Research on risk factors for deep vein thrombosis in elderly patients with upper limb fractures and establishment of Nomogram prediction model

doi:10.21203/rs.3.rs-3978909/v1

Purpose

In this study, we aimed to investigate the risk factors for the development of deep vein thrombosis (DVT) in elderly patients with upper extremity fractures, construct and validate a Nomogram prediction model.

Methods

Based on the inclusion and exclusion criteria, we retrospectively analyzed 359 patients with upper limb fractures over the age of 70 who were admitted to the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from September 2020 to September 2023. All the patients underwent color Doppler vascular ultrasound of all four limbs to determine the presence or absence of upper and lower limb venous thrombosis before surgery. The patients were divided into DVT and non-thrombosis groups based on thrombosis. Along with the prevalent complications in clinical elderly patients, the sex, smoking, the use of indwelling needle, hypertension, fracture site, hyperlipidemia, atrial fibrillation, diabetes, premature beat, body mass index (BMI), preoperative plasma D-dimer levels, and hemoglobin levels of the patients in the two groups were analyzed by performing univariate and multivariate analysis. We also investigated the relationship between different factors and DVT in patients. All patients were divided into a modeling group (n = 251) and validation group (n = 108) in a ratio of 7:3. Logistic regression was used to construct a Nomogram prediction model and internal validation was performed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were drawn to evaluate the predictive efficiency, accuracy and clinical benefit of the nomogram model, and the validation group was used to evaluate the feasibility of the nomogram.

Results

In total, 38 cases of DVT were found in 359 patients, and the incidence rate of thrombosis was 10.58%. High blood pressure, hyperlipidemia, diabetes, anemia, BMI > 25kg/m² and shoulder periarticular fracture are independent risk factors for deep vein thrombosis in upper limb fractures. The results of univariate and multivariate analysis revealed significant differences in terms of smoking, hyperlipidemia, diabetes, atrial fibrillation, anemia, fracture location, indwelling needle, and BMI between the two groups (p < 0.05). The periarticular fracture of the shoulder joint is a common site for the occurrence of DVT. No significant difference was found in terms of plasma D-dimer levels and premature beats with thrombosis between the two groups (p < 0.05). Furthermore, we also found that DVT is not associated with a history of hypertension but is associated with blood pressure levels, which showed significant differences between the two groups (p < 0.05). The ROC curve analysis showed that the area under the curve (AUC) of the prediction nomograph model was 0.972(95%CI: 0.952–0.992)for the modeling group and 0.860༈95%CI༚0.699-1.000༉for the validation group, with a sensitivity of 96.3% and 81.8%, and a specificity of 88.8% and 87.6%, respectively, with a good discrimination. The calibration curve of two groups showed that the predictive probability of the model was in high consistency with the actual incidence. The decision curve analysis (DCA) results in both groups showed that the nomograph model had good net returns.

Conclusions

The high-risk factors for the development of DVT in elderly patients with upper extremity fracture include smoking, high blood pressure, diabetes mellitus, hyperlipidemia, atrial fibrillation, anemia, BMI > 25kg/m², indwelling needle, and periprosthetic shoulder fracture. These factors should be paid attention to in clinical settings, and the relevant evaluation methods should be improved, to prevent the occurrence of DVT and pulmonary embolism (PE).

Upper extremity fracture

DVT

D-dimer

Advanced age

Risk factors

Nomogram

Venous thromboembolism (VTE), includes PE and DVT, which occur in the upper or lower limbs[1, 2]. A previous study reported that the mortality rate of patients with untreated PE is as high as 30%[3]. DVT impairs venous return due to abnormal deep vein coagulation, which usually occurs in the lower limbs and is a prevalent complication in orthopedics[4–6]. Previous studies have reported that fracture site, diabetes, and age are crucial factors affecting DVT occurrence in patients with lower limb fractures[7, 8]. Due to the continuous advancements of guidelines, the incidence of DVT in patients with traumatic fractures of lower limbs has considerably decreased. However the relationship between upper limb fractures and DVT is rarely reported[9, 10]. Previous studies have reported that the incidence of DVT in the upper extremity is higher than that in the lower extremity[2], DVT in patients with upper extremity traumatic fracture lead to the development of thrombus detachment[11], which can cause high-risk complications, such as PE, and seriously threaten the safety of patients. Many studies have reported that advanced age can increase the risk of DVT in patients with fractures[12, 13]. Due to the increase in the aging population, the risks of fractures in elderly patients are also increasing. Therefore, the risk factors of DVT in elderly patients with upper limb fractures should be investigated to provide guidance for the development of relevant clinical prevention programs.

