Article

Pre-analytical errors in hematology laboratory: From laboratory errors to national targets

Ahmed Jaber ¹, Ahmed Mirza ¹, Rowaid Qahwaji ^1,2*

¹ Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.

² Hematology Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

* Correspondence: rgahwajy@kau.edu.sa (R.Q.)

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Citation: Jaber, A, Mirza, A, and Qahwaji, R. Pre-analytical errors in hematology laboratory: From laboratory errors to national targets. Glob. Jour. Bas. Sci. 2025, 1(10). 1-9.

Received: July 11, 2025

Revised: July 22, 2025

Accepted: August 01, 2025

Published: August 04, 2025

doi: 10.63454/jbs20000053

Volume 1; Issue 10

ISSN: 3049-3315

Download the PDF file for full article

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Abstract: Applying of Quality assurance (QA) in hematology laboratory is essential to generate accurate and reliable laboratory results. Inaccuracy of laboratory results usually causes misguidance in clinical diagnosis and treatment plan of the patients. Such issues could be due to pre-analytical, analytical or post-analytical variables. Although analytical and post-analytical phases can be monitored and avoided through recent advanced technology and computing system, the pre-analytical phase is considered a challenge in controlling. Therefore, we aimed through this study to investigate the common types of pre-analytical errors accounted in sample rejection in hematology laboratory at King Abdulaziz University Hospital (KAUH). This retrospective study was conducted over a period of 6 months (Jan 2017-Jun 2017) in Hematology laboratory of KAUH, Jeddah, Saudi Arabia. A total of 164,493 samples were received during this period and rejected samples reported based on the lab rejection policy. Trained hospital staff collected the samples from outpatient department (OPD) and inpatient department (IPD). A full information about medical wards and pre-analytical variables were collected from the lab rejection sheets. Overall results have shown that the most common pre-analytical variables encounter for samples rejection were clotted sample, insufficient quantity, wrong tube, overfilled tube, double order, and hemolysed sample.

Keywords: Pre-analytical errors; Hematology laboratory; Laboratory quality management; Sample rejection; Clotted samples; Specimen handling; Laboratory automation; Total testing process; Quality indicators; Laboratory accreditation; Patient safety; National quality targets; Phlebotomy practices; Pre-analytical phase.

1. Introduction

The reliability of hematology laboratory results forms the cornerstone of accurate clinical diagnosis, effective patient management, and monitoring of therapeutic outcomes. However, despite advances in automation, standardization, and quality management systems, laboratory errors remain a persistent challenge within clinical diagnostics. It is widely recognized that the majority of laboratory errors occur not within the analytical phase, but during the pre-analytical phase—the stage encompassing all processes from patient preparation and specimen collection to sample transportation, storage, and processing. Studies have shown that up to 70% of laboratory errors originate from the pre-analytical phase, underscoring its critical impact on overall laboratory quality and patient safety. Quality is indeed a multifaceted entity. Consequently, a simple answer to questions such as “What is quality? is not easy. The traditional definition of quality is based on the viewpoint that products and services must meet the requirements of those who use them. Quality is inversely proportional to variability: if the variability of end-product decreased the quality of the product increases [1]. Quality assurance is concerned with all aspects of laboratory practice. Specific activities include internal quality control, external quality assessment, proficiency surveillance and standardization [2].

These new challenges are a change from the traditional laboratory-based activities with which many laboratory staff is comfortable and this new role can cause some unease and discomfort. This article outlines the different phases of the total testing process, discusses laboratory accreditation requirements for the extra-analytical phase and describes some of the resources available for laboratories in managing this unfamiliar area [3]. It is also important to ensure that the specimen containers do not leak, not only because contamination of laboratory (and ward) staff is a potential health but also because container without secure cap is likely to disturb the constituents and their relationship, by evaporation of plasma and by leakage. The laboratory director must maintain close contact with the wards and staff who undertake specimen collection and also with the suppliers to ensure that these many factors are taken into account before specimen actually arrives at the laboratory [4].

In hematology, pre-analytical variables exert a particularly profound influence due to the delicate nature of blood cells and their susceptibility to time, temperature, anticoagulant ratios, and mechanical stress. Errors such as improper sample identification, hemolysis, clot formation, incorrect anticoagulant use, inadequate mixing, delayed analysis, and inappropriate storage conditions can significantly alter hematological parameters, leading to spurious results. These errors not only compromise diagnostic accuracy but can also lead to inappropriate clinical decisions, delayed treatments, and unnecessary repeat testing—ultimately increasing healthcare costs and reducing laboratory efficiency.

