Automated Nucleated RBC Measurement Using the Sysmex XE-5000 Hematology Analyzer: Frequency and Clinical Significance of the Nucleated RBCs

Influence of hemolysis on nucleated red blood cells count

INTRODUCTION

Hemolysis is a common pre-analytical factor that can influence test results. Here, we explored the influence of hemolysis on nucleated red blood cells (NRBCs) count and tried to illustrate the mechanisms underlying this interference.

METHODS

From July 2019 to June 2021, 20 preanalytical hemolytic peripheral blood (PB) samples from inpatient at Tianjin Huanhu Hospital were evaluated using Sysmex XE-5000 automated hematology analyzer. When NRBC enumeration was positive and a flag was triggered, a 200-cell differential count was performed by experienced technologists on microscopic review. When the manual count was inconsistent with automated enumeration, samples will be re-collected. Plasma exchange test was performed to verify the influence factors of hemolyzed samples and the mechanical hemolysis experiment mimicking hemolysis that might occur during blood collection was performed to illustrate the underlying mechanisms.

RESULTS

Hemolysis led to false-positive NRBC count and the value of NRBC was positively correlated with the degree of hemolysis. Hemolysis specimen shared a common scatter diagram: a "beard" on WBC/ basophil (BASO) channel and a "blue scatter line" on immature myeloid information (IMI) channel. Lipid droplets were found above the hemolysis specimen after centrifugation. Plasma exchange experiment confirmed that these lipid droplets interfered with NRBCs count. Mechanical hemolysis experiment implied further that broken red blood cells (RBCs) released lipid droplets causing the false-positive NRBCs count.

CONCLUSION

In the present study, we firstly found that hemolysis could lead to false-positive NRBCs enumeration, which was associated with lipid droplets released from broken RBCs during hemolysis.

Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors

BACKGROUND

Researchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events.

METHODS

We developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models.

RESULTS

Event rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models.

CONCLUSIONS

We conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.

Improvement of mortality prediction accuracy in critically ill patients through combination of SOFA and APACHE II score with markers of stress haematopoiesis

INTRODUCTION

In critically ill patients nucleated red blood cells (NRBC) and immature granulocytes (IG) appear in the peripheral blood as the consequence of stress haematopoesis. The aim of this retrospective study was to evaluate the diagnostic value of NRBC and IG and to propose a model of improved mortality prediction including these parameters in the assessment of critically ill patients.

METHODS

The study included 338 critically ill adult patients hospitalized at Department of Anaesthesiology and Intensive Medicine, Louis Pasteur University Hospital in Kosice. As NRBC positive patients were considered patients with peripheral NRBC > 0.01 × 10⁹ /L and IG positivity as >0.03 × 10⁹ /L. Apache II index was calculated 24 hours after admission and Systemic Organ Failure Assessment (SOFA) on the day with the worst clinical condition.

RESULTS

NRBC positivity was found in 27.6% of patients. The mortality of NRBC positive patients was 48.38%, significantly higher than 23.7% of NRBC negative patients. IG positivity was 79.0% and their mortality was also higher as compared with that of IG negative patients (69.3% vs 33.8%). Three regression models predicting mortality including stress haematopoiesis markers, APACHE II, SOFA scores and age had sufficient level of sensitivity and specificity.

CONCLUSION

The presence of NRBC in the peripheral blood and the IG increase are available early risk predictors of mortality in critically ill patients. Regression models designed by combination of SOFA, APACHE II, and the new haematological parameters increase the accuracy and effectivity of diagnostic process in predicting prognosis and risk of mortality with high sensitivity and specificity.

Complex biological patterns of hematology parameters in childhood necessitating age- and sex-specific reference intervals for evidence-based clinical interpretation

INTRODUCTION

Hematology laboratory parameters are among the most routinely ordered tests in support of adult and pediatric care. However, appropriate interpretation of test results has been a challenge in pediatrics since accurate and up-to-date reference intervals that reflect the dynamic physiological changes associated with growth and development have not been available. Critical gaps continue to exist in pediatric hematology reference intervals for modern laboratory platforms. To address this gap, this study establishes age- and sex-specific reference intervals for 25 hematology parameters in the CALIPER cohort of healthy children and adolescents using a common platform, the Sysmex XN-3000 analytical system.

METHODS

Fresh whole blood samples collected from a total of 641 healthy children and adolescents (birth to <21 years) with informed consent were analyzed for 25 hematological parameters on the Sysmex XN-3000 Hematology Analyzer. Age- and sex-specific reference standards were calculated based on Clinical and Laboratory Standards Institute guidelines.

