INTRODUCTION

Venous thromboembolism (VTE) is a vascular condition that leads to deep vein thrombosis (DVT) and pulmonary embolism (PE), with an incidence of 1–2 per 1,000 people in Europe and the United States, although the occurrence is lower in Asia.[1,2]

DVT is characterized by an obstruction in venous return, most commonly occurring in the lower extremities. The process of clot formation usually begins in distal areas (such as the calf) and can extend to more proximal veins, with reported distributions of 40% in distal veins, 16% in the popliteal vein, 20% in the femoral vein, 20% in the common femoral vein, and 4% in the iliac vein. In addition, DVT can occur in the mesenteric and cerebral veins, and it is one of the three major causes of cardiovascular death following myocardial infarction or stroke.3 Epidemiological data show that there are 80 cases of DVT per 100,000 population annually worldwide, with 15–20% of these cases occurring in Asia.[3,4] In Indonesia, particularly in 2020, 37.1–40.3% of inpatients were recorded as having DVT, although specific data from Medan are incomplete, and DVT has a mortality rate of approximately 6%.[5]

Triggers for DVT include acquired conditions such as post-operative status, pregnancy, immobilization, and infection, as well as hereditary factors like antithrombin deficiency, factor V Leiden mutation, and polymorphisms in the protein C gene promoter (C2405T and A2418G).[6] The pathogenesis of DVT is associated with Virchow’s triad: stasis of blood flow, endothelial injury, and hypercoagulability. Currently, the involvement of platelets is also recognized; endothelial injury due to inflammation increases the expression of P-selectin, which facilitates the adhesion of leukocytes and platelets, as well as creates a hypoxic environment that further enhances the expression of adhesion molecules and activation of the coagulation cascade.[6]

The link between inflammation and thrombosis forms the basis of thromboinflammation, wherein the activation of non-adaptive immune cells and platelets contributes to the activation of the complement system and the coagulation cascade, potentially leading to both microvascular and macrovascular occlusion.[7] Furthermore, two phenotypes of DVT exist: microthrombus and macrothrombus formation, which depend on the depth and extent of vascular wall injury; limited endothelial injury, as seen in sepsis, leads to disseminated microthrombi, whereas trauma that extends into the subendothelial layer produces macrothrombi.[8]

In addition to D-dimer, a complete blood count provides information on the inflammatory status through parameters like the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR). With a normal range of 1–2, NLR reflects the balance between the non-specific and specific immune responses; an NLR above 3.0 indicates pathology, and values above 11–17 suggest severe inflammation and stress.[9-13] Rinaldi et al. demonstrated that the diagnostic value of NLR is comparable to that of D-dimer, especially in patients with a low clinical probability of DVT.[14]

The activation of coagulation factors accelerated by inflammatory mediators and disturbances in the fibrinolysis process are key to the development of DVT. Fibrinolysis, which degrades fibrin, produces D-dimer—a sensitive marker for intravascular thrombus—that also increases in conditions such as acute aortic dissection, pregnancy, aging, and malignancy.[11,12] The D-dimer assay has a high negative predictive value, although high values require further investigation to confirm VTE.

Platelets also play a role in inflammation, with PLR being an additional parameter calculated from a complete blood count. PLR is not only useful in assessing thromboembolic risk but also serves as a prognostic factor in cardiovascular diseases, where higher PLR is associated with long-term mortality and a threefold increased risk of thromboembolic symptoms.[15–18] The combination of NLR, PLR, and D-dimer has been shown to significantly improve the diagnostic performance for DVT compared to using D-dimer alone.[19,20] Based on this background, the present study was conducted to evaluate the relationship between D-dimer levels and both NLR and PLR in DVT patients at Adam Malik Hospital.

METHOD

This study utilized an analytical observational design with a cross-sectional approach to evaluate the relationship between D-dimer levels and NLR as well as PLR in DVT patients at Adam Malik Hospital. The independent variables were NLR and PLR, while the dependent variable was the D-dimer level. The study was conducted at Adam Malik Hospital in Medan from July 2024 to September 2024, with the population comprising DVT patients and the sample collected consecutively from patients diagnosed between June 2022 and June 2024. The minimum sample size was calculated using a correlational formula,35 resulting in 34 patients, and sampling was performed using purposive sampling based on inclusion criteria (age ≥18 years, newly diagnosed DVT, and complete medical records) and exclusion criteria (patients with hematologic malignancies, previous DVT, or incomplete medical records).