1.1 Patients

We retrospectively analyzed patients with upper limb fractures over the age of 70 years in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from September 2020 to September 2023. Based on the inclusion and exclusion criteria, 359 patients were included, including 95 males and 264 females. The requirement for informed consent was waived as this is a retrospective study.

1.2 Diagnostic, inclusion, and exclusion criteria

(1) The diagnostic criteria of DVT[14]: Absence of venous flow at the site of limb examination, inability of the lumen to be deflated, filling defects, loss of fluctuations in venous flow associated with respiration, solid echoes in the lumen, decreased degree of increase in venous return in the compressed distal limb, and reduction in the degree of increase in blood flow in the released proximal limb.

(2)The inclusion criteria were as follows: Age ≥ 70 years old; patients with upper limb fracture due to trauma; patients with fresh fracture (within 1-week post-injury); patients with closed fracture; patients who underwent vascular color Doppler ultrasound prior to surgery, such as upper limb vascular color Doppler ultrasound or lower limb vascular color Doppler ultrasound; and no previous history or familial genetic history of thrombosis.

(3)The exclusion criteria were as follows: incomplete case data; two or more fractures; complications due to serious medical diseases, such as malignant tumors; taking anticoagulant drugs within one week of injury; with hemophilia or coagulation disorders; surfing operation within three months; and taking hormone drugs for a long time.

1.3 Clinical data

The clinical data of the patients were retrospectively analyzed, and the following data were recorded in an Excel sheet: fracture site, sex, smoking, indwelling needle, plasma D-dimer levels (D-dimer ≤ 0.5µg/mL was normal group, and > 0.5µg/mL abnormal group), hemoglobin levels (bounded by moderate anemia and above, hemoglobin < 90g/L was considered anemic, and > 90g/L was considered non-anemic), BMI (BMI ≤ 25 kg/m² was considered normal and > 25kg/m² was considered was considered overweight), information on whether there was a combination of hypertension, hyperlipemia, diabetes mellitus, atrial fibrillation, premature beats, and other medical diseases. The fracture sites were divided as follows: forearm (ulnar radius) fracture, periprosthetic wrist fracture, elbow fracture, humeral stem fracture, periprosthetic shoulder fracture, and clavicle fracture.

1.4 Correlation analysis

SPSS26.0 was applied to statistically analyze the clinical data. Firstly, a unifactorial analysis was carried out on the clinical data of the two groups, observing the correlation between the sex, smoking, indwelling needle, fracture site, plasma D-dimer levels, hemoglobin levels, BMI, whether they were combined with hypertension, diabetes mellitus, hyperlipidemia, atrial fibrillation, premature beats. Then multifactorial analysis was performed to synthesize the effects of different factors on DVT.

1.5 Statistical analysis

SPSS26.0 was used to analyze the data. The qualitative data were compared between groups by c² test or Fisher exact probability method, and the multivariate analysis was performed by binary Logistic regression analysis. R software (v4.3.2) and Logistic regression analysis were used to construct a nomogram model of independent risk factors for upper limb fractures complicated with DVT. The discrimination of the nomogram model was evaluated by ROC curve and DCA curve, and the accuracy of the nomogram model was evaluated by plotting a calibration curve.

