Hemolytic specimens in complete blood cell count: Red cell parameters could be revised by plasma free hemoglobin

Abstract Introduction Hemolysis is the main cause of unqualified clinical samples. In this study, we established a method for detecting and evaluating hemolysis in whole blood test. We used a mathematical formula for correcting the influence of hemolysis on complete blood cell count (CBC) so as to avoid re‐venipuncture and obtain more accurate parameters of red blood cell detection, reduce the burden of patients, and improve the efficiency of diagnosis and treatment. Methods Hemolytic samples were selected and then corrected using the new formula. Plasma free hemoglobin (fHB) was used as the criterion to determine the degree of hemolysis; the uncertainty of measurement is acceptable as the limit value of deviation between the measured value and the revised value. Hemolysis simulation analysis in vitro and continuous monitoring of clinical patients were used to verify the correction effect. Results A total of 83 clinical samples with hemolysis were collected and analyzed; fHB 1.4 g/L was selected as the unacceptable value for clinical hemolysis detection. In hemolytic samples, the red blood cell parameters corrected by formula are significantly different from those uncorrected and had a good consistency with those before hemolysis. Conclusion The results show that the hemolysis phenomenon of CBC has a significant impact on routine blood testing. By using the new formula, the influence of hemolysis on erythrocyte and related parameters can be quickly and easily corrected, thus avoiding venipuncture again for re‐examination, reducing diagnostic errors, and saving medical resources.


| INTRODUC TI ON
Over the last decade, new methods for complete blood cell count (CBC) have been developed. Some of the novel technologies include automatic detection assembly line, cell image recognition and capability evolution based on neural network, and expert approval system based on logic tree. Still, an important foundational work has been excluded from the intelligent detection process, and that is the examination of sample traits.
The pre-analysis stage mainly relays on the acquisition of qualified samples and the appropriate clinical test application, which is the premise of the test activities and an important basis for the test quality assurance. 1 However, this stage is preformed outside the laboratory, involving more departments and a larger time and space span, thus might be prone to inspection errors. 2,3 The whole process monitoring of sample collection and transportation is highly efficient process, which includes standardization and automation; yet, the laboratories are often unable to achieve timely and comprehensive quality audit when receiving the samples, which often results in unqualified specimens before the start of the test or during the process. This extends the unqualified specimen turnaround time during the process of testing, which can easily cause the loss of reagents or equipment damage, and could even lead to medical errors. [3][4][5] For CBC that uses anticoagulant blood, there is a greater possibility of unqualified conditions compared with the serum sample test items. 4 Compared with blood coagulation, which can be observed by naked eye, microscopic blood agglutination can often be missed, thus seriously affecting the test results. Another common nonconformity that is commonly ignored in the field of CBC is the sample hemolysis. 6,7 Hemolysis is the most common problem observed during laboratory examination, which usually affects many test items. [8][9][10][11][12] At present, biochemical tests have been applied as a standard for judging and grading the severity of hemolysis. They are based on different items and have different standard of acceptable degree of hemolysis. [13][14][15] Today, many of the biochemical instruments relay on automatic identification and quantitative hemolysis sample capacity, which allows for the severity of detecting errors to be avoided. [16][17][18][19] However, because of the homogeneous sample, the detection of hemolysis in CBC is challenging, no matter which approach is used. It is thought that hemolysis is caused by damage to red blood cells, which can affect red blood cell count (RBC), mean corpuscular hemoglobin concentration (MCHC), and hematocrit (HCT), while the cell debris left from the broken red blood cells may also affect platelet count.
In this study, we used sample surveys to examine the incidence rate of hemolysis when preforming CBC. The severity of hemolysis was graded reasonably; the impact of hemolysis on the test project and the treatment countermeasures were analyzed, thus providing help for the comprehensive identification and treatment of hemolysis samples in the field of CBC.

| Samples collection
This study was performed at the Department of Central Medical Laboratory, Children's Hospital Zhejiang University School of Medicine, Hangzhou, China. All blood samples were selected from discarded specimens and were collected from elbow vein or jugular vein in children and anticoagulated with K2-EDTA. The clinical tests were completed within 2 hours after blood collection.

| Ethics
The Hospital Ethics Committee approved the study.

