Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Summary

A protocol to measure peripheral blood leukocytes using a POCT card-based leukocyte analyzer is presented here. Same blood samples were tested by two automated hematology analyzers to evaluate the consistency and accuracy of the results. The results showed that the evaluated analyzer had a good correlation with the reference system.

Abstract

White blood cell (WBC) is an important indicator of inflammation in the body, and it can help distinguish between bacterial and viral infections. At present, most primary medical institutions in China have a poor percentage of adoption of blood-testing technology, and a hematology detection system with a high price to performance ratio and easy operation is urgently needed in primary healthcare centers. This paper introduces the principle and operation procedures of a point-of-care testing (POCT) card-based leukocyte analyzer (evaluated system), which was used to detect WBC indexes such as neutrophils, lymphocytes, and intermediate group cells (including eosinophils, basophils, and monocytes) in whole blood. The results from the evaluated system were compared to those from two commercial automatic hematology analyzers (reference system). The correlation and consistency between the evaluated system and the commercial reference systems were analyzed. The results showed that WBC count and number of granulocytes detected by the evaluated and reference systems showed a strong positive correlation (r_s = 0.972 and 0.973, respectively), while the number of lymphocytes showed a relatively low correlation (r_s = 0.851). A Bland-Altman plot showed that the major difference between the values detected by the evaluated system and the reference systems is within 95% limits of agreement (LoA), indicating that the two systems are in good agreement. In conclusion, the evaluated system has an excellent correlation, robust consistency, and a reliable comparison with the results of the widely used automatic hematology analyzers. It is ideal for WBC detection in primary medical institutions where a full-automatic five-category hematology analyzer is unavailable, especially during the COVID-19 normalized prevention and control period.

Introduction

White blood cell (WBC)count or differential is an important indicator to reflect the inflammation of the body, which can distinguish bacterial infection from viral infection. WBC analysis is also helpful to guide the follow-up diagnosis and treatment¹. At present, the five-classification fully automatic hematology analyzer has been widely used in large and medium-sized medical units, because it is automatic, has high efficiency, yields accurate and reliable results, and effectively reduces the work intensity of laboratory technicians. It plays an important role in clinical examination^2,3. However, most primary medical institutions, such as community healthcare centers and private clinics, have a low adoption rate of a hematology analyzer. According to a nationwide multicenter study on clinical laboratory construction in China, the laboratory construction of primary medical institutions is insufficient, as evidenced by the small size of laboratories, the insufficient talents transmission, and the spread of science and technology to the countryside, amongst other factors⁴.

Since December 2019, COVID-19 began to spread all over the world and developed into a global pandemic. In the 'post-epidemic era', a series of national policies have been proposed to implement the normalized prevention and control measures of epidemic situations. The laboratory of primary medical institutions plays an important role in grassroots diagnosis and treatment and disease prevention and control. It is the first line of defense and control in epidemic situations, and it is critical to COVID-19 prevention and control⁵. Some studies have shown that the detection of peripheral blood lymphocytes and neutrophils will contribute to COVID-19 patient screening, diagnosis, and treatment, and that the neutrophil/lymphocyte ratio can also be used as clinical early warning indicators of severe and critical COVID-19^6,7. Moreover, leukocyte detection has the benefit of providing a quick report. Primary medical and health institutions can extensively carry out leukocyte detection to help detect and screen suspected infections in time.

POCT card-based leukocyte analyzer (evaluated system; see Table of Materials) is a three-classification blood cell analyzer based on the gold standard "Coulter principle". The evaluated system provides quantitative analysis results of one WBC histogram and seven blood parameters including WBC count, number of granulocytes (Gran#), percentage of granulocytes (Gran%), number of lymphocytes (Lym#), percentage of lymphocytes (Lym%), number of intermediate cells (Mid#), and percentage of the intermediate cells (Mid%). It adopts the card-based innovative technology and has advantages such as the availability of single-person detection kit, absence of liquid waste, fast detection in 30 s, being free from routine maintenance, and user-friendly operation. Therefore, it is particularly well-suited to primary medical institutions. This study aims to evaluate the clinical detection performance of POCT card-based leukocyte analyzer by comparing against two fully automatic commercial hematology analyzers (reference system 1 and reference system 2; see Table of Materials) from laboratories of two large-scale public hospitals.

Protocol

This study and the use of human blood samples were approved by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University (GYYY-2016-73). All participants have given their written consent independently or through their parents (in the case of children).

1. Basic information of the study group

NOTE: Venous blood was collected from patients who visited the First Affiliated Hospital of Guangzhou Medical University (Hospital 1) and the Fifth Affiliated (Zhuhai) Hospital of Zunyi Medical University (Hospital 2). The instrument used for blood routine examination in Hospital 1 is Reference system 1, while Hospital 2 uses Reference system 2.

