Vol 4, No 1 (2026): Current Issue (Volume 4, Issue 1), 2026
Editorial
Evolving Challenges in Modern Qualitative Research
Snur Othman
Qualitative research works at revealing the depth of human experiences, cultural nuances, and complex social dynamics, yet it confronts formidable challenges including pervasive researcher subjectivity, methodological inconsistencies, ethical intricacies, resource burdens, data management overload, and struggles with establishing rigor and transferability that often invite skepticism from quantitative paradigms. These obstacles not only complicate the research process but also threaten the perceived validity and broader applicability of findings in fields like health, education, and social sciences. Addressing them requires deliberate strategies to fortify qualitative inquiry's contributions to knowledge [1].
Subjectivity and Researcher Bias
The interpretive essence of qualitative research inherently invites researcher bias, as personal worldviews, cultural backgrounds, and preconceptions influence every stage from question formulation to data interpretation. For example, during thematic analysis of interviews, a researcher's emphasis on certain participant quotes might overlook contradictory evidence, leading to unbalanced narratives. Mitigation strategies like reflexivity where researchers explicitly document their influences and triangulation, cross-verifying data from multiple sources, prove essential, though full elimination of subjectivity remains impractical in this paradigm [2].
Methodological Design and Rigor Hurdles
Crafting a robust qualitative design demands precise alignment between philosophical underpinnings, research questions, and methods such as phenomenology, grounded theory, or discourse analysis, yet mismatches frequently occur due to insufficient expertise. Determining data saturation when new data yields no fresh insights relies on subjective judgment, complicating claims of completeness, while ensuring transferability to other contexts necessitates detailed "thick descriptions" of participants and settings. In health research, these issues amplify without clear audit trails, prompting calls for standardized rigor criteria akin to quantitative benchmarks [3].
Data Collection and Management Complexities
Gathering qualitative data through prolonged interviews, focus groups, or ethnographies generates vast, unstructured volumes of transcripts, field notes, and multimedia that overwhelm storage, organization, and preliminary sorting. Logistical barriers, like recruiting hard-to-reach participants or adapting to virtual formats, further delay progress, while ensuring consistency across sessions proves elusive without rigid protocols. Digital tools offer relief for transcription and initial coding, but they demand technical proficiency and risk diluting contextual richness if misapplied [1-4].
Analysis and Interpretation Demands
Transforming raw qualitative data into coherent themes involves iterative coding, pattern identification, and narrative synthesis, a labor-intensive process prone to interpretive drift among team members. Balancing depth with transparency challenges researchers, especially when handling ambiguous or contradictory data, and emerging AI aids accelerate this but introduce concerns over algorithmic bias eroding human insight. Peer debriefing, inter-coder reliability checks, and software like NVivo enhance trustworthiness, yet the time investment often months strains projects and underscores the need for advanced training [4].
Ethical, Practical, and Interdisciplinary Tensions
Ethical navigation intensifies in qualitative work due to intimate participant interactions, raising issues like securing ongoing consent, safeguarding anonymity in sensitive topics, and managing power imbalances with vulnerable groups. Practical constraints, including high costs for fieldwork and participant fatigue, compound these, while interdisciplinary skepticism particularly from STEM fields questions replicability and generalizability. Mixed-methods integration and decolonial approaches that center marginalized voices offer bridges, but they require institutional support and evolved review board processes [5].
Emerging Trends and Solutions
Technological innovations like AI-driven analysis and big data integration promise efficiency, yet they challenge traditional methodological purity and amplify ethical risks around data privacy. Postqualitative and indigenous methodologies push boundaries by rejecting linear processes, fostering inclusivity amid globalization. Researchers advance by prioritizing comprehensive training, open-access protocols for auditability, and collaborative networks to elevate qualitative work's stature and impact.
Conflicts of interest: The author has no conflicts of interest to disclose.
Original Articles
Could first-trimester bleeding affect a newborn's Apgar score?
Leila Sekhavat, Atiyeh Javaheri
Abstract
Introduction
Vaginal bleeding is a common complication during pregnancy and may contribute to adverse pregnancy outcomes. This study aimed to evaluate the effect of first trimester bleeding on newborns Apgar scores.
Methods
A retrospective study was conducted on pregnant women who delivered at Shahid Sadoughi hospital in Yazd, Iran, between 2022 and 2023. Only singleton, nulliparous, non-diabetic women were included. Participants were divided into two groups: the exposure group (Bleeding Group) and control Group (Non-Bleeding Group), based on archived records. Apgar scores recorded at the first and fifth minutes after birth in newborns file were compared between groups.
Results
A total of 992 women were included, with 218 in the exposure and 774 in the control groups. The incidence of a first-minute Apgar score <7 was significantly higher in the bleeding group compared to controls (22.5% vs. 6.2%, p = 0.02). However, there was no significant difference in five-minute Apgar scores between groups.
Conclusion
This study demonstrated a positive association between first-trimester vaginal bleeding and a low first-minute Apgar score in newborns.
Introduction
The first trimester of pregnancy is a crucial period of fetal development, during which the body undergoes significant physiological changes to support the growing baby [1]. Maternal and fetal well-being are closely interconnected, and multiple factors influence fetal growth and metabolic programming.
First trimester bleeding defined as vaginal bleeding occurring between conception and 12 weeks of gestation. It is common and affecting between 16 - 25% of all pregnancies and often causes anxiety for both patients and clinicians [2-4]. Although many pregnancies with first-trimester bleeding progress without complication, emerging evidence suggests an increased risk of neonatal complications later in pregnancy [5,6]. The Apgar score, developed by Dr. Virginia Apgar in 1952, is a rapid and reliable method of assessing newborn condition and clinical status immediately after delivery [7-9]. The score is reported at one and five minutes after birth and, if below 7, at five-minute intervals up to 20 minutes [10]. Approximately 1% of low-risk live births have a five-minute Apgar score below 7, which is associated with a significantly higher risk of neonatal morbidity and mortality [11]. Low Apgar scores have also been linked to long-term adverse outcomes such as epilepsy, cerebral palsy, and developmental delays [12-14].