2.1 Univariate analysis of DVT in patients with upper limb fractures

There were 38 cases of DVT among the 359 patients (Figure 1), with an incidence of 10.58%, which included 12 cases of lower extremity DVT and 26 cases of upper extremity DVT, including 9 in the axillary vein, 10 in the brachial vein, 4 in the subclavian vein, and 3 in the axillary vein-subclavian vein commissure. The risk factors of DVT in patients with upper limb fractures include smoking, indwelling needle, diabetes mellitus, atrial fibrillation, hyperlipidemia, anemia, BMI > 25kg/m², and fracture site (p < 0.05). There was no significant difference in D-dimer levels and premature beats between the two groups (p > 0.05). We found that a history of hypertension was not statistically significant between the two groups, whereas high blood pressure (systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg) was statistically significant between the two groups (p < 0.05) (Table 1).

Table 1. Univariate analysis of DVT in patients with upper limb fractures

Factors	patients	DVT patients	c²	P
sex			1.412	0.235
Male	95	7
Female	264	31
History of Hypertension			0.210	0.647
Yes	102	12
No Excessive blood pressure Yes No	257 47 312	26 9 29	4.191	0.041
Hyperlipidemia			16.947	0.000
Yes	77	18
No	282	20
Diabetes			9.730	0.002
Yes	87	17
No	272	21
Atrial Fibrillation			12.818	0.000
Yes	16	6
No Premature Beats Yes No Anemic Yes No BMI ＞25 ≤25	343 33 326 162 197 53 206	32 4 34 28 10 14 24	0.091 13.998 7.340	0.763 0.000 0.007
smoking			5.376	0.020
Yes	87	15
No	272	23
Indwelling Needle			4.900	0.027
Yes	247	32
No	112	6
Plasma D-dimer levels			0.088	0.767
Abnormal	317	33
Normal	42	5
Fracture Site			10.901	0.039
Clavicle	33	1（3.03%）
Shoulder	115	19（16.52%）
Humerus Shaft	26	4（15.38%）
Elbow	33	5（15.15%）
Forearm	22	1（4.55%）
Wrist	131	8（6.17%）

2.2 Multifactorial analysis of DVT in upper extremity fractures

We found that periprosthetic fracture, high blood pressure, diabetes mellitus, hyperlipidemia, anemia, and BMI > 25kg/m² were independent risk factors for elderly patients with upper extremity fractures, with statistically significant differences. However, smoking, indwelling needle, atrial fibrillation is not an independent risk factor (Table 2).

Table 2. Multifactorial analysis of DVT in upper extremity fractures

Factors	B	SE	Wald	P
Shoulder	-3.168	1.319	5.765	0.016
Excessive blood pressure	-3.766	0.852	19.559	0.000
Hyperlipidemia	-2.261	0.707	10.235	0.001
Diabetes	-3.351	0.773	19.798	0.000
Atrial Fibrillation	-2.000	1.118	3.203	0.073
Premature Beats	0.724	1.060	0.467	0.494
Anemic	-3.470	0.811	18.322	0.000
BMI	-1.490	0.677	4.836	0.028
Smoking	-0.289	0.679	0.182	0.670
Indwelling Needle	0.049	0.609	0.007	0.936
Plasma D-dimer levels	-18.555	6.397	9.765	0.936

2.3 Constructing the nomogram model

The nomogram prediction model was constructed based on the results of logistic regression analysis. (Figure 2)

Figure 2 Nomogram Predictive Modeling of Upper Extremity Fractures with DVT

2.4 Validating the nomogram model

Plotting the ROC curve for upper extremity fractures associated with DVT, the area under the curve (AUC) of the prediction nomograph model was 0.972（95%CI：0.952-0.992）for the modeling group and 0.860（95%CI：0.699-1.000）for the validation group, with a sensitivity of 96.3% and 81.8%, and a specificity of 88.8% and 87.6%, respectively, with a good discrimination (Figure 3a、3b). The calibration curve of two groups showed that the predictive probability of the model was in high consistency with the actual incidence (Figure 4a、4b). The decision curve analysis (DCA) results in both groups showed that the nomograph model had good net returns (Figure 5a、5b).