Data collected included demographic characteristics (age, gender, comorbidities) and laboratory values (neutrophils, lymphocytes, platelets, D-dimer) extracted from medical records. Data processing involved editing, coding, entry, cleaning, and analysis, with results presented in tables or graphs. Statistical analysis was performed using SPSS 26, employing Pearson’s test for normally distributed data or Spearman’s test for non-normal data to determine the relationship between NLR, PLR, and D-dimer levels.

RESULTS

Based on 100 samples, all subjects were confirmed with a DVT diagnosis using Doppler ultrasound. As shown in Table 1, the gender distribution was perfectly balanced (50% male, 50% female). The majority of patients were over 59 years old (38%), followed by those aged 45–59 (35%) and 19–44 (27%).

Table 1. Demographic Characteristics of Study Subjects (n = 100).

The most common comorbidity was infection (71%), followed by cardiovascular disease (49%), type 2 diabetes mellitus (40%), cancer (14%), chronic kidney disease (12%), and autoimmune disease (9%). All subjects were inpatients, with 62% staying more than 7 days and 38% less than 7 days. Most laboratory parameters had a non-normal distribution and were presented as median (minimum–maximum): leukocytes 12,935 (2,460–52,930), basophils 0.2 (0.00–1.20), eosinophils 0.90 (0.00–45.20), neutrophils 82.45 (30.40–97.50), lymphocytes 6.45 (0.70–37.00), NLR 13.37 (2.07–139.29), PLR 186.82 (15.29–1,944.44), and D-dimer 2,685 (140–35,000). Parameters with normal distribution (hemoglobin, platelets, and monocytes) were expressed as mean ± SD: hemoglobin 9.58 ± 2.55, platelets 261,680 ± 145,948, and monocytes 6.88 ± 3.59.

Table 2 shows the statistical results of the relationship between D-dimer levels and the neutrophil-to-lymphocyte ratio in DVT patients. Based on Spearman’s correlation test, a significant relationship (p = 0.001) was found between D-dimer and NLR among the 100 subjects, with a weak positive correlation (r = +0.315). This indicates that although an increase in NLR is associated with higher D-dimer levels, the strength of the correlation is relatively low.

Table 2. Relationship Between D-Dimer and Neutrophil-to-Lymphocyte Ratio

Note *Spearman’s rho

Table 3 presents the statistical analysis of the relationship between D-dimer levels and the platelet-to-lymphocyte ratio in DVT patients. Spearman’s correlation test revealed no significant relationship (p = 0.610) between D-dimer and PLR among the 100 subjects, with a very weak negative correlation (r = –0.052).

Table 3. Relationship Between D-Dimer and Platelet-to-Lymphocyte Ratio

Note *Spearman’s rho

DISCUSSION

This study demonstrated that the gender distribution among DVT patients was nearly equal, despite a slight difference noted in a systematic review by Lee et al,[21,23] which reported that women were slightly more often diagnosed with DVT than men. Other studies, such as Rinaldi et al,[14] have reported a 53.2% prevalence of DVT among female patients with suspected DVT. These discrepancies are likely due to variations in sample size and comorbid conditions among study populations. The majority of patients in the current study were older than 59 years (38%), which supports the notion that the risk of DVT increases with age, as confirmed by the systematic review by Fowkes et al21 and the study by Rinaldi et al,[14] where the incidence was higher in patients aged ≥45 years. Age-related physiological changes such as endothelial dysfunction, decreased fibrinolytic activity, and increased coagulation factors contribute to the heightened risk of thrombosis in the elderly.[22]

The comorbidity data showed that most patients had infections (71%), followed by cardiovascular disease (49%), type 2 diabetes mellitus (40%), cancer (14%), chronic kidney disease (12%), and autoimmune disorders (9%). Tambunan et al5 found that cancer and acute infection were the most common comorbidities in DVT patients, whereas Rinaldi et al,[14] reported that cancer (44%), type 2 diabetes mellitus (20.37%), and chronic kidney disease (14.81%) were predominant. Additionally, the systematic review by Lee et al.[23] indicated that many DVT cases are related to predispositions such as cancer, severe neurological diseases, major trauma, and major surgery within the last three months. The heterogeneity of the study populations may explain these differences.

