Risk Estimation of Deep Venous Thrombosis in Polytrauma Patients with Traumatic Brain Injury: A Nomogram Approach

doi:10.21203/rs.3.rs-4988270/v1

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Research Article

Risk Estimation of Deep Venous Thrombosis in Polytrauma Patients with Traumatic Brain Injury: A Nomogram Approach

https://doi.org/10.21203/rs.3.rs-4988270/v1

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Background

Deep venous thrombosis (DVT), known to be a major factor in poor outcomes and death rates, is common after polytrauma with traumatic brain injury (TBI). In this study, a nomogram will be developed to predict the risk of DVT in polytrauma patients with TBI, since there is currently no specific and convenient diagnostic method.

Methods

A retrospective and observational trial was conducted between November 2021 and May 2023.The predictive model was created using a group of 349 polytrauma patients with TBI in a training set, with data collected between November 2021 and August 2022.A nomogram was presented after using multivariable logistic regression analysis to create the predictive model. Validation of the model was conducted internally. A separate group for validation included 298 patients seen consecutively between August 2022 and May 2023.

Result

647 trauma patients were included in the study. Out of these, 349 individuals were part of the training group while 298 were part of the validation group. Training cohorts reported 32.1% and validation cohorts reported 31.9% DVT. Age, Smoking, Injury Severity Score (ISS), Glasgow Coma Scale (GCS), D-dimer, Mechanical ventilation (MV) and Application of Vasoactive Drugs (AVD) comprised the individualized prediction nomogram. The model exhibited strong discrimination, achieving a C-index of 0.783 and a statistically insignificant result (P = 0.216) following the Hosmer-Lemeshow test. Nomogram calibration plots and decision curve analysis showed the nomogram's utility in predicting DVT.

Conclusion

Our study characterized the incidence of DVT in polytrauma patients with TBI and further emphasized that it represents a substantial health concern, as evidenced by its frequency. Using this nomogram, it is possible to predict DVT in polytrauma patients with TBI based on demographics and clinical risk factors.

Polytrauma

Traumatic Brain Injury

Deep Venous Thrombosis

Nomogram

Polytrauma, defined as injuries with an Abbreviated Injury Scale (AIS) score of 3 or higher in at least two body regions (2AIS ≥ 3), is a complex condition associated with poor outcomes and high mortality rates resulting from severe damage and complicated complications, making it a persistent health concern[1, 2]. An growing amount of proof indicates that over half of polytrauma patients present with a concomitant traumatic brain injury (TBI), which greatly affects their chances of long-term survival[3]. Virchow's Triad, consisting of trauma-induced stasis, a hypercoagulable state, and endothelial injury, is a common primary factor leading to high early mortality rates (30–50%) after injury, with deep venous thrombosis (DVT) being a pathological result of coagulation dysfunction[4]. Up to 20% of polytrauma patients suffer lower extremity DVT, which is a leading cause of death for those who survive the initial trauma[5, 6]. Direct venous injury, increased blood clotting, and skeletal fixation all contribute to polytrauma patients' high DVT rates. Additionally, TBI patients are also at higher risk for blood clot complications because of immobility resulting from neurologic injuries, as well as sedatives and neuromuscular blocking agents[7–10].It is consistent with our own clinical practice experience that polytrauma patients with TBI are at a greater risk than polytrauma patients without TBI of developing DVT.

Managing bleeding and blood clotting presents a crucial and formidable challenge in instances of polytrauma, particularly in cases of traumatic brain injury (TBI), where surgeons must carefully weigh the risks of ongoing brain hemorrhage against the development of secondary thrombotic events such as deep venous thrombosis (DVT)[11].The commencement of chemical DVT prophylaxis may be postponed in polytrauma patients with TBI due to the concern of exacerbating intracranial hemorrhage. This delay in prophylaxis may elevate the likelihood of morbidity and mortality attributable to DVT[1]. The decision to initiate low-molecular-weight heparin or unfractionated heparin is typically made by the neurosurgery or trauma team in an acute care hospital[12]. However, due to the lack of definitive guidelines, chemical prophylaxis is usually initiated in response to stable bleeding conditions observed by repeated head CT scans or based on physician clinical expertise[9].