1. A total of 1066 blood samples were collected from patients who visited Hospital 1 (532) and Hospital 2 (534) and underwent blood routine examinations during January 2021.
   ​NOTE: Patients were randomly selected, came from multiple departments and suffer from various diseases.
2. Exclude patients with incomplete medical records, and those who were not cooperative or refused to give informed consent. Exclude those patients whose blood samples exhibited hemolysis, chyle blood, or cloudiness, or if the blood was inadequate in volume or stored for more than 24 h.

2. Study flow and measurements of interest

NOTE: The evaluated system needs 5 µL of the blood sample for determining WBC and the three classification parameters. After collecting blood, the evaluated system and the reference system were used for blood routine examination.

1. Detect WBC and the three classification parameters of 532 and 534 blood samples using the reference system and the evaluated system, respectively.
   1. Let a highly trained technician randomly renumber the selected blood samples after completing the clinical test with the reference system. Then, hand over the samples to another highly trained technician for detection of the WBC and classification parameters using the evaluated system.
2. Reveal the results of the two systems.
3. Ask a third technician to analyze the five indicators (namely WBC count, Gran#, Gran%, Lym#, Lym%) shared by both evaluated and reference systems only.

3. Procedure for using the evaluated system

NOTE: The evaluated system uses the electrical impedance principle (Coulter principle) to count WBC in the detection element. The testing protocol is divided into six parts: start the analyzer, test preparation, blood collection, reagent mixing, sample analysis, and turn off the analyzer.

1. Start the analyzer
   1. Turn the [O/I] power switch on the back of the analyzer to [I]. Check that the indicator light of the analyzer is on.
   2. Enter the correct username and password in the login dialog box and click on Login. Ensure that the system performs self-check and start-up initialization automatically and then displays the Sample Analysis home page.
2. Test preparation
   NOTE: A complete blood sample test requires four consumables: blood lancet, hemolytic reagent, quantitative pipette with a capillary tube inside, and blood cell detection module (Figure 1).
   1. Click on Next Sample, correctly enter the gender, name, and other clinical information (as required), and select an appropriate reference group. Click on OK to save the information.
      NOTE: Different age groups have their own reference interval, so choosing the right reference group can get a more suitable alarm prompt. Newborn: 1-28 days old; Children: 29 days to 14 years old; Adult Male/Female/General: more than or equal to 15 years old.
   2. Tear the thin film of the blood cell detection module, press the Entry/Exit warehouse button, and place the blood cell detection module into the machine warehouse correctly, with its orifice facing outward.
   3. Puncture the hemolytic reagent sealing film with the tip of a quantitative pipette.
3. Blood collection
   1. Capillary blood collection: Disinfect the left ring finger with a cotton swab dipped in alcohol one way and once. After the alcohol naturally dries out, use a blood lancet to puncture the skin of the left ring finger.
      1. Gently squeeze out the first drop of blood and wipe it with a cotton swab. Squeeze out enough blood to form a full "waterdrop" and collect 5 µL of the blood sample using the capillary tube inside the quantitative pipette.
   2. Venous blood collection: Collect 5 µL of the pre-obtained venous blood sample using the capillary tube inside the quantitative pipette. All tests in this study used venous blood which was collected from each patient (5 mL) using a vacuum vessel containing EDTA-K₂ anticoagulant. Complete all tests within 30 min to 24 h.
4. Reagent mixing
   1. Insert the quantitative pipette into the hemolytic reagent (2.5 mL) and press it tightly to release the blood sample from the capillary tube.
   2. Mix the blood in the capillary tube and hemolytic reagent by turning it upside down 15-20 times at a constant speed, until no obvious red blood remains in the capillary tube. In this study, the blood sample is mixed with hemolytic reagent at a ratio of 1:500.
5. Sample analysis
   1. Open the lid and squeeze the solution into the blood cell detection module.
   2. Press the Entry/Exit warehouse button. After the blood cell detection module enters the warehouse, press the Counting button.
      NOTE: A flashing green indicator light indicates that the analyzer is counting. The blood cell detection module will automatically exit the warehouse after counting, and it should be removed and disposed of properly. Each test takes only 30 s.
   3. On the analyzer interface, click on the OK button twice to confirm that the blood cell detection module has been taken out.
   4. On the analyzer interface, click on the Print button to print the test results.
6. Turn off the analyzer
   1. On the analyzer interface, click on the shutdown button, and select Yes in the dialog box that pops up on the interface. Check that the system starts to execute the shutdown sequence.
   2. Set the [O/I] switch on the back of the mainframe to [O] after the shutdown sequence is completed.