Numerous studies have investigated the relationship between first-trimester bleeding and neonatal health. Some of these studies indicate a correlation between first-trimester bleeding and low Apgar scores at the first and fifth minutes after birth [5,15-17]. Conversely, other studies have reported that first-trimester bleeding has no effect on newborn Apgar scores [18-21].
Several studies have explored the association between first-trimester bleeding and neonatal outcomes, with mixed results. Some found a correlation between early bleeding and low Apgar scores [5,15–17]. The others reported no significant relationship [18–21]. Some evidence suggests that only when bleeding results in complications such as intrauterine growth restriction (IUGR), preterm birth, or low birth weight does it significantly affect neonatal Apgar scores [5,22,23].
This study aims to further investigate the relationship between first-trimester bleeding and neonatal Apgar scores.
Methods
Study design and setting
This study was designed as a retrospective study. Data were collected from archived files at the hospital and medical records of pregnant individuals who delivered at Shahid Sadoughi hospital in Yazd, Iran, during one year.
Inclusion criteria
This study included singleton pregnancies that resulted in the delivery of live newborns at or beyond 37 weeks of gestation, with a birth weight greater than 2500 grams.
Exclusion criteria
Pregnancies were excluded if there were fetal anomalies, chronic maternal diseases such as diabetes mellitus, hypertension, renal, cardiac, or endocrine disorders, or any history of smoking or drug abuse. Additional exclusions included surgical conditions during pregnancy, multiple gestations, placental abruption or placenta previa in the later trimesters, and cases with incomplete medical records.
Grouping and data collection
Participants were categorized into two groups: an exposure group, consisting of pregnancies complicated by first-trimester vaginal bleeding, and a control group, comprising pregnancies without first-trimester bleeding. All included women were under 40 years of age. Demographic characteristics such as occupation, economic status, educational level, and maternal body mass index (BMI) were obtained. Clinical data were extracted from archived hospital records and patients' files, including obstetric history and detailed documentation of any first-trimester bleeding episodes. First-trimester vaginal bleeding was defined as bleeding occurring before 12 weeks of gestation in the presence of a closed cervix and a viable intrauterine pregnancy. Newborn outcomes, including Apgar scores at one and five minutes, were also retrieved from medical records. An Apgar score <7 at either time point was considered low, with scores classified as normal (>7), low (5–7), or very low (<5).
Statistical analysis
Data were analyzed using SPSS version 20. Continuous variables were compared using the t-test, while categorical variables were assessed using the chi-square test. A p-value of <0.05 was considered statistically significant.
Results
A total of 992 term singleton pregnancies were included in the analysis, comprising 218 women in the exposure (bleeding) group and 774 in the control group. Maternal and neonatal characteristics of both groups are presented in (Table 1).
|
Maternal characteristics |
Exposure group (first trimester bleeding) N = 218 |
Control group (without bleeding) N= 774 |
P-value |
|
Age in years N (%) <20 20 – 30 31 – 40 |
45 (20.6) 146 (67) 27 (12.4) |
155 (20) 513 (66.3) 106 (13.7) |
0.2 |
|
Body mass index (kg/m2) N (%) <18 18– 25 >25 |
38 (17.4) 145 (66.5) 35 (16.1) |
148 (19.1) 490 (63.3) 136 (17.6) |
0.1 |
|
Employment N (%) Yes No |
98 (44.9) 120 (55.1) |
379 (49) 395 (51) |
0.7 |
|
Educational level < 12 >12 |
66 (30.3) 152 (69.7) |
241 (31.1) 533 (68.9) |
0.4 |
|
Prenatal care Adequate Inadequate |
99 (45.4) 119 (54.6) |
363 (46.9) 411 (53.1) |
0.3 |
Newborns in the bleeding group had a significantly higher proportion of first-minute Apgar scores <7 compared with the control group (22.5% vs. 6.2%, p = 0.02). Although five-minute Apgar scores <7 were also more common among the bleeding group (8.7% vs. 6.7%), this difference did not reach statistical significance (p = 0.6) (Table 2).
| Neonatal Apgar score |
Exposure group (first trimester bleeding) N = 218 |
Control group (without bleeding) N= 774 |
P-value |
|
First min APGAR scores N (%) <7 >7 |
49 (22.5) 169 (77.5) |
48 (6.2) 726 (93.8) |
0.02 |
|
5 min after birth APGAR scores N (%) <7 >7 |
19 (8.7) 199 (91.3) |
52 (6.7) 722 (93.3) |
0.6 |
Women who experienced bleeding lasting more than two days had a greater frequency of low first-minute Apgar scores (65.3%) than those with shorter-duration bleeding (36.7%); however, this trend was not statistically significant (p = 0.06). Similarly, multiple bleeding episodes were associated with a higher proportion of low Apgar scores compared with single episodes, but without statistical significance (p = 0.07) (Table 3).
|
Characteristics |
First min APGAR scores <7 N=49 |
P-value |
|
Bleeding episode N (%) Single Multiple |
19 (38.8) 30 (61.2) |
0.07 |
|
Duration (days) N (%) 1– 2 > 2 |
18 (36.7) 31 (65.3) |
0.06 |
The mean birth weight of newborns was 2891 ± 539 g. While low birth weight was more common in the bleeding group, the difference was not statistically significant (Table 4). Overall, these findings suggest that first-trimester vaginal bleeding is associated with an increased risk of a low first-minute Apgar score at birth.
|
Neonatal birth weight (gm) |
Exposure group (first trimester bleeding) N = 218 |
Control group (without bleeding) N= 774 |
P-value |
|
LBW (<2500) N (%) |
48 (22) |
147 (19) |
0.07 |
|
Normal weight (2500-4000) N (%) |
159 (72.9) |
563 (72.7) |
0.4 |
|
Macrosomia (> 4000) N (%) |
11 (5.1) |
64 (8.3) | 0.2 |
Discussion
This study found a significant association between first-trimester vaginal bleeding and low one-minute Apgar scores. The Apgar score is a key indicator of neonatal health, and lower values often reflect perinatal distress and risk of complications such as hypoxic-ischemic encephalopathy and NICU admission [10,12].