| Collection and processing of clinical samples with hemolysis
The first study included venous blood samples that were received by the clinical hematology laboratory during 7 days in June 2018, workdays and weekends included. Briefly, samples with clotting or inadequate samples were excluded. Hemolyzed samples were manually selected by analyzing the red color in the supernatant plasma by naked eye, 1-2 hours after standing. Some samples without visible hemolysis were used as control group to verify the centrifugation effect through the plasma free hemoglobin. The samples with hemolysis were mixed and then centrifuged using 800 g for 5 minutes.
Plasma was separated, and the fHB was detected on sysmex XN-L 330 blood analyzer. "Pre-dilution" method was selected, in order to get more accurate results. The raw results were divided by seven to obtain the dilute coefficient 1:7. Then, the red blood cell parameters of the hemolyzed specimens could be revised by fHB, named as "fHB Revise Algorithm." The procedure was performed using the follow-

| Hemolysis simulation in vitro
A total of 15 clinical surplus samples were selected for blood analysis.
Artificial hemolysis resulted from mechanical damage to red blood cells, and it occurred when the syringe draw and detruded blood quickly for several times connected with needle. After stationary, the hemolysis phenomenon was confirmed for visual check; and then, blood analysis was performed. Then, fHB was determined according to the method aforementioned, and the corrected parameters were calculated. By comparing the erythrocyte parameters before and after hemolysis with the same sample, this method was used to verify whether the hemolysis correction formula can accurately reproduce the parameters of red blood cells before hemolysis.

| Continuous monitoring of clinical patients
A total of 5 patients from CICU who underwent multiple CBC testing during 5 days were selected, visible hemolysis occurred at least once during testing. By comparing the parameters of erythrocyte between nonhemolytic samples and hemolytic samples before and after correction, we were able to verify the effect of our correction formula in clinical practice.

| Statistical analysis
The paired t test was used to compare MCHC results between two groups. A P-value < .05 indicated significant difference between the two groups.  In the second study, pre-hemolysis parameters, post-hemolysis parameters, and post-hemolysis correction parameters of 15 hemolysis simulation samples were analyzed (Figure 2  During the research period, in five patients from CICU with stable diseases, frequent CBC tests were performed and hemolysis (fHB ≥ 1.4 g/L) occurred at least once. Regardless of the degree of hemolysis, first, the red blood cell parameters from all the samples were revised by fHB one by one, and then, the measured MCHC and revised MCHC were used to make a comparison (Figure 3). This suggested that the fHB revise algorithm could regain the uniformity of the red blood cell parameters, such as MCHC.

| D ISCUSS I ON
A number of studies have shown that 70%-80% of clinical decisions and subsequent efficacy assessments require the support of laboratory test data and diagnostic reports. 23 And all clinical tests should profoundly rely on good quality control. 24 Under evidencebased medicine, any influencing factor that may lead to errors in clinical data and diagnostic reports can result in harm to patients, 25 an increase in economic burden, a psychological injury, or even a threat to the lives of patients. 22 Therefore, the importance F I G U R E 3 The MCHC of before or after revised in 5 patients of inspection quality assurance is much higher than the applica-  Figure 3, the MCHC of the same patient after correction tends to F I G U R E 4 The processing flow of hemolysis CBC sample be more consistent than before. The research conclusion proves that our calibration formula can effectively correct the deviation caused by hemolysis.
Our data suggested that hemolysis did affect the parameters of red blood cells. Free hemoglobin is used as an indicator of clinical acceptable cutoff value of hemolysis. 30 The deviation of hemolytic samples below this value is not significant compared with the uncertainty of our laboratory testing system, that is, the accuracy of our laboratory testing system cannot well identify such mild hemolytic phenomena. The sample with hemolysis, which is higher than the cutoff value, has a significant effect on the parameters of the red blood cell. We believe that this cutoff value can be used as To sum up, the laboratory needs to establish the processing flow of hemolysis CBC sample ( Figure 4). Through this process, hemolytic samples can be found in a timely manner, and the severity of hemolysis can be assessed. At the same time, the impact of hemolysis can be corrected without the need for immediate re-sampling, which can reduce the physical and psychological burden of patients while avoiding test errors.

ACK N OWLED G M ENTS
This work was supported by the Zhejiang Provincial Natural Science Foundation of China (LY15H050001).

CO N FLI C T O F I NTE R E S T
No potential conflicts of interest relevant to this article are reported.