4. Statistical analysis

1. Detect the outliers using the generalized extreme studentized deviate (ESD) method and eliminate these outliers for follow-up statistical analysis according to the requirements of the American Association for Clinical Laboratory Standardization (CLSI) EP9-A3 document⁸.
2. Calculate the descriptive parameters such as means and standard deviations (SDs) for normally distributed continuous data; medians and 25%-75% interquartile ranges for nonnormally distributed data; and frequencies and percentages for categorical data.
3. Use Pearson χ2 test or Fisher's exact test to determine the degree of relationship between categorical variables. Use the Paired-Sample T-test or Mann-Whitney U test to compare numerical data between groups.
4. Show the distribution and linear association of the detected results of the two systems by scatter plots. Apply Spearman's nonparametric correlation test to access the degree of relationship between the quantitative variables. Use Bland-Altman plots and intraclass correlation coefficient (ICC) to verify the agreement between quantitative values detected by the two systems.
5. Analyze the data by statistical software of choice. P-value < 0.05 is considered statistically significant.

Representative Results

Sample data
A total of 1066 patients were enrolled in two research centers, including Hospital 1 (n = 532) and Hospital 2 (n = 534). The patient characteristics are shown in Table 1. The percentage of males is 49.9% and the median age is 52 (32, 66) years. Patients enrolled in the study were comprised of inpatients (51.1%), outpatients (39.0%), and physical examination patients (8.4%). The samples tested were from patients who visited the internal medicine departments (30.6%), surgery departments (19.1%), obstetrics and gynecology departments (9.0%), pediatric departments (3.9%), etc.

Detecting aberrant results (outliers)
Taking the reference system results as the horizontal axis and the evaluated system results as the vertical axis, a scatter plot of five leukocyte indexes of 1066 samples was obtained (results are not shown). Suspicious abnormal points are obvious in the scatter plot of five leukocyte indexes. According to the results of the ESD method, among the 1066 samples, there were 16 outliers in WBC count, 27 outliers in Gran#, 8 outliers in Gran%, 15 outliers in Lym#, and 9 outliers in Lym%. The data was analyzed after eliminating the outliers, which account for less than 5% in total.

Correlation analysis in the evaluated system and reference systems
Figure 2 shows the scatter plot and Spearman correlation analysis of the test results. The results show that WBC count, Gran#, Gran%, Lym#, and Lym% have good linearity and correlation between the two systems; the linear coefficient R² is between 0.7759 and 0.9676, and the correlation coefficient r_spearman is between 0.851 and 0.973 (all P-values < 0.001). WBC and Gran# exhibit the strongest correlation between the two systems (R² = 0.9608 and 0.9676, respectively; r_spearman = 0.972 and 0.973, respectively), whereas Lym# exhibit the weakest correlation between the systems (R² = 0.7759; r_spearman = 0.851). However, we could not evaluate the correlation and consistency of intermediate cells (Mid#/%), because they are separated into smaller divisions (i.e., eosinophils, basophils, and monocytes) in the reference system.

Consistency Analysis
A Bland-Altman plot has been obtained to visualize the consistency analysis and the ±2 SD has been marked as limits of agreement (LoA). The results are shown in Figure 3. For the WBC, the mean value of the difference is 0.9 x 10⁹/L between the evaluated and reference systems, and the 95% confidence interval (CI) of the difference is -0.7 x 10⁹ ~ 2.5 × 10⁹/L.There are 94.48% (992/1050) samples within the 95% CI, which means that the WBC results detected by the evaluated system are in good agreement with the reference system. For Gran# and Gran%, the mean value of the difference between the evaluated and reference systems is 0.8 x 10⁹/L and 2.0%, respectively, while the 95% CI of the difference is 0.7 x 10⁹ ~ 2.2 x 10⁹/L and -7.7% ~ 11.7%, respectively. There are 94.23% (979/1039) and 94.99% (1005/1058) samples within the 95% CI, respectively. For Lym# and Lym%, there are 93.82% (986/1051) and 93.76% (991/1057) samples within the 95% CI, and the average difference value is 0.33 x 10⁹/L and 1.8%, respectively. The average values of the differences in the leukocyte indexes are all above 0, indicating that the test results of the evaluated system are slightly higher than those of the reference system.