All subjects in the study were inpatients, with most having a hospital stay of more than 7 days. This finding is consistent with the study by Amawi et al,[24] which reported that patients with venous thromboembolism tend to have longer hospital stays. Both age and the presence of comorbidities significantly influenced the length of hospitalization, reflecting the complexity of patient conditions and the need for more intensive management.

The distribution of NLR and PLR values showed non-normal patterns, with median NLR of 9.8 and PLR of 193.2, indicating a high systemic inflammatory response in DVT patients. Both NLR and PLR are widely used inflammatory markers in various medical conditions, including DVT. In the pathogenesis of DVT, neutrophils and platelets play critical roles in hemostasis and inflammation.[10] The findings are consistent with the meta-analysis by Hu et al,[25] which involved 11 studies with a total of 4,289 subjects and showed increased NLR and PLR values in DVT patients.

Regarding coagulation biomarkers, the study found a median D-dimer value of 2,685, with 95% of subjects showing high D-dimer levels. Elevated D-dimer reflects increased fibrinolytic activity in response to thrombus formation. Peng et al,[26] demonstrated that patients with DVT had significantly higher D-dimer levels compared to non-DVT patients. The combination of increased NLR, PLR, and D-dimer provides a comprehensive picture of the role of inflammation and hypercoagulability in the pathophysiology of DVT. These results are in line with the findings by Gao et al,[20] which indicate that the combined use of these three parameters significantly improves the diagnostic accuracy for DVT.

Spearman’s correlation analysis showed a significant relationship between D-dimer and NLR (p = 0.001, r = +0.315), indicating that as systemic inflammation (measured by NLR) increases, so does fibrinolytic activity (reflected by D-dimer levels). This supports the concept that inflammation plays a central role in DVT pathogenesis. Neutrophils, through the release of neutrophil extracellular traps (NETs), can trigger the activation of coagulation factors and recruit platelets, thereby enhancing thrombus formation.[12-27] Higher NLR in DVT patients reflects a predominance of systemic inflammation that may predict the severity of DVT, meaning that higher NLR values are more likely to be associated with increased coagulation activation and higher D-dimer levels.

Conversely, the analysis showed no significant correlation between D-dimer and PLR (p = 0.610, r = –0.052). Although PLR is an inflammatory marker that also reflects the role of platelets in thrombus formation, the mechanism of D-dimer elevation as a direct biomarker of fibrinolytic activity is different from that of PLR, which more indirectly reflects the systemic inflammatory status. This may be because PLR can be influenced by chronic inflammatory conditions, while D-dimer levels tend to fluctuate rapidly after a thrombotic event. Other studies, such as those by Gao et al and Sujana et al,[20-28] have suggested that the combined use of NLR, PLR, and D-dimer provides better diagnostic performance than using each parameter individually.

The findings from this study underscore the importance of using a combination of inflammatory biomarkers (NLR and PLR) and a coagulation biomarker (D-dimer) in the management of diagnosis and treatment of DVT. Elevated NLR and PLR, when accompanied by high D-dimer levels, can serve as early indicators for detecting thrombotic risk in DVT patients. The combined use of these parameters not only helps predict the degree of inflammation and thrombosis but also holds potential in guiding the intensity of anticoagulant therapy. Therefore, the collective use of NLR, PLR, and D-dimer has significant prognostic value, including in predicting complications such as pulmonary embolism or recurrent DVT. Furthermore, using these parameters as diagnostic aids can enhance the accuracy of DVT diagnosis and assist in devising more appropriate treatment strategies. This is critical in reducing the morbidity and mortality associated with DVT, particularly in patients with complex clinical conditions and multiple comorbidities.

This study has several strengths, including being the first to evaluate the relationship between NLR, PLR, and D-dimer levels in DVT patients at Adam Malik Hospital. The use of NLR, derived from a complete blood count, is easily applicable in routine clinical practice and can be considered as an early detection tool for DVT in inpatients. However, there are limitations: the cross-sectional design and relatively small sample size without a control group may limit the generalizability of the findings. Additionally, as the study was conducted at a single center, the results may not fully represent a broader population. These limitations indicate the need for larger, multicenter studies in the future to obtain more representative and applicable results.