It is essential to promptly recognize polytrauma patients with TBI who are at risk of developing DVT in order to provide effective treatment and optimize resource allocation. At present, the diagnosis of DVT depends on evaluating D-dimer (DD) concentrations and performing Venous Doppler ultrasound (VDU)[13].However, DD testing has limited specificity in diagnosing DVT, especially in polytrauma patients who have complicating factors like infection. Moreover, polytrauma patients in need of mechanical ventilation may not have easy access to VDU[14].This study is focused on creating a nomogram to predict the likelihood of DVT in polytrauma patients with TBI due to the absence of a simple and specific diagnostic tool.

2.1 Study design and Patients

This study was conducted with approval from the institutional review board, and involved retrospective observation with informed consent from participants. From November, 2021 to May, 2023, every patient was hospitalized in the Traumatic Intensive Care Unit (TICU) at the Advanced Trauma Center located in Tongji Hospital (Wuhan). 647 patients in a row were eligible and categorized into DVT and N-DVT groups. Patients meeting the criteria from November 2021 to August 2022 were selected for the training group to create the nomogram, while those from August 2022 to May 2023 were assigned to the validation group .Approval for the current research was granted by the ethics committee of Tongji Hospital under the reference TJIRB20200720. Patients and their legal representatives gave their consent to the collection of their data. The entry criteria were: 1)Diagnosed as polytrauma with TBI;2) Admission to the hospital within 24 hours after trauma;3) age over 18 years. The exclusion criteria were: 1)Pregnancy;2)A history of VTE or received antithrombotic/anticoagulant therapy prior to admission;3) Previous cerebrovascular accident history, including hemorrhagic and ischemic; 4)Admission after 24h post-trauma. All participants were administered venous thromboprophylaxis treatment and management in accordance with current guidelines[15].Specifically, on the basis of preventing further injury and progressive cerebral hemorrhage, patients received early venous thromboprophylaxis, including chemical prophylaxis (either low molecular weight heparin or nadroparin calcium, administered once daily)[16].

2.2.Data Collection

Baseline clinical features were collected within 48 hours of admission by retrieving information from electronic medical and nursing files. The data contained details about the population, such as demographic information (e.g. age, sex), medical history (e.g. Injury Severity Score (ISS), Glasgow coma scale (GCS), cause and location of injury), laboratory results (e.g. D-dimer levels, lactate levels, APTT), and details of the hospital course (e.g. use of mechanical ventilation). Further, a VDU evaluation of the deep venous system and detection of DVT was performed on the lower extremities.

2.3.Diagnosis

The updated 'Berlin criteria' for polytrauma now includes a patient with a score of 3 or higher on the Abbreviated Injury Scale (AIS) in at least two body regions (2AIS ≥ 3) along with at least one additional parameter such as hypotension, unconsciousness, acidosis, coagulopathy, or being over 70 years old [17]. DVT was characterized by abnormalities detected on VDU, like dilated veins that cannot be compressed or shadows within the vein that indicate thrombosis [18]. TBI was characterized by the detection of subdural hemorrhage, epidural hemorrhage, focal contusion, subarachnoid hemorrhage on computed tomography (CT) and diffuse axonal injury on clinical symptoms and magnetic resonance imaging (MRI) according to recommended guidelines[19, 20]. Obesity is defined as a body mass index of 30 or greater[21]. Every subject who was enrolled received uniform treatment and care according to established guidelines for traumatic brain injury and polytrauma[22]. Chemical prophylaxis involves the initiation of low-molecular-weight heparin following admission, while the application of vasoactive drugs entails the use of norepinephrine and dopamine throughout the patient's hospitalization. The specification of the drugs used was according to the existing research[23].

