Methods: This retrospective study included a total of 68 patients (29 males, 39 females; mean age 55.2±1.6 years; range, 22 to 80 years) with acute lower extremity deep vein thrombosis and 34 healthy controls (15 males, 19 females; mean age 52.8±2.5 years; range, 21 to 77 years) without acute lower extremity deep vein thrombosis between March 2016 and August 2018. Demographic and clinical characteristics of the participant and laboratory data including complete blood count parameters were recorded. Univariate and multivariate analyses were used to identify significant predictors of deep vein thrombosis.
Results: Demographic and clinical characteristics were similar between the groups. According to the univariate analysis, platelet count and red cell distribution width were found to be significantly higher in the patient group compared to the control group. However, the red cell distribution width was not considered a significant predictor of acute deep vein thrombosis. According to the multivariate logistic regression analysis, the platelet-to-lymphocyte ratio and platelet count were significant predictors of acute deep vein thrombosis.
Conclusion: Our study results show that the platelet-tolymphocyte ratio may be a useful biomarker to support the diagnosis of acute deep vein thrombosis.
Although medical and surgical treatment modalities have evolved in recent years, DVT still poses a potential problem, as it is associated with various clinical entities such as chronic venous insufficiency, phlegmasia cerulea dolens, venous gangrene, and pulmonary thromboembolism which all can have severe consequences.[2-5] Hereditary or acquired tendency to thrombosis, hypercoagulability, venous stasis, endothelial damage, and inflammation are the main factors which play a role in the etiopathogenesis of DVT.
In cases of DVT, inflammatory response occurs with the increase in several biomarkers in the blood such as C-reactive protein (CRP) and interleukins.[6] In addition to these known markers, some routine complete blood count (CBC) parameters such as the mean platelet volume (MPV) and red cell distribution width (RDW) have been studied as the inflammatory indicators in patients with DVT.[7-9] On the other hand, novel and promising inflammatory biomarkers derived from CBC tests such as platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) have recently been investigated in various diseases, and gained an increasing popularity in many medical societies worldwide.[10-14] In accordance with the role of the inflammatory process, these aforementioned novel markers have been studied in vascular diseases as well, although studies investigating both PLR and NLR in patients with DVT are scarce. In addition, previous studies in the literature have often addressed into particular CBC parameters and there is no comprehensive study investigating all CBC parameters in DVT patients.
In the present study, we aimed to investigate most of the routine CBC parameters as well as the novel inflammatory biomarkers such as PLR and NLR in patients with acute lower extremity DVT and to evaluate the clinical relevance of these parameters in this patient population.
Peripheral venous blood samples were obtained from each participant included in the study, and the samples were placed into sterile tubes containing a standard amount of anticoagulant. To detect the results of CBC parameters, the blood samples were studied in an automatic CBC analysis device (Beckman Coulter Inc., CA, USA) within one hour after the samples were taken. Original kits of the manufacturer were utilized in the laboratory assays.
Statistical analysis
Due to the retrospective design of the study, only
patients who were admitted to our hospital with a certain
date were included. Therefore, we performed a post-hoc
compute achieved power analysis and, in the presence of
the aforementioned sample size, the power (1-b err prob)
was found to be 0.88 with an effect size of 0.5.
Statistical analysis was performed using the IBM SPSS for Windows version 20.0 software (IBM Corp., Armonk, NY, USA). Normality of the continuous variables was assessed using the Shapiro-Wilk test. The normally distributed variables were analyzed using the independent samples t-tests. The Mann- Whitney U test was used for non-normally distributed continuous variables. Categorical variables were analyzed using the chi-square with Fisher"s exact correction, where applicable. Logistic regression analysis was performed to identify significant predictors of acute DVT and the odds ratio (OR) for each possible variable was calculated after adjusting for the effects of the other variables. Continuous variables were presented in mean±standard deviation and median (min-max), while categorical variables were presented in number and frequency. A p value of <0.05 was considered statistically significant.
Table 1: Univariate analysis for control and deep vein thrombosis groups
Logistic regression analysis results are summarized in Table 2. After controlling for other variables, increased PLT count and PLR were found to be the significant predictors of the diagnosis of DVT. The risk of DVT increased by 15% with one unit increase in the PLT (OR=1.015; 95% CI: 1.003-1.027; p=0.014), while the same risk increased by 14.8% with one unit increase in the PLR (OR=0.852; 95% CI: 0.731-0.994, p=0.041).