With the growing emphasis on total quality management (TQM) and ISO 15189:2022 accreditation standards, laboratories are now mandated to establish robust quality indicators to monitor and minimize pre-analytical errors [5]. International and national agencies—such as the World Health Organization (WHO), the International Federation of Clinical Chemistry (IFCC), and various national accreditation boards—have proposed frameworks for error reduction and patient safety enhancement. Aligning with these initiatives, several countries have defined national quality targets for acceptable pre-analytical error rates in hematology and other diagnostic disciplines. These benchmarks aim to transform laboratory quality assurance from a reactive to a preventive model by integrating error reporting systems, root-cause analysis, and continuous professional training.

Despite these efforts, a significant gap persists between policy and practice, especially in low- and middle-income countries, where resource limitations, inadequate training, and lack of automation hinder the implementation of standardized pre-analytical protocols. Furthermore, there is a paucity of comprehensive national data on pre-analytical error rates in hematology laboratories, which limits the ability to set realistic and achievable national targets. Hence, a systematic evaluation of the frequency, causes, and impact of pre-analytical errors in hematology laboratories is essential to develop context-specific strategies and benchmarks [6].

The present study aims to analyze the spectrum and prevalence of pre-analytical errors in hematology laboratories, identify key risk factors, and correlate these findings with national and international quality indicators. By doing so, it seeks to contribute to the establishment of evidence-based national targets for pre-analytical error reduction, ultimately fostering a culture of continuous improvement and patient-centered laboratory practice.

2. Results

The incorporation of computational methods into the drug discovery and development process has greatly accelerated the creation of potent anti-cancer treatments. These methods shorten the time and expense of experimental validation, speed up the creation of hypotheses, and enable tailored medication. Drug discovery, target identification, treatment optimization, and systems-level modeling are the broad categories into which computational approaches in anti-cancer therapy belong.

2.1. Common reported pre-analytical variables in the collected samples: During the six-month study period from January to June 2017, a total of 164,493 blood samples were processed by the Hematology Laboratory at King Abdulaziz University Hospital (KAUH). Review of the laboratory’s rejection log revealed that 1,092 samples were deemed unacceptable for analysis due to various pre-analytical errors, corresponding to an overall rejection rate of 0.66%. Although relatively low, this percentage represents a significant number of samples when considered in the context of the high laboratory throughput and the potential clinical consequences of delayed or inaccurate test results.

Table 1. List of the most common pre-analytical variables reported in the KAUH Hematology lab. The variables are ordered based on frequency and percentage of occurrence.

	Pre-Analytical Variables	Code	Number of Rejected Samples	% out of the Total Rejected Samples	% out of the Total Received Samples
1	Clotted Sample	Clotted	825	75.5%	0.50%
2	Insufficient Quantity	Q.N.S	205	18.8%	0.12%
3	Wrong Tube	W.T	32	2.9%	0.019%
4	Over-filled Sample	O.F	21	1.9%	0.012%
5	Double Order	D.O	4	0.37%	0.002%
6	Hemolyzed Sample	Hemolyzed	2	0.18%	0.001%
7	Wrong Barcode	W.B	2	0.18%	0.001%
8	Mislabeled	Mis.L	1	0.091%	0.0006%
Total			1,092 samples	100%	0.6556%

For each rejected sample, the medical unit from which the sample originated and the specific cause of rejection were documented and systematically entered into a Microsoft Excel database for further analysis. The distribution of pre-analytical errors demonstrated considerable variability, but several issues consistently emerged as the most frequently encountered. These included clotted samples, insufficient sample volume, use of incorrect collection tubes, overfilled tubes, duplicate (double) orders, hemolyzed samples, incorrect barcodes, and mislabeled specimens. The detailed frequency of each category is summarized in Table 1, providing a comprehensive snapshot of the pre-analytical performance of the hematology sample collection process.

Among the reported issues, four pre-analytical errors stood out as the most prevalent and clinically impactful, and therefore these were selected for more detailed analysis, as illustrated in Figure 1. These comprised:

1. 1. Clotted samples
   2. Insufficient sample quantity (QNS – Quantity Not Sufficient)
   3. Incorrect or inappropriate tubes
   4. Hemolysis

Figure 1. List of the most four common pre-analytical variables reported in the KAUH Hematology lab. Such variables accounted for roughly 99% of the rejected samples.