Figure 3a 、3b ROC curve of prediction nomogram model of modeling group and validation group

DVT is a prevalent complication due to traumatic fracture, which is mostly found in lower extremity fractures[5]. Previous studies have reported that the incidence of DVT in lower extremity fractures is 32%[7, 14, 15]. The incidence of upper extremity fracture DVT is lower but can easily cause PE if it occurs[15, 16]. PE is a serious complication in patients with trauma, with a high mortality rate[17]. Most cases of PE occur within 72 hours post-injury. Previous studies have reported that the incidence of early post-traumatic PE is as high as 10%–42%, with a mortality rate of approximately 50%[10, 18] . Elderly patients with fractures have a higher risk of developing DVT; therefore, early prediction and intervention of DVT in elderly patients with upper extremity fractures is of immense significance.

Venography is considered the gold standard diagnostic procedure for venous thrombosis[19]; however, it is complicated, expensive, and invasive. Therefore, color Doppler vascular ultrasound is gradually being considered the diagnostic method of choice.

In this study, the incidence of DVT in upper limb fractures was 10.58%, which was higher than the 1.6% incidence reported by Rafael S[20] and the 0.66% incidence reported by Davide[21] . This can be because only elderly patients were included in this study, and there exists a strong correlation between advanced age and thrombosis. Some researchers have reported that there is no direct correlation between DVT of upper limb fractures and medical complications[21] . The results of this study revealed that upper limb fracture thrombosis exhibits the highest incidence of periprosthetic shoulder fractures. Univariate analysis showed no significant difference in the history of hypertension between the two groups. We hypothesize that most hypertensive patients may be taking antihypertensive drugs, which can decrease their blood pressure levels to normal levels. Therefore, we measured the blood pressure of patients with high blood pressure again, with systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg as the standards. Univariate analysis was again performed and found that smoking, indwelling needle, comorbid high blood pressure, hyperlipidemia, diabetes mellitus, atrial fibrillation, anemia, BMI >25 kg/m², and fracture site were risk factors for DVT, but smoking, indwelling needle, atrial fibrillation was not an independent risk factor in the multivariate analysis.

Excessive blood pressure

Unlike other studies, the present study reports that there is no direct correlation between the history of hypertension and thrombosis. However, excessive blood pressure increases the risk of thrombosis, which might be because high blood pressure leads to a collision of blood cells in the bloodstream with the vessel wall. This disrupts some of the blood cells and stimulates the coagulation mechanism.

Hyperlipidemia

Plasminogen activator inhibitor-1 (PAI-1), a marker of fibrinolytic activity, is produced and retained by vascular endothelial cells. Elevated levels of PAI-1 increase the risk of thrombosis[22, 23]. The content of PAI-1 in plasma of patients with hyperlipidemia are higher than that of patients without hyperlipidemia[24], so patients with hyperlipidemia have a tendency to thrombosis. At the same time, trauma causes endothelial cell injury and releases more PAI-1, both of which raise the risk of thrombosis.

Diabetes

Diabetes, as is well known, can impact lipid metabolism, and dyslipidemia can result in aberrant fibrinolysis and coagulation, which raises the risk of thrombosis. Diabetes induces hypercoagulability in the blood, one of the three major risk factors for thrombosis. The blood coagulation system may be impacted by diabetes-related vascular inflammation, and the combination of these variables raises the risk of thrombosis.

Anemia

Unifactorial and multifactorial analyses revealed that anemia increases the risk of thrombosis. Virchow proposed the following three important factors for thrombosis: stagnant blood flow, hypercoagulable state of blood, and vascular endothelial damage[25] . Our study reports that anemia may slow down blood flow in the body, causing thrombosis.

D-dimer

Previous studies have reported that in lower extremity fractures, increased levels of D-dimer indicate the increased activity of coagulation and fibrinolysis. Furthermore, the blood is in a hypercoagulable state, which increases the risk of thrombosis[26, 27] . In this study, the D-dimer levels were not statistically different between the two groups. D-dimer elevation is related to thrombosis, diabetes, infection, malignant tumors, and advanced age can cause D-dimer elevation; therefore, the prediction of DVT by D-dimer has a certain value and warrants further studies.

BMI

Obesity is not recognized as an independent risk factor for DVT, but studies have proved that there is a causal relationship between obesity and DVT. Reducing BMI can decrease the probability of DVT[28]. In this study, BMI > 25kg/m²was an independent risk factor for DVT in patients with upper limb fractures. First, obesity increases the risk of type 2 diabetes. Secondly, adipokines in adipose tissue affect glucose and lipid metabolism, increase coagulation activity, reduce fibrinolytic activity, and induce chronic inflammation, thereby increasing the risk of thrombosis.