2.4 Statistical Analysis

Before conducting the analysis, the data was checked for normal distribution and equality of variance. Frequency counts and percentages were used to analyze categorical variables, while median/IQR or mean ± SD were utilized to assess continuous variables. Statistical analyses were conducted using the Student t-test, Mann-Whitney U-test, and the two-tailed test. To determine the independent factors linked to DVT in polytrauma with TBI, a multivariate logistic regression analysis was performed. Following this, a nomogram was created using the findings from the multivariable logistic regression analysis and the rms software in the R coding language. The C-index was employed for evaluating model accuracy, bootstrap validation for detecting overfitting, calibration plot for assessing nomogram performance, and ROC curve analysis for predictive accuracy. Data analysis was conducted using R software (version 3.6.3) and GraphPad Prism software 9.3.1.

3.1 Sociodemographic Characteristics, laboratory parameters and events of the hospital course in Trauma Patients

647 trauma patients meeting the inclusion criteria were included in the study cohort. Out of these, 349 individuals were part of the training group while 298 were part of the validation group. Table 1 provides patient characteristics for both the training and validation cohort. The prevalence of DVT shows no significant differences between the two cohorts (P = 0.99). In the training and validation cohorts, the occurrence of DVT was 32.1% and 31.9%, respectively. The clinical characteristics did not show any notable discrepancies between the training and validation cohorts, whether in the DVT and N-DVT groups, supporting their selection as training and validation cohorts.

Table 1

Comparison of Sociodemographic Characteristics ,laboratory parameters and events of the hospital course between DVT(+) and DVT(-) in Training and Validation Cohorts.
Variables	Training(n = 349)			Validation(n = 298)
Variables	DVT(+,112)	DVT(-,237)	P	DVT(+,95)	DVT(-,203)	P
Age	56.1 ± 12.3	47.6 ± 11.3	< 0.01	57.3 ± 11.5	46.2 ± 10.4	< 0.01
Male	84(75.0)	177(74.7)	0.95	70(73.7)	153(75.4)	0.87
Obesity	20(17.9)	19(8.0)	< 0.01	18(18.9)	16(7.9)	< 0.01
Hypertension	21(18.9)	41(17.3)	0.86	17(17.9)	37(18.2)	0.93
Hyperlipidemia	28(25.0)	65(27.4)	0.73	24(25.3)	49(24.1)	0.95
Smoking	76(67.9)	86(36.3)	< 0.01	67(70.5)	84(41.4)	< 0.01
Cause of injury	-	-	0.21	-	-	0.25
Traffic accident	79(70.5)	165(69.6)	-	69(72.6)	151(74.4)	-
high-energy fall	26(23.2)	55(23.2)	-	21(22.1)	41(20.2)	-
other	7(6.3)	17(7.2)	-	5(5.3)	11(5.4)	-
Anatomical location of injury	-	-	0.32	-	-	0.44
Thoracic injury	58(51.8)	129(54.4)	-	51(53.7)	114(56.2)	-
Abdominal injury	50(44.6)	94(39.7)	-	45(47.4)	104(51.2)	-
Pelvic and limbs fracture	81(72.3)	116(48.9)	-	73(76.8)	124(61.1)	-
ISS	32.8 ± 6.9	30.4 ± 6.1	< 0.01	33.5 ± 6.5	31.2 ± 7.1	< 0.01
GCS	8.2 ± 2.1	10.4 ± 1.7	< 0.01	8.9 ± 2.4	10.1 ± 1.8	< 0.01
Location of brain injury			0.07			0.08
subdural hemorrhage	42(37.5)	60(25.3)		37(38.9)	53(26.1)
epidural hemorrhage	24(21.4)	68(28.7)		20(21.1)	55(27.1)
focal contusion	71(63.4)	113(47.7)		61(64.2)	100(49.3)
subarachnoid hemorrhage	48(42.9)	99(41.8)		39(41.1)	82(40.4)
Blood transfusions	-	-	0.12	-	-	0.16
Red cells	39(34.8)	56(23.6)	-	34(35.8)	46(22.7)	-
Platelets	13(11.6)	21(8.9)	-	9(9.5)	15(7.4)	-
Fresh frozen plasma	27(24.1)	30(12.7)	-	25(26.3)	19(9.4)	-
Lactate	1.9 ± 0.8	1.8 ± 0.7	0.24	1.7 ± 0.6	1.8 ± 0.8	0.28
PT	15.1 ± 2.4	14.5 ± 3.2	0.08	16.3 ± 2.6	16.7 ± 2.4	0.19
APTT	40.1 ± 11.8	38.6 ± 10.5	0.23	41.6 ± 12.5	39.8 ± 11.3	0.22
D-dimer	15.3 ± 6.2	3.6 ± 1.3	< 0.01	17.6 ± 6.4	3.7 ± 1.8	< 0.01
Mechanical ventilation	34(30.4)	19(8.2)	< 0.01	33(34.7)	13(6.4)	< 0.01
First day of chemical prophylaxis ≤ 3d	25(22.3)	140(60.1)	< 0.01	27(28.4)	126(62.1)	< 0.01
Application of vasoactive drugs (norepinephrine or dopamine)	42(37.5)	11(4.7)	< 0.01	33(34.7)	7(3.4)	< 0.01
Skeletal traction	34(30.4)	60(25.3)	0.39	32(33.7)	55(27.1)	0.30
Operation	89(79.5)	165(69.6)	0.07	71(74.7)	133(65.5)	0.14
Craniotomy	35(31.3)	69(29.1)	0.78	27(28.4)	52(25.6)	0.71
Internal fixation of fractures	45(40.2)	81(34.2)	0.33	36(37.9)	68(33.5)	0.54
Others	9(8.0)	15(6.3)	0.72	8(8.4)	13(6.4)	0.70
All values are presented as mean ± standard deviation or number(percentage);DVT: deep vein thrombosis; GCS: Glasgow coma scale; ISS: injury severity score; P < 0.05 is statistically significant.

