Computational Analysis of the Related Factors of Deep Vein Thrombosis (DVT) Formation in Patients Undergoing Hip Fracture Surgery.

A retrospective study was conducted on 51 patients undergoing hip fracture surgery to investigate the factors associated with the formation of deep venous thrombosis (DVT). The independent sample t-test and correlation analysis were used to sort out and analyze the data. The findings are as follows. (1) Different gender samples showed significant differences in the Caprini score and thrombus location. Most DVTs in females are located in the posterior tibial vein and intermuscular veins. The Caprini score of females was significantly higher than that of males. (2) Age displays a positive correlation with DVT, coronary heart disease, hypertension, and different surgical types, respectively. (3) There is a correlation between age and operation duration. (4) Hyperlipidemia and cerebrovascular disease show a positive correlation with DVT. (5) There was a significant negative correlation between the Caprini score and the quantification of D-dimer. This indicates that in this sample, the higher the patients' Caprini score is, the lower the quantitation of D-dimer will be. (6) Hyperlipidemia and cardiac insufficiency show a positive correlation with cerebrovascular disease. Patients with hyperlipidemia and cardiac insufficiency may also suffer from cerebrovascular diseases.

1. Introduction

Patients undergoing hip fracture surgery are likely to lead to deep venous thrombosis (DVT) due to decreased activity (for one, intraoperative supine position and anesthesia will make the peripheral veins dilate. For another, postoperative bed rest will cause deep venous blood flow in the lower limbs slow), dehydration (diet is prohibited for a long time before and after the operation, and blood viscosity will increase), senile diseases (a variety of chronic diseases), surgical trauma, and anesthesia factors. Following a hip fracture, vascular damage is serious, and platelet aggregation cannot heal the wound, so it needs to form a blood clot [1]. Thrombin will activate more platelets to promote blood coagulation. It helps soluble fibrin in plasma to be converted into insoluble protein for rapid hemostasis [2-4]. As shown in Figure 1, the interaction among exposed lipid-rich atherosclerotic plaques and platelet receptors and coagulation factors can lead to platelet activation and aggregation. This may lead to DVT, increase the risk of surgery, affect the smooth surgery, and even endanger life. It can also lead to heart disease, high blood pressure, high blood fat, and many other diseases [5, 6].

Figure 1

Causes of thrombosis.

SPSS has been widely used for data analysis in the analysis of risk factors related to DVT, and case analysis is used more frequently [7-10]. Treatment options for DVT have been analyzed and developed in some studies [11-13]. The INARI FlowTriever device can remove DVT and significantly reduce the risk of bleeding [14]. Some studies analyzed and studied the causes of DVT and the locations where DVT frequently occurs [15, 16]. Other studies have found that DVT can trigger other diseases [2, 17–19], such as multiple organ embolism, stroke, and acute myocardial infarction.

In summary, DVT is one of the common and serious complications of hip fracture. Whether the operation is performed or not, the incidence of DVT is high and the harm is great. Once it forms, it will affect the operation effect and even endanger life. At present, there are few studies analyzing the related risk factors of DVT formation in patients undergoing hip fracture surgery. Therefore, in this study, the locations and the related factors of DVT formation were analyzed among 51 patients with hip fractures, including 18 males and 33 females. The independent sample t-test and correlation analysis were used to sort out the data. This study will supplement the data on DVT and its related factors in hip fracture patients, so as to provide a theoretical basis for future research in this field.

2. Methods

2.1. General Description

51 hip fracture patients (18 males and 33 females) were randomly selected as research subjects according to their hospital stay. These patients were admitted to the orthopedics department for surgery from June 1, 2021, to April 7, 2022. The femoral head replacement was performed in 19 patients, intramedullary nailing in 21 patients, and total hip replacement in 11 patients. SPSS analysis was conducted on the related factors of deep vein thrombosis (DVT) formation in these patients. The t-test analysis, Pearson correlation, and curve regression of SPSS were used to study the relationship between variables.

2.2. Inclusion and Exclusion Criteria

Inclusion criteria were as follows:

Patients admitted to our hospital and diagnosed with no DVT using color Doppler ultrasonography of blood vessels of both lower limbs

Patients with hip fractures and undergoing surgical treatment

Exclusion criteria were as follows:

Coagulation dysfunction

Pathological fractures

Data cannot be collected

3. Results

As given in Table 1, the t-test (independent sample t-test) was used to analyze the differences between gender for the Caprini score as well as thrombus sites. As given in Table 1, samples of different genders showed the significance for the Caprini score and DVT site. For females, most DVTs are located in the posterior tibial vein and intermuscular veins. This is different from males. Females were significantly higher than males in the Caprini score.