Smoking

Smoking is a well-established risk factor for atherosclerotic disease, but its role as an independent risk factor for VTE remains controversial[29]. In our study, smoking increased the risk of DVT after upper limb fracture in the elderly, but it was not identified as an independent risk factor. Then we found that in male patients, there was no significant difference in smoking habits between male patients in the DVT group and those in the non-DVT group (p > 0.05). However, among female patients, there was a significant difference in smoking habits between the two groups (p < 0.05). This is consistent with the study by Regina[30]. We only collected data on the presence or absence of smoking behavior, and did not include specific details such as smoking time, number of cigarettes, etc., which may also have an impact on thrombosis.

Indwelling needle

The main risk factors for deep vein thrombosis of the upper extremity (DVT-UE) are central venous catheter(CVC), peripherally inserted central venous catheters (PICC), and cardiac pacemakers[31]. Foreign bodies in the vascular system represent the most significant independent risk factor for DVT-UE[32]. Therefore, patients with intravenous catheters should undergo regular testing for upper and lower extremity thrombosis, irrespective of the presence of fractures. The incidence of upper extremity deep venous thrombosis is increasing, which may be related to the rise in CVC, PICC, and pacemaker implantation[33-35]. In this study, the use of an indwelling needle is identified as a risk factor for DVT in upper limb fractures, but it is not considered an independent risk factor. On one hand, the presence of the needle inserted into the vein as a foreign object will increase the risk of thrombosis. On the other hand, when compared to CVC, PICC, and pacemaker, an indwelling needle has a smaller volume, causes less trauma, and has a shorter retention time, so it cannot be considered an independent risk factor.

The incidence of thrombosis in patients with malignant tumors is 2–3 times higher than that in non-tumor patients[16, 36, 37]. Therefore, more attention should be paid when there is a combination of malignant tumors or pathologic fractures. In elderly patients with lower extremity fractures, the incidence of DVT increases as the preoperative waiting time increases. Therefore, the preoperative waiting time, such as most prolonged surgical timing due to glycemic problems, should be minimized in elderly patients with upper extremity fractures, which requires the extensive experience of clinicians in regulating blood glucose levels to normal levels as quickly as possible. In 38 cases of DVT, 12 cases have occurred in the lower extremities. It is possible that the pain caused by fractures led to their refusal to walk around. None of the patients reported experiencing pulmonary embolism. However, this does not necessarily mean that pulmonary embolism did not occur. Due to financial constraints, CT scans are not performed when there are no significant symptoms.

This study has some limitations. The sample size included in this study was small, and we only included elderly patients with fractures. Since we excluded patients with malignant tumors and all of the patients in our study did not have cardiac pacemakers, PICCs, or CVCs prior to surgery, more research is required to understand the DVT circumstances in these patients.

To summarize, the incidence of DVT in patients with upper limb fractures is lower than that of lower limb fractures; however, its risk should not be underestimated. For elderly patients with upper limb fractures, especially peri-shoulder fractures, as well as those with hyperlipidemia, diabetes mellitus, high blood pressure, anemia, obesity, and internal medicine diseases, such as malignant tumors, the clinical testing should be strengthened. Furthermore, anticoagulant drugs should be given when necessary to prevent the risk of PE and decrease the clinical sequelae associated with thrombosis.

Conflict of Interest: The authors declare that they have no conflict of interest.

Funding：There is no funding source.

Availability of data and materials: The datasets used and/or review during the current study are available from the corresponding author on reasonable request.

Consent for publication: Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.

Informed consent: Informed consent was obtained from all individual participants included in the study.

Acknowledgements: Not applicable.

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No competing interests reported.

Research on risk factors for deep vein thrombosis in elderly patients with upper limb fractures and establishment of Nomogram prediction model

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Abstract

Purpose

Methods

Results

Conclusions

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Introduction

1. Information and Methods

2. Results

3. Discussion

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References

Additional Declarations

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