RESULTS

Of the 25 analytes assessed, 19 required age-partitioning and seven required sex-partitioning (ie, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, red blood cell distribution width-SD, red blood cell distribution width-CV, and monocyte percentage). Age- and sex-specific differences mostly coincided with the onset of puberty.

CONCLUSION

This study establishes a comprehensive database of pediatric reference intervals for hematology parameters in the CALIPER cohort using the widely used Sysmex XN-3000 analytical platform. These data highlight the dynamic hematological profile observed in healthy children and adolescents and the need for reference interval stratification by age and sex.

Reliable assessment of bone marrow and bone marrow concentrates using automated hematology analyzer

Aim: A limiting factor in advancement of bone marrow based cell therapies is the lack of characterization of cell products delivered to patients. Methods: Using an automated hematology analyzer that can be implemented in clinical setting, the composition of bone marrow aspirates (n = 17 patients) and bone marrow concentrates (n = 12 patients) were assessed. ICC estimates were calculated for measuring reliability. Results: Bone marrow aspirates assessment resulted in excellent reliability for determining white blood cells (ICC - 0.96; 95% CI: 0.92-0.99), red blood cells (ICC - 0.9; 95% CI: 0.77-0.96), platelets (ICC - 0.93; 95% CI: 0.85-0.97) composition. Bone marrow concentrate assessment resulted in excellent reliability for determining white blood cells (ICC - 0.97; 95% CI: 0.93-0.99), platelets (ICC - 0.95; 95% CI: 0.89-0.99) and moderate reliability for red blood cells (ICC - 0.66; 95% CI: 0.36-0.87) composition. Conclusion: Modern automated hematology analyzers could assist to better characterize the cell therapy products to provide reliable and consistent outcomes.

Cutoff Value for Correcting White Blood Cell Count for Nucleated Red Blood Cells: What is it? Why is it Important?

Nucleated red blood cells (RBCs) are normally observed in the peripheral blood of neonates and during pregnancy. Under other conditions, the presence of nucleated RBCs in circulating blood indicates disorder in the blood-producing mechanism. The increased presence of nucleated RBCs, however, falsely elevates the leukocyte count, as measured by most automated hematology analyzers, warranting a manual correction of the leukocyte count. For a long time, cutoff values for correcting white blood cell (WBC) count for the presence of nucleated RBCs have been used regularly, particularly in developing countries. However, because those values are largely subjective, they can vary widely between laboratories worldwide. These varied cutoff values include 1, 5, 10, 20, and 50; it appears that the numbers 5 and 10 are the most common values used in corrections; the reasons require further elucidation. In this article, we discuss the merits of correcting the WBC count for nucleated RBCs at certain cutoff points.

Performance evaluation of the automated nucleated red blood cell count of five commercial hematological analyzers

INTRODUCTION

Recent automated hematology analyzers (HAs) can identify and report nucleated red blood cells (NRBC) count as a separate population out of white blood cells (WBC). The aim of this study was to investigate the analytical performances of NRBC enumeration on five top of the range HAs.

METHODS

We evaluated the within-run and between-day precision, limit of blank (LoB), limit of detection (LoD), and limit of quantitation (LoQ) of XE-2100 and XN-module (Sysmex), ADVIA 2120i (Siemens), BC-6800 (Mindray), and UniCel DxH 800 (Beckman Coulter). Automated NRBC counts were also compared with optical microscopy (OM).

RESULTS

The limits of detection for NRBC of the BC-6800, XN-module, XE-2100, UniCel DxH 800, and ADVIA 2120i are 0.035×10⁹ /L, 0.019×10⁹ /L, 0.067×10⁹ /L, 0.038×10⁹ /L, and 0.167×10⁹ /L, respectively. Our data indicated excellent performance in terms of precision. The agreement with OM was excellent for BC-6800, XN-module, and XE-2100 (Bias 0.023, 0.019, and 0.033×10⁹ /L, respectively). ADVIA 2120i displayed a significant constant error and UniCel DxH 800 both proportional and small constant error.

CONCLUSION

Regards to NRBC counting, the performances shown by BC-6800, XN-module, and XE-2100 are excellent also a low count, ADVIA 2120i and UniCel DxH 800 need to be improved.

Novel Automated Hematology Parameters in Clinical Pediatric Practice

Blood sampling in children is a challenging task, and to extract maximum possible information from these 'precious' samples, the modern-day automated hematology analyzers have been aided with much technological advancement. Various novel blood cell parameters are now available to narrow down the differential diagnoses. However, only few of these are available for routine clinical reporting. Knowledge about their interpretation and reference ranges can prove useful in challenging situations.