In univariate analysis, Age, Obesity, Smoking, D-dimer levels, ISS score, GCS score, Mechanical ventilation, First day of chemical prophylaxis ≤ 3d and Application of vasoactive drugs were risk factors for DVT in the training cohort .Notably, delayed initiation of chemical prophylaxis was found to be particularly risky for developing DVT in this group. Table 1 contains important predictors for DVT.

3.2.Multivariate analyses of relative factors for DVT

Following univariate examination, variables with a P value < 0.05 in the training cohort were chosen for multivariate analysis utilizing a stepwise multiple regression approach. Multivariate logistic regression analysis indicated that a higher Age(OR1.725, 95%CI 1.215–5.314 ,P = 0.016), Smoking(OR 1.976, 95%CI1.142-4.642,P = 0.003), ISS(OR 3.612, 95%CI1.354-8.437,P = 0.006), D-dimer (OR2.847, 95% CI 1.243–6.831, P = 0.014), Mechanical ventilation (OR 1.824, 95% CI, 1.011–5.835, P = 0.024), Application of vasoactive drugs (OR 2.018, 95% CI 1.164–6.312, P = 0.013) and a lower GCS (OR0.425 95% CI 0.237–0.863, P = 0.009) were all linked to an increased risk of DVT.(Table 2)

Table 2

Multivariable Logistic Regression Model for Predicting Development of deep venous thrombosis in polytrauma patients with traumatic brain injury in the Training Cohort.
Variables	B	OR	odds ratio (95% CI)	P
Age	0.532	1.725	1.215–5.314	0.016
Obesity	0.974	1.125	0.894–1.436	0.152
Smoking	1.026	1.976	1.142–4.642	0.003
ISS	1.283	3.612	1.354–8.437	0.006
GCS	-0.856	0.425	0.237–0.863	0.009
D-dimer	1.206	2.847	1.243–6.831	0.014
Mechanical ventilation	0.601	1.824	1.011–5.835	0.024
First day of chemical prophylaxis ≤ 3d	1.263	1.783	0.354–3.742	0.073
Application of vasoactive drugs	0.702	2.018	1.164–6.312	0.013