Table 1

The t-test analysis of gender and the Caprini score and thrombus site.

	Gender (mean and standard deviation)		t	P
	Female (n = 33)	Male (n = 18)
Caprini score	11.21 ± 1.95	9.33 ± 2.66	2.888	0.006∗∗
Thrombus site	2.33 ± 0.85	1.78 ± 0.81	2.261	0.028∗

P < 0.05. P < 0.01.

As given in Table 2, correlation analysis was performed to study the correlation between age and DVT, coronary heart disease, hypertension, D-dimer quantification, and operation type changes, respectively. The Pearson correlation coefficient was used to indicate the strength of the correlation. According to the correlation coefficient in Table 2, age displayed a positive correlation with DVT, coronary heart disease, hypertension, and surgical-type changes, respectively. There was a negative correlation between age and the D-dimer quantitative value. This indicates that, in this sample, as the age increases, people will be more likely to suffer from DVT, coronary heart disease, hypertension, and other diseases, but the quantitative value of D-dimer will be lower. In addition, as the age increases, patients will choose different types of surgery.

Table 2

Pearson correlation of age with DVT, coronary heart disease, hypertension, D-dimer quantification, and surgical-type changes.

		Age (years)
DVT	Correlation coefficient	0.421∗∗
	P value	0.002
Coronary heart disease	Correlation coefficient	0.294∗
	P value	0.036
Hypertension	Correlation coefficient	0.301∗
	P value	0.032
D-dimer quantification	Correlation coefficient	−0.288∗
	P value	0.041
Surgical-type changes	Correlation coefficient	0.307∗
	P value	0.028

P < 0.05. P < 0.01.

As given in Table 3, the regression coefficient (p = 0.006, less than 0.01) of the curve between age and surgical duration indicates that the age (independent variable) has a correlation with the surgical duration (dependent variable). But the selected effective sample size was too small, and this conclusion is only for describing the situation of this sample. Curvilinear regression is a nonlinear relationship in the relation form, but it can be changed into a linear relationship through various conversions. The relationship between age and surgery duration is clearly shown in Figure 2.

Table 3

Curvilinear regression coefficient of age and operation duration.

	Nonstandardized coefficient		Normalization coefficient	t	P
	B	Standard error	Beta
Constant	243.905	80.435	—	3.032	0.006∗∗
Age (years)	−3.040	2.890	−1.143	−1.052	0.303
Age (years)∗∗2	0.018	0.024	0.785	0.723	0.477

P < 0.01. Dependent variable, operation duration (minutes).

Figure 2

Quadratic curve fitting.

As given in Table 4, correlation analysis was performed to study the correlation between DVT and hyperlipidemia and cerebrovascular disease, respectively. The Pearson correlation coefficient was used to indicate the strength of the correlation. From the correlation coefficient given in Table 4, it can be seen that DVT shows a positive correlation with hyperlipidemia and cerebrovascular disease. It indicates that hyperlipidemia and cerebrovascular disease increase the risk of DVT in patients with hip fractures in this sample.

Table 4

Pearson correlation of DVT with hyperlipidemia and cerebrovascular disease.

		DVT
Hyperlipidemia	Correlation coefficient	0.305∗
	P value	0.030
Cerebrovascular disease	Correlation coefficient	0.368∗∗
	P value	0.008

P < 0.05. P < 0.01.

As given in Table 5, correlation analysis was performed to analyze the correlation between the Caprini score and D-dimer quantification. The Pearson correlation coefficient was used to indicate the strength of the correlation. From the correlation coefficient given in Table 5, it can be seen that there is a significant negative correlation between the Caprini score and D-dimer quantification. This indicates that a higher Caprini score is associated with a lower D-dimer quantitative value in this sample. D-dimer is an indicator of risk for DVT. Notably, the higher the Caprini score and D-dimer quantification value are, the higher the risk of DVT will be. Theoretically, the Caprini score and the D-dimer quantification value have a positive correlation with thrombosis. However, in this sample, the Caprini score displayed a negative correlation with D-dimer quantification. The cause of this result remains unclear and deserves attention and discussion in future studies.

Table 5

Pearson correlation of Caprini scores with D-dimer quantification.