Table 2: Multivariate analysis results for significant predictor of acute deep vein thrombosis
Platelets are mainly responsible for hemostasis and tissue regeneration in the human body. In addition, they play an essential role in thrombus formation. Thromboxane synthesis and release of adhesion molecules secondary to increased PLT count and activity in the blood are considerable etiopathogenetic factors of thrombus formation in the development of venous thrombosis, as well.[15,16] There are many reports indicating that PLT count increases in patients with DVT than those without DVT.[8,17,18] The results of the aforementioned reports are consistent with the results from our study. One of the possible mechanisms of that increase in the PLT count in DVT may be that the levels of inflammatory cytokines in the systemic circulation increase in case of an inflammatory event. These inflammatory cytokines may induce thrombopoiesis in the bone marrow and, thereby, resulting in a more production of PLTs from megakaryocytes. On the contrary, in a study with a relatively large sample size, the authors found no significant difference in the PLT count between the patients with and without DVT.[7] In this study, the authors, for the first time, showed that increased RDW levels were significantly and independently associated with DVT. More interestingly, another study demonstrated that the mean PLT count was significantly lower in the patients with DVT compared to healthy individuals.[9]
In recent years, PLR and NLR have become the most popular and valuable CBC parameters as novel inflammatory biomarkers. These parameters are easily calculated by the ratio of PLT and NEU counts to lymphocyte count from a simple and inexpensive CBC test. Although both PLR and NLR have been examined and are still being examined in the prediction of a wide range of disease spectrum including many cardiovascular diseases such as congestive heart failure,[10] coronary artery disease,[11] atrial fibrillation,[19] and carotid artery disease,[20] there are few studies in the existing literature regarding whether PLR and NLR can be used as predictors in patients with acute lower extremity DVT. Ferroni et al.[21] analyzed the role and clinical importance of PLR and NLR in venous thromboembolism (VTE) risk prediction for ambulatory cancer patients and concluded that only PLR could be used as an independent predictor of a future VTE episode. Similarly, Yang and Liu[22] investigated that whether PLR was a risk predictor of VTE in cancer patients and revealed that the PLR at the time of cancer diagnosis could be an independent predictor of VTE. These evidences may be considered consistent with our study results; however, the aforementioned studies specifically focused on the prediction of future VTE in patients with cancer. On the other hand, in our study, we performed an analysis to determine independent predictors of acute DVT after comparing all DVT patients (regardless of etiological and comorbid factors) with healthy individuals. However, Yao et al.[23] examined the predictive value of both PLR and NLR for acute DVT development after a specific surgical intervention and reported that neither PLR nor NLR could predict surgery-related DVT accurately. Moreover, in a recent study, the predictive values of PLR and NLR as well as some PLT indices were studied in the patients with acute DVT, consequently only NLR was found to be the independent predictor of DVT.[24]
In our opinion, the main strength of the current study is the examination of almost all CBC parameters in patients with DVT and, to the best of our knowledge, this study is the first study to investigate almost all CBC parameters in acute lower extremity DVT. On the other hand, the main limitations of our study are the retrospective nature, relatively small sample size, and single-center cohort study.
In conclusion, our study results suggest that among complete blood count parameters, platelet-tolymphocyte ratio and platelet can be used as significant predictors of acute deep vein thrombosis.
Declaration of conflicting interests
The authors declared no conflicts of interest with respect to
the authorship and/or publication of this article.
Funding
The authors received no financial support for the research
and/or authorship of this article.
1) Cohen AT, Agnelli G, Anderson FA, Arcelus JI, Bergqvist D,
Brecht JG, et al. Venous thromboembolism (VTE) in Europe.
The number of VTE events and associated morbidity and
mortality. Thromb Haemost 2007;98:756-64.
2) Yuksel A, Tuydes O. Midterm outcomes of
pharmacomechanical thrombectomy in the treatment of
lower extremity deep vein thrombosis with a rotational
thrombectomy device. Vasc Endovascular Surg
2017;51:301-6.
3) Polat A, Ketenciler S, Yücel C, Boyacıoğlu K, Akdemir İ,
Kük ZG, et al. Accelerated catheter-directed thrombolytic
treatment in deep venous thrombosis: mid-term results. Turk
Gogus Kalp Dama 2015;23:485-92.
4) Tok M, Tuydes O, Kan İİ, Yuksel A, Yolgösteren A. Our early
results for the treatment of acute and subacute iliofemoral
deep vein thrombosis with rotational thrombectomy catheter.
Turk J Vasc Surg 2014;23:169-75.