One of the most significant findings in this study was Neonates born to mothers with first-trimester bleeding were more likely to have a one-minute Apgar <7 (22.5% vs. 6.2%, p = 0.02). This finding is consistent with previous research suggesting that early pregnancy bleeding may compromise fetal growth and lead to neonatal distress [5,6,15-17,24].
Karimi et al. reported in their meta-analysis that vaginal bleeding during pregnancy is a risk factor for adverse outcomes, including low Apgar scores and preterm birth [5]. Bever et al. found that first-trimester bleeding was linked to altered fetal growth patterns, which can contribute to neonatal distress and first minute low Apgar [6]. Some of these studies indicate a correlation between first-trimester bleeding and low Apgar scores at one and five minutes of birth [15-17, 24].
The underlying mechanism may involve placental dysfunction. Early bleeding may indicate subchorionic hematoma or implantation abnormalities, which can reduce placental efficiency and lead to fetal hypoxia. Gaillard et al. [1] reported that placental dysfunction adversely affects fetal growth and development, potentially manifesting as low Apgar scores. Maternal inflammation during early bleeding episodes may also negatively influence fetal development [15].
Although, some studies conversely have reported that first-trimester bleeding has no effect on newborn Apgar scores [18- 21], therefore, it seems that more studies are needed in this objective.
The absence of a significant difference in five-minute Apgar scores in our study the groups (bleeding: 8.7% vs. control: 6.7%, p = 0.6) suggests that prompt neonatal care and resuscitation may mitigate initial distress. This finding is consistent with Chen et al, who suggested that while low five-minute Apgar scores are predictive of long-term adverse outcomes, short-term resuscitation efforts often improve neonatal condition [11]. Current guidelines from the American Academy of Pediatrics recommend that neonates with low Apgar scores receive immediate and thorough evaluation to mitigate the risks associated with potential perinatal asphyxia [7].
Longer or recurrent bleeding episodes appeared to increase the risk of low Apgar scores, though not significantly. Chandrakala and Reshmi similarly noted that recurrent bleeding episodes often indicate placental dysfunction and can contribute to perinatal morbidity [24].
Although low birth weight was more common in the bleeding group, the difference was not statistically significant, differing from studies by Karimi et al, and Velez et al, possibly due to differences in population size and inclusion criteria [5,22]. The discrepancy may be attributed to variations in study populations, sample sizes, and differing criteria for defining low birth weight.
Conclusion
This study underscores the importance of vigilant prenatal monitoring in pregnancies affected by early first-trimester bleeding. Further investigations are required to identify predictors of adverse neonatal outcomes and to develop preventive measures that may enhance newborn health.
Declarations
Conflicts of interest: The authors have no conflicts of interest to disclose.
Ethical approval: The study was approved by the Institutional Ethics Committee and utilized data obtained from hospital archives.
Patient consent (participation and publication): Was obtained from all participants prior to completing the questionnaire, and participants were assured of confidentiality and anonymity.
Funding: The present study received no financial support.
Acknowledgements: The authors thank the residents of the Obstetrics and Pediatrics Departments at Shahid Sadoughi University of Medical Sciences, Yazd, Iran, for their assistance in data collection, and the Department of Statistics for support with data analysis.
Authors' contributions: LS Contributed to drafting the manuscript and critically revising its content, and approved the final version prior to submission. AJ Responsible for data acquisition, study conception and design, as well as data analysis and interpretation. All authors read and approved the manuscript.
Use of AI: AI was not used in the drafting of the manuscript, the production of graphical elements, or the collection and analysis of data.
Data availability statement: Not applicable.
Impact of Common Anticoagulants on Complete Blood Count Parameters Among Humans
Rawezh Q. Salih, Dahat A. Hussein, Sharaza Q. Omer, Shvan L. Ezzat, Ayman M. Mustafa, Hawnaz S....
Abstract
Introduction
Among the most frequently used anticoagulants in hematological testing are tetra-acetic acid (EDTA), sodium citrate, and sodium heparin. However, there is a noticeable gap in literature concerning the effects of these anticoagulants on hematological parameters specifically in humans. This study aims to assess the effectiveness of EDTA, sodium citrate, and sodium heparin for conducting complete blood count (CBC).
Methods
This cross-sectional study conducted at Smart Health Tower from January to April 2024 involved 250 participants who underwent CBC using K2EDTA, sodium citrate, and sodium heparin. The acquired data were analyzed using SPSS, with a significance level of p < 0.05, employing Intra-class correlation coefficient and one-way ANOVA to assess consistency and agreement among anticoagulants.
Results
A total of 250 participants, with 138(55.2%) males and 112(44.8%) females, underwent CBC testing with di potassium EDTA(K2EDTA), sodium citrate, and sodium heparin. Comparing K2EDTA with sodium heparin showed comparable values in 14 out of 23(60.87%) CBC parameters. Using K2EDTA as the standard, citrate showed perfect or substantial agreement in assessing 8 out of 23 CBC parameters (34.78%). Regarding the comparison of anticoagulants to K2EDTA to determine their agreement levels while sodium heparin was accurate and precise in 13(56.52%) parameters.