Correlation Analysis in different patient groups
The WBC count results for the samples from patients of different ages and genders and those from different departments were compared and analyzed for correlations. The results showed that the WBC level of the evaluated system is higher than that of the reference system. There is no significant difference in consistency and correlation between male and female patients (ICC: 0.97 versus 0.98; r_spearman: 0.98 versus 0.97). The consistency and correlation are better for the inpatients than for the outpatients (ICC: 0.98 versus 0.96; r_spearman: 0.98 versus 0.96), as shown in Table 2.

Figure 1: Representative photographs of the evaluated system and its supporting reagents and consumables. (A) The analyzer uses the Coulter principle to detect leukocytes in blood by three classifications. Its small size (242 mm x 397 mm x 321 mm) makes it easy to transport. (B) Limited supporting reagents and consumables are combined in a suit as a single-person detection kit, which can be stored and used at room temperature. Please click here to view a larger version of this figure.

Figure 2: Scatter plots of five leukocyte indexes detected by the evaluated system and reference system. R² = linearity coefficient, r_s = Spearman correlation coefficient (95% CI), and n = sample size. Please click here to view a larger version of this figure.

Figure 3: Bland-Altman plot of the five leukocyte indexes detected by the evaluated system and reference system. The Y-axis depicts the difference of measured values between the evaluated and reference systems while the X-axis depicts the mean value of leukocyte indexes measured by the evaluated and reference systems. The continuous black line represents the average difference value for the entire pool of samples and the dashed black lines represent 95% upper and lower limits of agreement (mean limits of agreement ± 1.96 SD). ES_i denotes values measured by the evaluated system and RS_i denotes values measured by the reference system. Please click here to view a larger version of this figure.

Table 1: General data of the study population. n = sample size, IQR = interquartile range. The P-value corresponds to the comparison of the sample data from Hospital 1 and Hospital 2. Chi-square test (χ²) was used to compare the differences between categorical variables in groups (when the theoretical frequency was less than 5, the Fisher exact probability method was used to calibrate), and t-test or Mann-Whitney U test was used to compare numerical data between groups. Please click here to download this Table.

Table 2: Correlation analysis of the WBC results detected by the evaluated system and reference system in different patient groups. n = sample size; IQR = interquartile range; and ICC = intraclass correlation coefficient (a value > 0.75 indicates good reliability). r_s = Spearman's correlation coefficient; r_s of 0.90-1.00, 0.70-0.90, 0.50-0.70, 0.30-0.50 and 0-0.30 indicate, respectively, very high, high, moderate, low, and negligible positive correlation. Please click here to download this Table.

Discussion

With the advancement of modern laboratory medicine, it is now typical to see several detection technologies utilized in the same or different laboratories to identify the same clinical marker. As a result, more emphasis should be placed on the consistency of test results to assist clinics in making accurate interpretations and judgments of test results. According to the investigation, the total value of laboratory equipment in tertiary hospitals and independent laboratories is substantially higher than that in primary hospitals and other medical institutions⁴. Although this type of equipment has the advantages of multiple item detection, high accuracy, stability, and large detection flux, it has disadvantages such as being expensive, large and heavy, complex operation, a requirement for many reagents, and high demand for professional quality operators, amongst others, making it unsuitable for primary medical institutions to use. The development of detection instruments and equipment is constantly developing in the direction of automation, intelligence, standardization, personalization, and small portability⁹. Advantages of POCT testing equipment, such as high timeliness, being lightweight and easy to carry, as well as no maintenance, make up for the shortcomings of large blood analyzers and are more easily accepted by primary medical institutions¹⁰. Realizing the comparability of the test results of the same specimen and items by different testing instruments is the core content of laboratory quality management¹¹. However, few studies evaluate the correlation and consistency between POCT hematology analyzer and similar clinical products.

Patients in this study were recruited at random from two centers in Guangzhou (Hospital 1) and Zhuhai (Hospital 2) city, China. There was no significant difference in gender ratios between the two centers. Patients enrolled included those who visited hospitals for physical examination, and outpatients and inpatients from internal medicine, surgery, obstetrics and gynecology, pediatric, critical care departments, etc. The median age of the patients in Hospital 1 is significantly higher than that in Hospital 2 (58 (37, 68) versus 46 (31, 63); P < 0.001). Hospital 1 is a national key specialized hospital for respiratory diseases, with most patients suffering from respiratory diseases which are more likely to affect the elderly¹². The scientific study of aging found that the proportion of people over 65 years old in Hospital 1 is higher than that in Hospital 2 (6.67% versus 5.01%)¹³.