CONCLUSION

The patient demographics in this study revealed an equal distribution between males and females, with the majority being over 59 years old (38%), followed by those aged 45–59 (35%) and 19–44 (27%). The most common comorbidities were infection (71%), cardiovascular disease (49%), type 2 diabetes mellitus (40%), cancer (14%), chronic kidney disease (12%), and autoimmune disease (9%).

All subjects were inpatients, with 62% having a hospital stay of more than 7 days and 38% less than 7 days. A significant relationship was found between D-dimer levels and NLR in DVT patients, while no relationship was observed between D-dimer levels and PLR.Further studies are recommended to explore in greater depth the diagnostic and prognostic values of NLR and PLR compared to D-dimer, particularly in distinguishing DVT from other conditions with similar clinical manifestations.

DECLARATIONS

Ethics approval and consent to participate. Per,ission for this study was obtained from the Ethics Committee of Universitas Sumatera Utara and Haji Adam Malik General Hospital.

CONSENT FOR PUBLICATION

The Authors agree to publication in Journal of Society Medicine.

FUNDING

None

COMPETING INTERESTS

The authors declare that there is no conflict of interest in this report.

AUTHORS’ CONTRIBUTIONS

All authors significantly contribute to the work reported execution, acquisition of data, analysis, and interpretation, or in all these areas. Contribute to drafting, revising, or critically reviewing the article. Approved the final version to be published, agreed on the journal to be submitted, and agreed to be accountable for all aspects of the work.

ACKNOWLEDGMENTS

None

REFERENCE

1. Lutsey PL, Zakai NA, Cushman M, Folsom AR, Heckbert SR, Rosamond WD, et al. Epidemiology and prevention of venous thromboembolism. Nat Rev Cardiol. 2023; 20: 62-248.

2. Hartono T, Oktaliansah E, Zulfariansyah A, Mulyana A, Ramdhani MN, Suryani S, et al. Ketepatan dan kecukupan profilaksis venous thromboembolism berdasar pedoman American College of Chest Physicians di ruang rawat intensif Rumah Sakit Dr. Hasan Sadikin Bandung. J Anestesi Perioperatif. 2019; 7: 8-100.

3. Waheed SM, Kudaravalli P, Hotwagner DT, Johnson T, Patel S, Johnson M, et al. Deep vein thrombosis. StatPearls. 2023; 1: 1-16.

4. Chen CY, Liao KM, Lin HL, Hsu WH, Wang YH, Hsieh MJ, et al. The incidence of deep vein thrombosis in Asian patients with chronic obstructive pulmonary disease. Medicine (Baltimore). 2015; 94: 4-1741.

5. Tambunan KL, Kurnianda J, Suharti C, Nugroho Y, Prasetyo AD, Wibowo H, et al. IDENTIA Registry: Incidence of deep vein thrombosis in medically ill subjects at high risk in Indonesia: A prospective study. Acta Med Indones. 2020; 52: 14–25.

6. Navarrete S, Solar C, Tapia R, Pereira J, Fuentes E, Palomo I, et al. Pathophysiology of deep vein thrombosis. Clin Exp Med. 2022; 23: 54-645.

7. Stark K, Massberg S, Schön MP, Gawaz M, Lorenz M, Kaever V, et al. Interplay between inflammation and thrombosis in cardiovascular pathology. Nat Rev Cardiol. 2021; 18: 82-666.

8. Chang JC, Lee CH, Wang HC, Lin JH, Wu CC, Tsai YH, et al. Pathogenesis of two faces of DVT: New identity of venous thromboembolism as combined micro-macrothrombosis via unifying mechanism based on “two-path unifying theory” of hemostasis and “two-activation theory of the endothelium.” Life. 2022; 12: 1-220.

9. V S, Mohanty S, Das D, Ghosh A, Maiti R, Nanda P,et al. Hematological parameters as an early marker of deep vein thrombosis in diabetes mellitus: An observational study. Cureus. 2023; 15: 4-36813.

10. Hu J, Cai Z, Zhou Y, Lin W, Xu J, Liu F, et al. The association of neutrophil–lymphocyte ratio with venous thromboembolism: A systematic review and meta-analysis. Clin Appl Thromb Hemost. 2022; 28: 1–11.

11. Johnson ED, Schell JC, Rodgers GM, Harris MB, Wright JG, Thomas EM, et al. The D-dimer assay. Am J Hematol. 2019; 94: 9-833.