 

 

 

 

 

Across the six-month period, the average number of rejected samples was 182 per month, indicating a persistent pattern of pre-analytical challenges rather than isolated occurrences. However, the dominance of a single error category was clearly evident: sample clotting accounted for approximately 76% of all rejected specimens, making it by far the leading cause of rejection in the hematology laboratory. This disproportionate frequency underscores the vulnerability of the coagulation-sensitive hematology tests to errors in blood collection technique, sample handling, or timing.

Given the overwhelming representation of clotted samples within the rejection dataset, the subsequent results and interpretation in the discussion section primarily emphasize this issue. By focusing on clotting-related errors—which carry significant implications for diagnostic validity, patient safety, and resource utilization—this study highlights a critical area for targeted quality improvement within the pre-analytical phase of hematology testing.

2.2. Medical words showed high frequency pre-analytical variables: A comprehensive review of rejected hematology samples across the institution revealed that pre-analytical variables were distributed among approximately 65 medical units, each identified according to the standardized terminology used in the King Abdulaziz University Hospital (KAUH) electronic medical system. These units collectively contributed to a wide spectrum of pre-analytical errors; however, the pattern of rejection demonstrated significant variation across departments. Among all rejection categories, blood coagulation (clotted samples) emerged overwhelmingly as the predominant cause, representing nearly 75% of all rejected specimens. Due to its high prevalence and potential impact on diagnostic accuracy, this study focused specifically on characterizing the distribution and implications of clotted samples across different clinical areas. A complete list of all sixty-five medical terms used is provided in Table 2.

Table 2. List of the KAUH medical clinics associated with the pre-analytical variable of clotting sample reported in the KAUH Hematology laboratory. The clinics are ordered based on frequency of clotting samples occurrence.

	KAUH Wards	Code	# of Clotted Samples	% out of the Total Clotted Samples
1	Neonatal intensive care	NIC	97	11.8%
2	Pediatric level 1	PED1	89	10.8%
3	Major intensive care	MIC	72	8.7%
4	Female medical	FM	65	7.9%
5	Surgical intensive care	SIC	46	5.6%
6	Male surgical	MS	46	5.6%
7	Male medical	MM	37	4.5%
8	Pediatric level 2	PED2	34	4.1%
9	Pediatric cardiac intensive care unit	PCICU	32	3.9%
10	Pediatric intensive care unit	PICU	31	3.8%
11	Female surgical	FS	27	3.3%
12	Emergency Room	ER	20	2.4%
13	Nursery	NN	18	2.2%
14	Dialysis	LD	18	2.2%
15	Pediatric surgery clinic	C-PSU	18	2.2%
16	Pediatric clinic	C-PED	17	2.1%
17	Surgical clinic	C-SUR	12	1.5%
18	Gynecology	GYN	11	1.3%
19	Definitive care unit	DCU	10	1.2%
20	Hematology clinic	C-HEM	10	1.2%
21	Day care unit	DCUO	9	1.1%
22	Cardiac care unit	CCU	9	1.1%
23	Obstetric clinic	OB	8	1.0%
24	Ophthalmology clinic	C-OPH	8	1.0%
25	ENT clinic (upper respiratory)	C-ENT	8	1.0%
26	Pre-anesthesia clinic	C-PAN	7	0.8%
27	Isolation unit	ISO	5	0.6%
28	Urology clinic	C-URO	5	0.6%
29	Cardiology clinic	C-CAR	5	0.6%
30	Pediatric intensive care unit	PICU2	4	0.5%
31	Gastroenterology and hepatology	C-END	4	0.5%
32	Plastic surgery clinic	C-PLA	3	0.4%
33	Medical clinic (respiratory-GIT)	C-MED	3	0.4%
34	Unknown	PISU	2	0.2%
35	Unknown	C-URC	2	0.2%
36	Unknown	C-OR1	2	0.2%
37	Nephrology clinic	C-NEP	2	0.2%
38	Fetal medicine clinic	C-FM	2	0.2%
39	Pediatric surgery clinic	PSU	1	0.1%
40	Unknown	ERO	1	0.1%
41	Unknown	C-STIO	1	0.1%
42	Staff health clinic	C-STF	1	0.1%
43	Pediatric infectious clinic	C-PIC	1	0.1%
44	Pediatric hematology oncology	C-PHO	1	0.1%
45	Peritoneal dialysis clinic	C-PDV	1	0.1%
46	Orthopedic clinic	C-ORT	1	0.1%
47	Unknown	C-ONO	1	0.1%
48	Oncology clinic	C-ONC	1	0.1%
49	Endocrinology clinic	C-OCR	1	0.1%
50	Obstetric clinic	C-OBS	1	0.1%
51	Neurosurgery clinic	C-NSR	1	0.1%
52	Infertility clinic	C-INF	1	0.1%
53	Infectious disease clinic	C-INDS	1	0.1%
54	Infectious disease clinic	C-IND	1	0.1%
55	Hyper lipedemia clinic	C-HLC	1	0.1%
56	Unknown	C-GDM	1	0.1%
57	General clinic	C-GC	1	0.1%
58	Fracture clinic	C-FRC	1	0.1%
59	Dawn syndrome clinic	C-DS	1	0.1%
60	Dermatology clinic	C-DER	1	0.1%
61	Dental/maxillo facial surgery clinic	C-DEN	1	0.1%
62	Celiac disease clinic	C-CEL	1	0.1%
63	Academic university staff clinic	C-AUS	1	0.1%
64	Asthma clinic	C-ASM	1	0.1%
65	Pain clinic	C/GCA	1	0.1%
Total			825	100%