Conclusion
Citrate was found to be a less reliable anticoagulant for CBC estimation compared to K2EDTA, potentially leading to inaccurate readings. On the other hand, sodium heparin showed comparable performance to K2EDTA, making it a suitable alternative under specific conditions.
Introduction
The Complete Blood Count (CBC) is a widely requested blood test by clinicians, assessing the total quantities and characteristics of cellular constituents within the bloodstream. The CBC parameters include red blood cells (RBCs), white blood cells (WBCs), and platelets (PLTs). This comprehensive assessment includes determining the total and differential count of WBCs, also measuring RBC count, hemoglobin (HGB) levels, and hematocrit (HCT), as well as their indices such as mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular hemoglobin (MCH), and red cell distribution width (RDW). Additionally, CBC evaluates platelet (PLT) count indices [1,2]. A CBC serves as an important diagnostic tool for assessing human health, detecting congenital abnormalities, and identifying functional changes due to various pathological factors [3]. Its findings can reveal various conditions such as infections with elevated WBC counts, leukemia with abnormal WBC counts, anemia with low HGB levels, and liver cirrhosis with reduced PLT counts. Recent studies suggest that specific combinations of CBC components, along with derived secondary results, can predict risks of different diseases like cardiovascular disease, cancer, type 2 diabetes, and metabolic syndrome [1,2].
It's widely recognized that collection and sampling of blood, laboratory techniques and storage conditions, and the choice of anticoagulant can substantially impact the outcomes derived from hematological analysis, especially CBC results [4]. Among the various anticoagulants used for both sample collection and routine laboratory analysis, the most commonly utilized ones in hematology are ethylene diamine tetra acetic acid (EDTA), citric acid salts, sodium and lithium oxalates, and heparin [5,6].
The National Committee for Clinical Laboratory Standards has suggested using EDTA for CBC due to its ability to preserve cell structure [7]. However, limited evidence exists on the effects of other anticoagulants on CBC parameters among animal species. Among humans, heparin is typically avoided for blood smears and WBC counts due to staining and clotting issues, respectively. Conversely, EDTA is considered unsuitable for erythrocyte osmotic fragility assessment and may cause cell damage if overused [8].
The majority of studies documented in existing literature have focused on evaluating the impacts of different anticoagulants on CBC results or its specific components across diverse animal species. The current study aims to estimate variations in CBC parameters using different anticoagulants, employing dipotassium EDTA(K2EDTA), sodium citrate, and sodium heparin, among humans to evaluate their effectiveness.
Methods
Study Design, population, and criteria
This cross-sectional laboratory-based study was conducted at Smart Health Tower from January to April 2024. Prior to participation, all individuals were thoroughly briefed about the study and required to provide informed written consent. The study included a total of 250 participants, all of whom underwent complete blood count tests utilizing K2EDTA, sodium citrate, and sodium heparin as anticoagulants. The study population consisted of patients attending Smart Health Tower, representing both genders without any gender bias. Inclusion was restricted to those who had visited the facility, while individuals or their guardians ( in case of minors) who declined to provide consent were excluded from the study.
Determination of the sample size
The effective sample size was determined using G*Power statistic 3.1.9.7, employing linear multiple regression as the statistical test with a two-tailed approach. With an effective sample size of 0.35, an α error probability of 0.01, and a statistical power of 0.99, along with a predictor value of 1, the minimum required sample size was 158. Therefore, a sample size of 250 was utilized for the comparison in CBC parameters between these three different anticoagulants.
Sample collection and statistical analysis
Trained health workers collected blood samples from participants using sterile syringes and needles, drawing 5 mL from either the median cubital or prominent forearm vein. The samples were distributed as follows: 1.8 mL into sodium citrate tubes and 1.6 mL into K2EDTA and sodium heparin tubes. After gentle mixing, complete blood counts (CBC) were analyzed with the Medonic M51 automated hematology analyzer within 3 to 6 hours post-collection. Tube characteristics are detailed in Table 1. Various hematological parameters were assessed, including WBC, percentages of neutrophils, lymphocytes, monocytes, eosinophils, basophils, as well as RBC, HCT, HGB, MCV, MCHC, RDW-SD, PLT, MPV, PDW, PCT, and PLCR. Participant demographics, such as age and gender, were also recorded. Data were initially processed in Microsoft Excel 2019 for accuracy and completeness before being transferred to SPSS version 25.0 and MedCalc version 20 for statistical analysis. Intra-class correlation coefficient (ICC) analysis was conducted to evaluate consistency among the three anticoagulants, with interpretations as follows: <0.50 for poor consistency, 0.50-0.75 for moderate, 0.75-0.90 for good, and >0.90 for excellent consistency. A p-value of <0.05 was considered significant. One-way ANOVA assessed variations in CBC parameters among samples collected in K2EDTA, sodium citrate, and sodium heparin tubes. Additionally, the concordance correlation coefficient (CCC) was used to evaluate agreement, with K2EDTA as the standard, and interpreted as follows: ≥0.99 for almost perfect agreement, 0.95-0.99 for significant agreement, 0.90-0.95 for moderate agreement, and <0.90 for poor agreement [9].