There are some outliers in granulocyte count, accounting for 2.5%, but the outliers of all items are less than 5%, which is within the acceptable range. The linear coefficient R² of the test results of the evaluated system and the reference system are all greater than 0.75, indicating that the linear regression lines of the two systems have good linear goodness of fit, with white blood cell count and neutrophil count having better linear goodness of fit. The strongest correlation was found between the number of leukocytes and granulocytes (r_s = 0.972 and 0.973, respectively) detected by the two systems, followed by granulocyte percentage and lymphocyte percentage (r_s = 0.939 and 0.932, respectively), and lymphocyte number (r_s = 0.851). Similar to a reported study, the effect of POCT series equipment on white blood cell counting is better than the results of classified indexes¹⁴. In addition, the Bland-Altman plot of the difference between the test data of the two systems shows that most of the test points are within the 95% consistency limit, indicating that the test results of the evaluation system and the reference system are in good agreement. Although the results show that the consistency analysis of lymphocytes is slightly lower than that of WBC and granulocytes, the correlation coefficient of lymphocytes is still above 0.85, which means that the two systems also have good consistency when detecting lymphocytes. On the other hand, the Bland-Altman plot shows that the test results of the evaluated system are slightly higher than the test values of the reference system. It is speculated that this may be due to systematic errors in the evaluated system, which makes the overall result high. Firstly, one should carefully check whether the analyzer is placed correctly before the test begins; for example, the surrounding area of the instrument should be kept at a certain distance (≥ 20 cm) from the wall or obstacles, and the space where the instrument is placed should be well ventilated. Secondly, the R and D team can also adjust the internal parameters of the system to correct the systematic error.

The novel card-based POCT leukocyte detection system uses the electrical impedance principle to detect leukocytes and their volume distribution^15,16. Taking advantage of the difference in electrical conductivity between blood cells and electrolyte solutions, when blood cells with different volume sizes pass through the counting hole, it can cause changes in current or voltage inside and outside the hole, forming a pulse voltage. This pulse voltage is comparable to the number of blood cells and corresponds to the volume size, thus indirectly distinguishing groups of blood cells and counting them separately. Several critical steps in the leukocyte detection using the evaluated analyzer need to be paid attention to, which is closely related to the accuracy of the test results. First, when collecting blood, the first drop of blood should be wiped away with a sterile dry swab, as it may contain excess tissue fluid which may affect the test results. Second, after collecting the second drop of blood with a capillary tube, the blood attached to the outside of the tube should be wiped off to ensure that the collected blood sample volume is exactly 5 µL.In addition, blood samples and hemolytic reagents should be fully mixed. At the same time, ensure that all solutions are fully squeezed into the blood cell detection module.

Because of its outstanding advantages, including low cost, simple operation, being fast, and being free from daily maintenance, this POCT system is very suitable for use in outpatient centers or primary medical units, which is an important supplement to the current clinical application⁴. The application of this system can cover the blood routine examination in all primary medical institutions in all developing areas of China and has important clinical significance for early screening of diseases, especially infectious diseases and blood diseases^17,18. However, an important limitation is that leukocytes can only be divided into three categories (neutrophils, lymphocytes, intermediate cells). In the future, the classification ability of the system should be further improved in order to achieve the purpose of accurate diagnosis.

In conclusion, as a novel leukocyte analysis equipment, POCT leukocyte analyzer has the advantages of being portable, low cost, easy operation, fast and accurate detection; it exhibits good correlation, strong consistency, and reliable comparison with the automatic hematology analyzer widely used in clinics, and is, therefore, suitable for use in primary medical units.

Materials

Name	Company	Catalog Number	Comments
Blood cell detection module	Chuanghuai Medical Technology Co., Ltd.(Shenzhen, China)		consumables for evaluated system
Blood lancet	Chuanghuai Medical Technology Co., Ltd.(Shenzhen, China)		consumables for evaluated system
Hemolytic reagent	Chuanghuai Medical Technology Co., Ltd.(Shenzhen, China)		consumables for evaluated system
IBM SPSS Statistics 25	International Business Machines Corp., Armonk, NY		Software for data analysis
MedCalc 11.4.2.0	2021 MedCalc Software Ltd		Software for data analysis
Microsoft Excel 2019	Microsoft		Software for data analysis
Point-of-care testing (POCT) card-based leukocyte analyzer	Chuanghuai Medical Technology Co., Ltd.(Shenzhen, China)	CX-2000	Evaluated system
Quantitative pipette with capillary tube inside	Chuanghuai Medical Technology Co., Ltd.(Shenzhen, China)		consumables for evaluated system
Siemens fully automatic hematology analyzer and its related reagents and consumables	Siemens Healthcare Diagnostics Inc.	ADVIA 2120i	Reference system 2
UniCel DxH 800 Coulter Cellular Analysis System and its related reagents and consumables	Beckman Coulter, Inc.	DxH 800	Reference system 1