Analysis of clotted samples by unit revealed distinct patterns that reflect differences in patient populations, sampling workflow, and clinical workload. The Neonatal Intensive Care Unit (NIC) contributed the highest proportion of clotted samples, with 97 cases (11.8%) of all rejected clotted specimens. This disproportionately high frequency may be attributed to challenges inherent in neonatal blood collection, such as small vessel size, difficulty in venipuncture, and increased reliance on heel-prick sampling, all of which increase the risk of clot formation. Following the NIC unit, the Pediatric Clinic 1 (PED1) accounted for 89 samples (10.8%), indicating that pediatric patient groups overall experience a relatively higher incidence of pre-analytical coagulation issues compared with adult units. The Major Intensive Care Unit (MIC) ranked third, contributing 72 clotted samples (8.7%). High-acuity environments like MIC often involve urgent or repeated blood sampling, sometimes under suboptimal conditions, which may explain the elevated rate of clot-related sample rejection.

The Female Medical Unit (FM) contributed 65 clotted samples (7.9%), highlighting that general inpatient wards also experience substantial pre-analytical variability. Meanwhile, the Male Surgical Unit (MS) and the Surgical Intensive Care Unit (SIC) exhibited comparable frequencies, each accounting for 46 clotted samples (5.6%). The similarity between these two units may reflect shared procedural workflows and similar patient management pathways. Beyond these major contributors, the remaining hospital units demonstrated clotting-rejection frequencies ranging from 4.5% down to 0.1%. Although individually smaller in proportion, these cumulative contributions underscore that pre-analytical coagulation is a pervasive issue affecting nearly all clinical units to varying degrees.

Collectively, these results highlight the need for targeted intervention strategies, particularly in units with the highest rejection rates, such as NIC, PED1, and MIC. These findings also emphasize the importance of continuous staff training, optimization of phlebotomy techniques, and reinforcement of standardized blood collection protocols to reduce the incidence of clotted samples and improve laboratory quality metrics across the institution.

3. Discussion
A testing cycle is defined as a set of procedures that include choosing the appropriate test, gathering samples, transporting samples safely to the laboratory, analyzing the samples, and then having the clinical team interpret the results. Pre-analytical, analytical, and post-analytical are the three primary phases of this process. Any flaw throughout the entire process or at any particular point could have an impact on the final results, which would then have an impact on the standard of laboratory services [7-9]. Up to two thirds of sample processing errors have been found to occur during the pre-analytical phase, despite the possibility of errors occurring during any of the three phases [10]. Therefore, it is generally accepted that in order to ensure the best standard procedure and to minimize or maybe prevent errors, standard practices must be applied at the pre-analytical phase process (10). Phlebotomy is regarded as a critical procedure where the majority of pre-analytical errors might occur, leading to unfavorable feedback about sample quality, result reports, and the physician’s decision regarding diagnostic and treatment strategies based on these findings. Based on that, the purpose of this project was to examine KAUH’s quality service before laboratory arrival and its impact on hematology laboratory services.