		Caprini score
D-dimer quantification	Correlation coefficient	−0.376∗∗
	P value	0.006

P < 0.01.

As given in Table 6, correlation analysis was performed to study the correlation between cerebrovascular disease and cardiac insufficiency and hyperlipidemia, respectively. The Pearson correlation coefficient was used to indicate the strength of the correlation. From the correlation coefficient given in Table 6, it can be seen that cerebrovascular disease displays a positive correlation with cardiac insufficiency and hyperlipidemia. It indicates that in this sample, patients with hyperlipidemia and cardiac insufficiency may be accompanied by the occurrence of cerebrovascular diseases.

Table 6

Pearson correlation of cerebrovascular diseases with cardiac insufficiency and hyperlipidemia.

		Cerebrovascular disease
Cardiac insufficiency	Correlation coefficient	0.327∗
	P value	0.020
Hyperlipidemia	Correlation coefficient	0.328∗
	P value	0.019

P < 0.05.

4. Conclusion

The following conclusions were derived from this sample:

Samples of different genders showed significant differences in the Caprini score and thrombus location. The location of thrombosis in females is different from that in males, and most DVTs in females are located in the posterior tibial vein and intermuscular veins. The Caprini score of females was significantly higher than that of males.

Age displays a positive correlation with DVT, coronary heart disease, hypertension, and different surgical types, respectively. There was a negative correlation between age and D-dimer. It indicates that in this sample, as the age increases, people will be more likely to suffer from DVT, coronary heart disease, hypertension, and other diseases, but the quantitative value of D-dimer will be lower. As the age increases, patients will choose different types of surgery.

There is a correlation between age and operation duration

Hyperlipidemia and cerebrovascular disease show a positive correlation with DVT. This indicates that in this sample, the patients with hyperlipidemia and cerebrovascular disease had an increased risk of DVT.

There was a significant negative correlation between the Caprini score and the quantification of D-dimer. This indicates that in this sample, the higher the patients' Caprini score is, the lower the quantitation of D-dimer will be.

Hyperlipidemia and cardiac insufficiency show a positive correlation with cerebrovascular diseases. Patients with hyperlipidemia and cardiac insufficiency may suffer from cerebrovascular diseases.

5. Discussion

In this sample, females differ from males in the location of the thrombus, and most DVTs in females are located in the posterior tibial vein and intermuscular veins. However, there is no fixed location in males. This may be due to the fact that the majority of patients with deep venous thrombosis of the lower extremities suffer from varicose veins of the lower extremities [20]. Females are more likely to have varicose veins in the lower extremities [21] and have a higher incidence of hip fractures than in males [22]. There were twice as many females as males in this sample, so there is such a significant difference.

It is also important to note that the Caprini score is significantly higher for females than for males. The Caprini score is related to age, gender, and thrombosis. This is meaningful because previous studies have found that VTE frequently occurs in females who use estrogen, during pregnancy, or in people with thrombophilia [23-26]. Our study was in agreement with other researchers. At present, these studies do not address specific reasons for gender differences. The causes of this situation can be further explored in future studies.

It was concluded that there was a negative correlation between age and D-dimer quantification. In previous studies, D-dimer levels were positively correlated with age [27] and significantly decreased in males [28]. Since the number of young and middle-aged people in the samples of this study is small and the D-dimer level of the young and middle-aged people is already high at the time of going to the hospital, it can be concluded that the D-dimer value decreases with age in statistical significance. The development of age-related changes in microcirculation and blood coagulation was excluded, resulting in results that were inconsistent with the increasing concentrations of D-dimer with age.

The study found that there was a relationship between age and operation duration. The older the patient was, the longer the operation duration would be. This finding is common in surgery [29]. The older the patient is, the more comorbidities he has [30] and the higher the risk of surgery would be.

A higher Caprini score was associated with a lower D-dimer quantitative value. Notably, the D-dimer quantitative value and Caprini scale were used as indicators and assessment tools to determine the risk of DVT, respectively [31]. Theoretically, both have a positive correlation with thrombosis. It indicates that the higher the Caprini score is, the greater the risk of DVT will occur, and the higher the D-dimer quantitative value is, the more likely the patients will suffer from thrombosis [32]. However, in this sample, the Caprini score showed a negative correlation with the D-dimer quantification value. Our speculation about the cause of this result was that the sample cases were incidental. There was accidental variation in the extracted samples, and the variation of medical data may lead to wrong conclusions [33]. This situation deserves attention and discussion in future studies.