|
Tube details |
EDTA |
Heparin |
Citrate |
|
Type of tube |
K2EDTA |
Sodium (vacuum blood collection tube) |
PT Tube (Sodium citrate) |
|
Dimension |
13 x 75 mm |
13 x 75 mm |
13 x 75 mm |
|
Storage |
5- 25°C |
5-25°C |
5-25°C |
|
Expiration date |
31-3-2025 |
24-11-2027 |
19-12-2025 |
|
Tube capacity (volume) |
5 ml |
5 ml |
5ml |
|
Required volume |
1.5-2 ml |
1.5-2ml |
1.8ml |
|
Tube material |
Plastic |
glass |
glass |
|
Manufacturer |
Vacutest kima sri |
MR+ |
MR+ |
|
Origin/country |
Italy |
China |
China |
|
Anticoagulant concentration |
5.4 mg |
18iu |
3.2% |
Results
Among the 250 participants involved, 138 (55.2%) were male, and 112 (44.8%) were female. The participants had an average age of 41.20 ± 16.51 years (5-91). Consistency in CBC results using sodium heparin, K2EDTA, and sodium citrate indicated excellent consistency in the determination of WBC, %Neu, %Lymph, Neu, Lymph, RBC, HGB, HCT, MCV, MCH, MCHC, RDW-SD, MPV, PDW, and PLCR among these anticoagulants with ICC >0.90 (Table 2).
|
CBC parameters |
Intra-class correlation coefficient |
Confidence interval 95% |
|
|
Lower |
Upper |
||
|
WBC |
0.991 |
0.972 |
0.996 |
|
%Neu |
0.961 |
0.880 |
0.981 |
|
%Lymph |
0.987 |
0.984 |
0.990 |
|
%Mon |
0.494 |
0.038 |
0.717 |
|
%Eos |
0.869 |
0.838 |
0.895 |
|
%Bas |
0.733 |
0.636 |
0.801 |
|
Neu |
0.988 |
0.961 |
0.994 |
|
Lymph |
0.987 |
0.973 |
0.993 |
|
Mon |
0.612 |
0.120 |
0.802 |
|
Eos |
0.182 |
0.038 |
0.367 |
|
Bas |
0.803 |
0.719 |
0.858 |
|
RBC |
0.922 |
0.328 |
0.976 |
|
HGB |
0.923 |
0.321 |
0.976 |
|
HCT |
0.902 |
0.449 |
0.964 |
|
MCV |
0.998 |
0.994 |
0.999 |
|
MCH |
0.996 |
0.992 |
0.998 |
|
MCHC |
0.963 |
0.921 |
0.979 |
|
RDW-SD |
0.924 |
0.901 |
0.941 |
|
PLT |
0.536 |
0.276 |
0.689 |
|
MPV |
0.915 |
0.843 |
0.948 |
|
PDW |
0.921 |
0.880 |
0.945 |
|
PCT |
0.563 |
0.104 |
0.763 |
|
PLCR |
0.930 |
0.856 |
0.960 |
Regarding variation in CBC parameters using K2EDTA, sodium citrate, and sodium heparin, no statistically significant variation was found in the median %Lymph, Eos, MCV, and MCH among these three different anticoagulants (Table 3).
|
CBC parameters |
Sodium Heparin Median (Min-Max) |
Citrate |
K2EDTA |
P-value |
|
WBC |
7.54(2.52-26.26) |
7.21(2.26-23.85) |
7.51(2.54-26.10) |
0.046 |
|
%Neu |
61.15(37.2-92.6) |
56.70(37.60-90.90) |
56.30(30.70-91.80) |
<0.001 |
|
%Lymph |
33.2(3.8-50.30) |
33.55(4-50.50) |
33.45(3.90-52.60) |
0.718 |
|
%Mon |
1.9(0.0-12.20) |
6(0.20-13) |
6.45(0.80-14.10) |
<0.001 |
|
%Eos |
2.5(0.10-22.50) |
2.50(0.20-24.30) |
6.45(0.80-14.10) |
<0.001 |
|
%Bas |
0.65(0.20-3.10) |
0.50(0.10-2.20) |
0.50(0.10-1.50) |
<0.001 |
|
Neu |
4.92(0.94-24.31) |
4.40(0.99-21.68) |
4.65(0.78-23.96) |
0.015 |
|
Lymph |
2.41(0.39-5.76) |
2.31(0.37-5.40) |
2.49(0.45-5.99) |
0.049 |
|
Mon |
0.15(0.00-0.93) |
0.43(0.01-1.06) |
0.49(0.05-1.17) |
<0.001 |
|
Eos |
0.20(0.01-2.08) |
0.18(0.01-2.19) |
0.16(0.01-2.55) |
0.730 |
|
Bas |
0.05(0.01-0.23) |
0.04(0.01-0.17) |
0.04(0.01-0.13) |
<0.001 |
|
RBC |
5.12(2.47-7.65) |
4.62(2.21-6.35) |
5.13(2.48-7.05) |
<0.001 |
|
HGB |
14.2(6.90-21.30) |
12.6(6.20-16.9) |
14.10(6.90-18.20) |
<0.001 |
|
HCT |
43.15(21.4-64.8) |
38.90(19-51.20) |
43.45(3.72-54.60) |
<0.001 |
|
MCV |
85.45(56.90-108.5) |
85.15(56.8-108.4) |
85.9(57.3-108.6) |
0.534 |
|
MCH |
28.4(18.10-35.70) |
28.10(18-37.5) |
28.25(18.10-36.40) |
0.425 |
|
MCHC |
33(30.50-37.60) |
32.70(30.4-39.10) |
32.60(30-38.40) |
<0.001 |
|
RDW-SD |
43.5(34.70-63.10) |
43.40(34.60-64.00) |
44.10(35.30-82.20) |
0.016 |
|
PLT |
159(32-424) |
176(21-1584) |
250(86-482) |
<0.001 |
|
MPV |
9.30(6.90-12.10) |
8.80(4.40-11.80) |
9.10(7.10-13.00) |
<0.001 |
|
PDW |
11.85(7.30-21.10) |
11.10(2.60-20.00) |
11.60(8.10-23.60) |
<0.001 |
|
PCT |
0.15(0.03-0.34) |
0.15(0.02-0.70) |
0.22(0.09-0.37) |
<0.001 |
|
PLCR |
31.75(15.30-51.10) |
28.15(5.20-48.80) |
30.40(15.40-57.90) |
<0.001 |
Regarding variation in estimation of CBC parameters using the results of two anticoagulated blood such as K2EDTA-sodium citrate, K2EDTA-sodium heparin, sodium citrate-sodium heparin, the results of the comparison of K2EDTA-sodium citrate indicated comparable results in median %Neu, %Lymph, %Eos, Neu, Bas, MCV, MCH, and MCHC with a p-value of ≥0.05. Comparison of K2EDTA-sodium heparin results indicated comparable results in median WBC, %Lymph, %Eos, Neu, Lymph, RBC, HGB, HCT, MCV, MCH, RDW-SD, MPV, PDW, and PLCR with a p-value of ≥0.05. In comparing the results of CBC between sodium citrate-sodium heparin, the result indicated a nonsignificant difference in median WBC, %Lymph, %Eos, Lymph, MCV, MCH, RDW-SD, and PCT (Table 4).
