject BV were between 0.3% and 16.4% and 0.2%–10.4% (MCH and IRF), respectively, whereas medium and short-term between-subjects BV ranged between 0.9% and 66.6% (MCHC and Micro-R) and 1.4%–43.6% (MCHC and IRF), respectively. The RCVs were similar for all parameters in both arms of the study, except for hemoglobin, RDW-CV and MCV.

Conclusions:

This study allowed for estimating the BV of many RBC and reticulocyte parameters, some of which have not been currently explored. For RBC, hemoglobin, RDW-CV and MCV it seems advisable to use RCV calculated according to monitoring time and/or differentiated by sex. As regards analytical goals, we suggest using the most stringent targets found in the short-term arm of this study.