12. Gurram M, Pulivarthi S, Rao Y, Kaur M, Reddy SK, Suresh B, et al. Effectiveness of D-dimer as a screening test for venous thromboembolism: An update. North Am J Med Sci. 2014; 6: 6-491.

13. Zahorec R, Hudeček J, Kriška M, Radoš M, Valkovičová V, Šoltés L , et al. Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl Lek Listy. 2021; 122: 88-474.

14. Rinaldi I, Hamonangan R, Azizi MS, Cahyanur R, Wirawan F, Fatya AI, et al. Diagnostic value of neutrophil lymphocyte ratio and D-dimer as biological markers of deep vein thrombosis in patients presenting with unilateral limb edema. J Blood Med. 2021; 12: 25-313.

15. Chen Y, Zhong H, Zhao Y, Luo X, Gao W, Lin H, et al. Role of platelet biomarkers in inflammatory response. Biomark Res. 2020; 8: 1-28.

16. Balta S, Ozturk C, Demirkol S, Celik T, Aparci M, Iyisoy A, et al. The platelet-lymphocyte ratio: A simple, inexpensive and rapid prognostic marker for cardiovascular events. Platelets. 2015; 26: 1-680.

17. Simadibrata DM, Pandhita BAW, Ananta ME, Tango T, Nugroho RA, Wibowo H, et al. Platelet-to-lymphocyte ratio, a novel biomarker to predict the severity of COVID-19 patients: A systematic review and meta-analysis. J Intensive Care Soc. 2022; 23: 6-20.

18. Ye GL, Chen Q, Chen X, Li M, Sun X, Wang Z, et al. The prognostic role of platelet-to-lymphocyte ratio in patients with acute heart failure: A cohort study. Sci Rep. 2019; 9: 1-10639.

19. Yao C, Zhang Z, Yao Y, Xu X, Jiang Q, Shi D, et al. Predictive value of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio for acute deep vein thrombosis after total joint arthroplasty: A retrospective study. J Orthop Surg Res. 2018; 13: 1-40.

20. Gao Z, Zhao K, Jin L, Yang J, Tang Y, Liu H, et al. Combination of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio with plasma D-dimer level to improve the diagnosis of deep venous thrombosis (DVT) following ankle fracture. J Orthop Surg Res. 2023; 18: 1-362.

21. Fowkes FJ, Price JF, Fowkes FG, Greenhalgh RM, Murray GD, Lee AJ, et al. Incidence of diagnosed deep vein thrombosis in the general population: Systematic review. Eur J Vasc Endovasc Surg. 2003; 25: 1–5.

22. Cushman M, Tsai AW, White RH, Heckbert SR, Rosamond WD, Folsom AR, et al. Epidemiology and risk factors for venous thrombosis. Semin Hematol. 2007; 44: 9-62.

23. Lee LH, Gallus A, Jindal R, Wang C, Wu CC, Tan MH, et al. Incidence of venous thromboembolism in Asian populations: A systematic review. Thromb Haemost. 2017; 117: 60-2243.

24. Amawi H, Arabyat RM, Al-Azzam S, AlZu'bi T, U'wais HT, Hammad AM, et al. The length of hospital stay of patients with venous thromboembolism: A cross-sectional study from Jordan. Medicina (Kaunas). 2023; 59: 1-727.

25. Hu C, Zhao B, Ye Q, Zou J, Li X, Wu H, et al. The diagnostic value of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for deep venous thrombosis: A systematic review and meta-analysis. Clin Appl Thromb Hemost. 2023; 29: 1–10.

26. Peng L, Bao Q, Hong X, Li W, Zheng Y, Zou Z, et al. High level of neutrophil to lymphocyte ratio increases the risk of deep venous thrombosis in intensive care unit patients after oral cancer surgery: A retrospective study. Ann Transl Med. 2022; 10: 1-763.

27. Zhang XY, Zhang XX, Xu JL, Li YQ, Ma YZ, Wang Y, et al. Identification of and solution for false D-dimer results. J Clin Lab Anal. 2020; 34: 4-23216.

28. Sujana KY, Semadi IN, Mahadewa TGB, Pradnyan KL, Arya IKA, Raka KA, et al. The correlation of neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio compared to D-dimer as a diagnostic test in deep vein thrombosis (DVT). Bali Med J. 2020; 9: 52-546.