5) Dumantepe M, Tarhan A, Kehlibar T, Özler A. Low
molecular weight heparin versus oral anticoagulants
in the long-term treatment of deep venous thrombosis:
Surveillance of thrombus regression. Turk Gogus Kalp
Dama 2013;21:69-77.
6) Vazquez-Garza E, Jerjes-Sanchez C, Navarrete A, Joya-
Harrison J, Rodriguez D. Venous thromboembolism:
thrombosis, inflammation, and immunothrombosis for
clinicians. J Thromb Thrombolysis 2017;44:377-385.
7) Cay N, Unal O, Kartal MG, Ozdemir M, Tola M. Increased
level of red blood cell distribution width is associated
with deep venous thrombosis. Blood Coagul Fibrinolysis
2013;24:727-31.
8) Gulcan M, Varol E, Etli M, Aksoy F, Kayan M. Mean platelet
volume is increased in patients with deep vein thrombosis.
Clin Appl Thromb Hemost 2012;18:427-30.
9) Cil H, Yavuz C, Islamoglu Y, Tekbas EÖ, Demirtas S,
Atilgan ZA, et al. Platelet count and mean platelet volume in
patients with in-hospital deep venous thrombosis. Clin Appl
Thromb Hemost 2012;18:650-3.
10) Durmus E, Kivrak T, Gerin F, Sunbul M, Sari I, Erdogan O.
Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte
ratio are predictors of heart failure. Arq Bras Cardiol
2015;105:606-13.
11) Cho KI, Ann SH, Singh GB, Her AY, Shin ES. Combined
usefulness of the platelet-to-lymphocyte ratio and the
neutrophil-to-lymphocyte ratio in predicting the long-term
adverse events in patients who have undergone percutaneous
coronary intervention with a drug-eluting stent. PLoS One
2015;10:e0133934.
12) Pedrazzani C, Mantovani G, Fernandes E, Bagante F, Luca
Salvagno G, Surci N, et al. Assessment of neutrophil-tolymphocyte
ratio, platelet-to-lymphocyte ratio and platelet
count as predictors of long-term outcome after R0 resection
for colorectal cancer. Sci Rep 2017;7:1494.
13) Qin B, Ma N, Tang Q, Wei T, Yang M, Fu H, et al. Neutrophil
to lymphocyte ratio (NLR) and platelet to lymphocyte ratio
(PLR) were useful markers in assessment of inflammatory
response and disease activity in SLE patients. Mod Rheumatol
2016;26:372-6.
14) Yoldas H, Karagoz I, Ogun MN, Velioglu Y, Yildiz I, Bilgi
M, et al. Novel mortality markers for critically Ill patients. J
Intensive Care Med 2018:885066617753389.
15) Bath PM, Butterworth RJ. Platelet size: measurement,
physiology and vascular disease. Blood Coagul Fibrinolysis
1996;7:157-61.
16) Karagoz I, Aktas G, Yoldas H, Yildiz I, Ogun MN, Bilgi
M, et al. Association between hemogram parameters and
survival of critically Ill patients. J Intensive Care Med
2019;34:511-13.
17) Çalışkan A, Yazıcı S, Karahan O, Demirtaş S, Yavuz
C, Güçlü O, et al. The investigation of complete blood
counting parameters in deep venous thrombosis. Dicle Med
J 2014;41:118-22.
18) Canan A, Halıcioğlu SS, Gürel S. Mean platelet volume and
D-dimer in patients with suspected deep venous thrombosis.
J Thromb Thrombolysis 2012;34:283-7.
19) Weymann A, Ali-Hasan-Al-Saegh S, Sabashnikov A, Popov
AF, Mirhosseini SJ, Liu T, et al. Prediction of new-onset and
recurrent atrial fibrillation by complete blood count tests:
a comprehensive systematic review with meta-analysis. Med
Sci Monit Basic Res 2017;23:179-222.
20) Massiot N, Lareyre F, Voury-Pons A, Pelletier Y, Chikande
J, Carboni J, et al. High neutrophil to lymphocyte ratio and
platelet to lymphocyte ratio are associated with symptomatic
internal carotid artery stenosis. J Stroke Cerebrovasc Dis
2019;28:76-83.
21) Ferroni P, Riondino S, Formica V, Cereda V, Tosetto L, La
Farina F, et al. Venous thromboembolism risk prediction
in ambulatory cancer patients: clinical significance of
neutrophil/lymphocyte ratio and platelet/lymphocyte ratio.
Int J Cancer 2015;136:1234-40.
22) Yang W, Liu Y. Platelet-lymphocyte ratio is a predictor of
venous thromboembolism in cancer patients. Thromb Res
2015;136:212-5.