Table 1 shows that throughout a six-month period, roughly 0.66% (1,092) of the samples received in the hematology laboratory were rejected due to multiple pre-analytical mistakes. Despite being a small proportion, this could add up to over 1.5% or more every year. It should be noted that only samples from a hematological lab were included in this category of rejected samples. As a result, the overall proportion of all laboratories prompts us to consider this tiny statistic more deeply and to take further steps that enable the prevention of such inappropriate behavior. For example, providing effective training programs for phlebotomists and nursing staff could play a crucial role in increasing the professionalism of the service provided and in turn reducing the percent of errors.

As previously mentioned, of the eight pre-analytical characteristics recorded in the hematological laboratory, clotted samples accounted for roughly 825 samples, or 75.5%, of the samples that were rejected. Inadequate EDTA, particularly in homemade vials, incorrect sample mixing, and prolonged blood retention in a syringe due to the absence of an anticoagulant are major causes of sample clotting [7, 8]. When clotted samples are processed, tiny capillaries inside instruments get blocked and coagulation and CBC test findings typically vary significantly [8]. Inadequate quantity (205 samples, 18.8%) was found primarily in pediatric patients, where sampling can occasionally be quite challenging, particularly if done by unskilled workers. Additionally, diluted samples have been suggested to be another reason for rejection in the pediatric age range [7]. This supported our findings because pediatric and intensive care unit clinics accounted for the majority of the samples that were rejected due to Q.N.S. Following this, the primary cause of incorrect tubes (32 samples, 2.9%) is a phlebotomist’s lack of experience or inability to identify the right tube for the necessary test. Over-filling (21 samples, 1.9%) can happen when a phlebotomist first collects blood in a syringe and then applies pressure to the syringe plunger while transferring to the tube, or when the tube’s mechanism malfunctions and it continues to drag blood when it reaches the maximum capacity that can be collected or the appropriate volume. In our investigation, double order (4 samples, 0.37%) is a rare issue that arises when a fresh test is requested from a sample that has already been registered and the volume is insufficient to handle. Hemolysis (2 samples, 0.18%) in vitro is mostly dependent on how the blood samples are taken and handled. Additionally, it can specifically depend on the blood being pushed into a tube using a syringe’s large-bore needle or an excessively small needle. Shaking the tube vigorously and/or centrifuging blood samples before clotting is finished could be another cause [10-16]. The RBC parameters could be impacted by such a mistake. The least common errors in our analysis were mislabeled and incorrect barcodes (0.18% and 0.091%, respectively). It is the phlebotomist’s or nurse’s obligation to tape confusing labels or barcodes on tubes. In order to prevent the release of findings for the mismatched patient, mislabeled samples were discarded because the patient’s identity was unknown.

Neonatal blood collection is regarded as a major difficulty for phlebotomists; in order to minimize the neonate’s suffering and obtain a safe blood collection, they need to be highly skilled and knowledgeable about the use of unsettling instruments [17]. Neonates in the neonatal intensive care unit (NIC) are frequently tested for coagulation disorders due to suspected bleeding disorders [18], as well as complete blood counts (CBCs) [19]. Due to the use of tiny diameter needles, small volume tubes, and challenges in correctly mixing the blood with the anticoagulant, newborn samples are more likely to be hemolyzed, insufficient volume, and clotted [17]. A fresh sample is ordered since clotted samples cannot be analyzed [18]. As a good practice and a requirement of the College of American Pathologists (CAP), sample recollection rate is a quality indicator that is counted every month and distributed among phlebotomists to provide information on their execution [14,16,17].

In order to reduce such pre-analytical errors, the hematology laboratory, being a Canadian recognized laboratory, has tight guidelines for accepting and receiving samples as part of its quality control program. The intra-laboratory QC program must incorporate at least minimal monitoring and control procedures at every stage, from blood specimen collection to real processing and analysis to result reporting, in order to guarantee high-quality patient care. All relevant staff should be competent and well-trained; the protocol should be written succinctly and in plain language; the process manual should provide all relevant information and references; and sufficient facilities and time should be available for QC. A successful QC program must also include continuing education [20].