|
CBC parameters |
Sodium Citrate |
Sodium Heparin |
P-value |
K2EDTA |
Sodium Heparin |
P-value |
K2EDTA |
Sodium Citrate |
P-value |
|
WBC |
7.21(2.26-23.85) |
7.54(2.52-26.26) |
0.121 |
7.51(2.54-26.10) |
7.54(2.52-26.26) |
0.941 |
7.51(2.54-26.10) |
7.21(2.26-23.85 |
0.05 |
|
%Neu |
56.70(37.60-90.90) |
61.15(37.2-92.6) |
<0.001 |
56.30(30.70-91.80) |
61.15(37.2-92.6) |
<0.001 |
56.30(30.70-91.80) |
56.70(37.60-90.90) |
0.788 |
|
%Lymph |
33.55(4-50.50) |
33.2(3.8-50.30) |
0.922 |
33.45(3.90-52.60) |
33.2(3.8-50.30) |
0.695 |
33.45(3.90-52.60) |
33.55(4-50.50) |
0.903 |
|
%Mon |
6(0.20-13) |
1.9(0.0-12.20) |
<0.001 |
6.45(0.80-14.10) |
1.9(0.0-12.20) |
<0.001 |
6.45(0.80-14.10) |
6(0.20-13) |
0.003 |
|
%Eos |
2.50(0.20-24.30) |
2.5(0.10-22.50) |
0.801 |
6.45(0.80-14.10) |
2.5(0.10-22.50) |
0.994 |
6.45(0.80-14.10) |
2.50(0.20-24.30) |
0.740 |
|
%Bas |
0.50(0.10-2.20) |
0.65(0.20-3.10) |
<0.001 |
0.50(0.10-1.50) |
0.65(0.20-3.10) |
<0.001 |
0.50(0.10-1.50) |
0.50(0.10-2.20) |
0.07 |
|
Neu |
4.92(0.94-24.31) |
4.92(0.94-24.31) |
0.011 |
4.65(0.78-23.96) |
4.92(0.94-24.31) |
0.288 |
4.65(0.78-23.96) |
4.92(0.94-24.31) |
0.345 |
|
Lymph |
2.41(0.39-5.76) |
2.41(0.39-5.76) |
0.282 |
2.49(0.45-5.99) |
2.41(0.39-5.76) |
0.633 |
2.49(0.45-5.99) |
2.41(0.39-5.76) |
0.040 |
|
Mon |
0.15(0.00-0.93) |
0.15(0.00-0.93) |
<0.001 |
0.49(0.05-1.17) |
0.15(0.00-0.93) |
<0.001 |
0.49(0.05-1.17) |
0.15(0.00-0.93) |
<0.001 |
|
Eos |
0.20(0.01-2.08) |
0.20(0.01-2.08) |
<0.001 |
0.16(0.01-2.55) |
0.20(0.01-2.08) |
<0.001 |
0.16(0.01-2.55) |
0.20(0.01-2.08) |
<0.001 |
|
Bas |
0.05(0.01-0.23) |
0.05(0.01-0.23) |
<0.001 |
0.04(0.01-0.13) |
0.05(0.01-0.23) |
<0.001 |
0.04(0.01-0.13) |
0.05(0.01-0.23) |
0.547 |
|
RBC |
5.12(2.47-7.65) |
5.12(2.47-7.65) |
<0.001 |
5.13(2.48-7.05) |
5.12(2.47-7.65) |
0.993 |
5.13(2.48-7.05) |
5.12(2.47-7.65) |
<0.001 |
|
HGB |
14.2(6.90-21.30) |
14.2(6.90-21.30) |
<0.001 |
14.10(6.90-18.20) |
14.2(6.90-21.30) |
0.816 |
14.10(6.90-18.20) |
14.2(6.90-21.30) |
<0.001 |
|
HCT |
43.15(21.4-64.8) |
43.15(21.4-64.8) |
<0.001 |
43.45(3.72-54.60) |
43.15(21.4-64.8) |
0.999 |
43.45(3.72-54.60) |
43.15(21.4-64.8) |
<0.001 |
|
MCV |
85.45(56.90-108.5) |
85.45(56.90-108.5) |
0.874 |
85.9(57.3-108.6) |
85.45(56.90-108.5) |
0.808 |
85.9(57.3-108.6) |
85.45(56.90-108.5) |
0.503 |
|
MCH |
28.4(18.10-35.70) |
28.4(18.10-35.70) |
0.391 |
28.25(18.10-36.40) |
28.4(18.10-35.70) |
0.773 |
28.25(18.10-36.40) |
28.4(18.10-35.70) |
0.807 |
|
MCHC |
33(30.50-37.60) |
33(30.50-37.60) |
0.009 |
32.60(30-38.40) |
33(30.50-37.60) |
<0.001 |
32.60(30-38.40) |
33(30.50-37.60) |
0.502 |
|
RDW-SD |
43.5(34.70-63.10) |
43.5(34.70-63.10) |
0.709 |
44.10(35.30-82.20) |
43.5(34.70-63.10) |
0.110 |
44.10(35.30-82.20) |
43.5(34.70-63.10) |
0.014 |
|
PLT |
159(32-424) |
159(32-424) |
0.001 |
250(86-482) |
159(32-424) |
<0.001 |
250(86-482) |
159(32-424) |
<0.001 |
|
MPV |
9.30(6.90-12.10) |
9.30(6.90-12.10) |
<0.001 |
9.10(7.10-13.00) |
9.30(6.90-12.10) |
0.211 |
9.10(7.10-13.00) |
9.30(6.90-12.10) |
<0.001 |
|
PDW |
11.85(7.30-21.10) |
11.85(7.30-21.10) |
<0.001 |
11.60(8.10-23.60) |
11.85(7.30-21.10) |
0.207 |
11.60(8.10-23.60) |
11.85(7.30-21.10) |
0.019 |
|
PCT |
0.15(0.03-0.34) |
0.15(0.03-0.34) |
0.022 |
0.22(0.09-0.37) |
0.15(0.03-0.34) |
<0.001 |
0.22(0.09-0.37) |
0.15(0.03-0.34) |
<0.001 |
|
PLCR |
31.75(15.30-51.10) |
31.75(15.30-51.10) |
<0.001 |
30.40(15.40-57.90) |
31.75(15.30-51.10) |
0.143 |
30.40(15.40-57.90) |
31.75(15.30-51.10) |
0.001 |
The agreement levels between different anticoagulants, using K2EDTA as the standard, were evaluated. Sodium citrate showed perfect agreement in assessing MCV and MCH (CCC = 0.990) but displayed significant agreement in determining WBC, %Neu, %Lymph, Neu, Lymph, and Eos (CCC between 0.95 and 0.99). Moderate agreement was observed in assessing MCHC (CCC = 0.929), while poor agreement was found in all other parameters with CCC<0.90. Similarly, sodium heparin demonstrated perfect agreement in determining MCV (CCC=0.994) and MCH (CCC=0.990), with substantial agreement in other parameters such as WBC, %Lymph, Neu, Lymph, RBC, and HGB (CCC between 0.95 and 0.99), but poor agreement in parameters with CCC<0.90. Regarding the comparison of K2EDTA and sodium citrate, citrate was highly precise and accurate in the estimation of WBC, %Neu, %Lymph, Neu, Lymph, Eos, MCV, MCH, and MCHC. While comparing sodium heparin to K2EDTA, it was highly precise in the estimation of WBC, %Neu, %Lymph, Neu, Lymph, Eos, RBC, HGB, HCT, MCV, MCH, MCHC, and PLCR (Table 5).
|
CBC parameters |
K2EDTA-Citrate |
Pearson ρ (precision) |
Accuracy |
K2EDTA-Sodium heparin |
Pearson ρ (precision) |
Accuracy |
|
WBC |
0.97(0.9571 -0.9718) |
0.988 |
0.977 |
0.985(0.981- 0.989) |
0.986 |
0.999 |
|
%Neu |
0.972(0.964-0.978) |
0.974 |
0.998 |
0.847(0.814-0.875) |
0.925 |
0.916 |
|
%Lymph |
0.984(0.980- 0.988) |
0.985 |
0.999 |
0.951(0.937- 0.961) |
0.954 |
0.997 |
|
%Mon |
0.663(0.593 - 0.723) |
0.705
|
0.941 |
0.157(0.111 -0.201) |
0.440 |
0.355 |
|
%Eos |
0.636(0.567 - 0.697) |
0.688 |
0.926 |
0.594(0.525 - 0.656) |
0.675 |
0.881 |
|
%Bas |
0.460(0.361 -0.548) |
0.480 |
0.958 |
0.305(0.209 - 0.396) |
0.371 |
0.822 |
|
Neu |
0.980(0.975 - 0.984) |
0.990 |
0.990 |
0.972(0.964 - 0.978) |
0.980 |
0.991 |
|
Lymph |
0.956(0.949 - 0.966) |
0.984 |
0.974 |
0.969(0.961 - 0.976) |
0.973 |
0.997 |
|
Mon |
0.733(0.675 - 0.782) |
0.795 |
0.922 |
0.209(0.157 - 0.261) |
0.500 |
0.419 |
|
Eos |
0.968(0.960 - 0.975) |
0.973 |
0.995 |
0.927(0.909 - 0.941) |
0.942 |
0.983 |
|
Bas |
0.565(0.477 - 0.642) |
0.576 |
0.990 |
0.458(0.371 -0.537) |
0.543 |
0.848 |
|
RBC |
0.723(0.691 - 0.764) |
0.983 |
0.742 |
0.973(0.966-0.979) |
0.974 |
0.999 |
|
HGB |
0.742(0.705 - 0.775) |
0.988 |
0.752 |
0.977(0.971 - 0.982) |
0.979 |
0.998 |
|
HCT |
0.670(0.620 - 0.714) |
0.911 |
0.735 |
0.907(0.882 - 0.926) |
0.909 |
0.998 |
|
MCV |
0.990(0.987 - 0.992) |
0.995 |
0.995 |
0.994(0.992 - 0.995) |
0.995 |
0.998 |
|
MCH |
0.990(0.987 - 0.992) |
0.991 |
0.998 |
0.990(0.987 - 0.992) |
0.992 |
0.998 |
|
MCHC |
0.929(0.910 - 0.944) |
0.933 |
0.995 |
0.874(0.844 - 0.898) |
0.932 |
0.937 |
|
RDW-SD |
0.721(0.661 - 0.772) |
0.762 |
0.946 |
0.738(0.681- 0.786) |
0.771 |
0.958 |
|
PLT |
0.313(0.236 - 0.386) |
0.470 |
0.668 |
0.290(0.235 - 0.343) |
0.648 |
0.448 |
|
MPV |
0.784(0.734 - 0.826) |
0.830 |
0.945 |
0.873(0.841 - 0.899) |
0.887 |
0.985 |
|
PDW |
0.790(0.740 - 0.832) |
0.815 |
0.970 |
0.819(0.774 - 0.856) |
0.832 |
0.983 |
|
PCT |
0.319(0.256 - 0.380) |
0.598 |
0.534 |
0.238(0.187- 0.289) |
0.585 |
0.408 |
|
PLCR |
0.833(0.794- 0.866) |
0.879 |
0.948 |
0.876(0.847- 0.901) |
0.901 |
0.973 |
Discussion
The choice of anticoagulants and storage time significantly affect blood sample analysis [10]. In a study by Akorsu et al. involving 55 healthy individuals, consistency in blood parameters across three anticoagulants was observed: K3EDTA, sodium citrate, and lithium heparin [3]. Similarly, a current study utilized K2EDTA, sodium citrate, and sodium heparin, finding excellent consistency in various blood parameters, with ICC values exceeding 0.90.