4. Conclusions
Since analytical and post-analytical errors have lately been prevented by using advanced instrument technology and hospital networking systems, it is generally agreed that pre-analytical errors account for the bulk of errors that occur in all hospital laboratories. Although pre-analytical variables typically do not directly hurt patients, they frequently lead to incorrect diagnoses and treatments, hospital financial losses, and higher staff workloads. In order to increase efficiency, it is imperative that we change the way we think about healthcare. In fact, clinical laboratories are powerless to directly prevent these kinds of mistakes in sample collection, transportation, receipt, and storage. Therefore, the laboratories use stringent standards for sample mistake detection before analysis in order to guarantee high-quality service. Clotted samples are the most common pre-analytical error recorded in the laboratory, according to data from the hematology lab at KAUH. Additionally, the most frequent medical facility associated with this pre-analytical variable was the newborn intensive care unit (NICU). Better coordination between laboratories and clinical wards, ongoing medical education programs for laboratory personnel, phlebotomists, porters, and nurses, computerization of the laboratories, and staff competency checks are all thought to be ways to address issue.

5. Methods
5.1. Data sources: This contemplative study was carried out in the Hematology Laboratory of King Abdulaziz University Hospital (KAUH), which is a governmental high hospital volume with a capacity of approximately 845 beds [7]. Routinely, samples receive in the hematology lab from KAUH outpatient department (OPD), inpatient department (IPD), emergency room (ER) and from other related hospital units including other KAUH laboratories and medical clinics. Samples are collected using BD Vacutainer® blood collection tubes by a trained team of hospital nurses and technologist [8]. Additionally, information of KAUH facilities, OPD, IPD, laboratories and other services is linked via a hospital computer networking system called Phoenix System. This computer system is simplifying tracking samples prior receiving in the laboratory, placing test request within system, releasing test results and reveals patient history, which is important for a quality control tool, delta check analysis. Through this tool, a comparison between obtained recently laboratory test results and previous results of same patient will be checked and analyzed. Delta checks are particularly useful for detecting errors in specimen identification, specimen benignity, manual data entry, or possible analytical errors. Unfortunately, these quality control issues usually not detected by testing QC materials [9].

5.2. Data collections: KAUH laboratories are Canadian accredited and as a part of standards and quality policy, the hematology laboratory is recording all rejected samples with informing clinics about the exact specimen issue for sending another sample. In this study, the data was collected manually from the laboratory designed problem log sheet for over a period of 6 months from January 2017 to Jun 2017. The collected data was represented and analyzed using a Microsoft Excel software. The collected samples were covered complete blood count (CBC), differential, coagulation, miscellaneous and special hematology tests.

5.3. Pre-analytical variables: The most common pre-analytical variables reported in the hematology lab at KAUH, which affect the results, were classified as:

i. Clotted sample: clotting occurs in a tube containing an anticoagulant.

ii. Quantity not sufficient: the blood volume is not sufficient to the proper sample/anticoagulant ratio.

iii. Over-filled tube: the blood volume is more than the appropriate sample/anticoagulant ratio.

iv. Wrong tube: the tube is not suitable (the best of choice) for test order.

v. Mislabeled: a sample with no barcode.

vi. Double order: a test ordered twice on same sample and test request.

vii. Wrong barcode: a sample with mismatched patient identity.

viii. Hemolyzed sample: a sample with lysis RBCs.

5.4. Research approval: This study was approved by the representative of FAMS (the project supervisor), and the supervisor of the hematology laboratory at KAUH. An additional approval has been also obtained from the head of KAUH laboratories to confirm the legal process of collecting our data.

Author Contributions: Conceptualization, A.J., A.M., and R.Q.; methodology, R.Q.; software, A.J., A.M., and R.Q.; formal analysis, A.J., A.M., and R.Q.; investigation, R.Q.; resources, A.J., A.M., and R.Q.; data curation, A.J., A.M., and R.Q.; writing—original draft preparation, A.J., A.M., and R.Q.; writing—review and editing, A.J., A.M., and R.Q.; visualization, A.J., A.M., and R.Q.; supervision, R.Q.; project administration, R.Q.; funding acquisition, R.Q. The authors have read and agreed to the published version of the manuscript.

Funding: Not applicable.

Acknowledgments: We are grateful to the Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia and Hematology Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia for providing us all the facilities to carry out the entire work.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement: We have already mentioned in details in the method section.

Informed Consent Statement: We have already mentioned in details in the method section.

Data Availability Statement: All the related data are supplied in this work or have been referenced properly.

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