Regarding variation in CBC parameters using different anticoagulants, in a study which is conducted on 30 clinically healthy dogs from different breeds, no significant variation between sodium citrate and K3EDTA was found in 4 out of 8 CBC parameters (50%) including HGB, HCT, PLT, and PCT [11]. Similarly, in a study conducted on humans, in which variation in the estimation of CBC parameters was evaluated using three different anticoagulants, namely, K3EDTA, sodium citrate, and lithium heparin, no statistically significant difference was observed in 5 out of 14 CBC parameters (35.7%) including MCV, MCH, MCHC, %Lymph, and %Neu among the three anticoagulants examined [3]. In the present study, regarding variation in CBC parameters using K2EDTA, sodium citrate, and sodium heparin, no statistically significant variation was found in 4 out of 23 CBC parameters (17.40%) including %Lymph, Eos, MCV, and MCH among these three different anticoagulants. The significant variations observed in other CBC parameters underscore the need for careful consideration when selecting anticoagulants, particularly in clinical settings where precise and consistent CBC measurements are crucial for accurate diagnosis and monitoring of conditions [12].
In a study of 50 healthy dogs comparing EDTA and sodium citrate, no comparable results were found among 9 CBC parameters, suggesting citrate may lead to inaccurate results compared to EDTA [13]. Another study of 55 healthy individuals comparing heparin and citrate revealed significant differences in 5 out of 14 CBC parameters (35.71%), with the remaining parameters showing variations. Similar patterns were observed when comparing citrate to EDTA. Comparing heparin to K3EDTA showed significant variations in three parameters (21.43%) [3]. In the current study, comparing K2EDTA to sodium citrate showed similar results in 8 out of 23 CBC parameters (34.78%), while comparing K2EDTA to sodium heparin showed comparable values in 14 out of 23 CBC parameters (60.87%).
Comparing PLT results between K2EDTA and sodium citrate with sodium heparin, significantly lower PLT counts were found in the latter two in the current study, contradicting findings in existing genuine literature [14-16]. One study suggested that citrate's strong platelet activation in sick animals may lead to decreased PLT counts due to platelet clumping [17]. Additionally, lower HGB and HCT values were observed in citrated blood samples compared to EDTA, consistent with previous studies [3,11]. This discrepancy may be attributed to citrate's interference with HGB oxidation, resulting in higher HGB levels in EDTA samples.
The CBC is commonly conducted on venous blood specimens anticoagulated with EDTA. Among various EDTA subtypes, the dipotassium salt form, K2EDTA, is endorsed by the International Council for Standardization in Hematology as the preferred anticoagulant for blood cell enumeration and sizing [7]. The study evaluated agreement levels between different anticoagulants, using K2EDTA as the standard. Sodium citrate showed substantial agreement in 8 out of 23 CBC parameters (34.78%), including MCV, MCH, WBC, %Neu, %Lymph, Neu, Lymph, and Eos. Similarly, sodium heparin demonstrated substantial agreement in determining MCV, MCH, WBC, %Lymph, Neu, Lymph, RBC, and HGB. These findings align with previous literature, which indicated substantial agreement with heparin in assessing 4 out of 14 CBC parameters (28.57%), including RBC, HGB, HCT, and MCH [3].
Conclusion
Citrate was found to be a less reliable anticoagulant for CBC estimation compared to K2EDTA, potentially leading to inaccurate readings. On the other hand, sodium heparin showed comparable performance to K2EDTA, making it a suitable alternative under specific conditions.
Declarations
Conflicts of interest: The authors have no conflicts of interest to disclose.
Ethical approval: The study was approved by the Institutional Ethics Committee and utilized data obtained from hospital archives Ethical Approval for this study was obtained from the Ksciens ethical committee (Approval Number 43. 2025).
Patient consent (participation and publication): Written informed consent was obtained from all patients (or their legal guardians, where applicable) for participation in the study and for the publication of all associated clinical information and images.
Source of Funding: Star Lab Company.
Role of Funder: The funder remained independent, refraining from involvement in data collection, analysis, or result formulation, ensuring unbiased research free from external influence.
Acknowledgements: Not applicable.
Authors' contributions: RQS and SQO were major contributors to the conception of the study, as well as to the literature search for related studies. DAH and AMM were involved in the literature review and the writing of the manuscript. SLE, HAY, HSA and MTT were involved in the literature review, the design of the study, the critical revision of the manuscript, and the processing of the tables. QOS and AMM confirm the authenticity of all the raw data. All authors have read and approved the final manuscript.
Use of AI: AI was not used in the drafting of the manuscript, the production of graphical elements, or the collection and analysis of data.
Data availability statement: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.