3.3. Construction of the Risk Score and Nomogram-Based Calculator

A nomogram, serving as a visual aid, displays the importance of different factors in a model and can assist in predicting the likelihood of certain results. The training cohort utilized these risk factors that were independently linked to create a nomogram to estimate DVT risk .When employing the nomogram, it is essential to identify the position of each factor on the corresponding axis, draw a line connecting the points axis based on the assigned number of points, calculate the total points accumulated from all factors, and trace a line from the total points axis to determine the probabilities of DVT at the lowermost section of the nomogram(Fig. 1). Through the training process, the model was internally validated. Based on the calibration curve, the prediction and observation of DVT were in good agreement in the training cohort because of a closer ft to the diagonal dashed line that represents an ideal evaluation by a perfect model. The Hosmer-Leme exhibition examination resulted in a statistically insignificant finding (P = 0.216), indicating that there was no deviation from ideal alignment (Figur2A).The nomogram had an AUC of 0.783 in the training group when analyzing the ROC curve. Additionally, the training model's net benefit rate closely matched the validation model's within the threshold range of 0.0–1.0.

DVT is classically caused by stasis, hypercoagulability, and endothelial injury, called Virchow's Triad, which occurs frequently in polytrauma patients[24]. Meanwhile, in cases of multiple traumatic injuries, bleeding can result in the activation of blood clotting factors, potentially resulting in DVT [25]. Following the initial survival of the acute phase on day one, the primary worries for these individuals involve severe complications that could be dangerous to life, such as DVT[26]. The occurrence of DVT in trauma cases ranges from 7–60%, varying based on patient characteristics, detection techniques, and preventive measures[27, 28]. In our literature, the incidences of DVT were 32.1% in polytrauma patients with TBI. In our previous literature, significant difference in the DVT rate between polytrauma patients with TBI and those with isolated TBI (31.9% vs 20.2%, P < 0.05), despite similar GCS scores. Polytrauma patients with TBI had a higher DVT rate than polytrauma patients without TBI (31.9 versus 22.0%, P < 0.05), even though ISS was not different[1].A possible explanation for this might be that many people who suffer TBIs have immobility, weakness, and bed rest, as well as other injuries that can increase their risk of venous stasis[12]. DVT is three to four times more likely to occur in people suffering from traumatic brain injury (TBI) according to a 2009 study [29].

Our research identified older age, smoking, elevated D-dimmer levels, higher ISS scores, lower GCS scores, mechanical ventilation, and increased use of vasoactive drugs as risk factors for DVT in polytrauma patients with TBI. DVT is commonly seen as a condition that affects older individuals more, with a much higher chance of developing DVT in individuals aged 40 and above than in younger patients [30].Additionally, the likelihood of developing DVT increases by about two times for each additional ten years of age [30]. D-dimer serves as an indicator of both the creation and breakdown of fibrin [31]. A bioinformatics study was done to look back and find neurosurgical patients who were tested for serum D-dimer levels and had a VDU to check for DVT. The research determined that the D-dimer procedure was successful in detecting DVT in patients who were hospitalized[14]. A clear connection has been found between DVT and the duration of ventilator use, as extended use of a ventilator leads to more time spent lying flat [32].

A nomogram was created and validated internally to predict DVT on an individual basis in polytrauma patients with TBI. The nomogram incorporates seven items of the Age, Smoking, ISS, GCS, D-dimer, MV and AVD. The risk score's performance was deemed satisfactory in the training cohort, achieving an accuracy of 0.78 based on AUC. Utilize the nomogram for predicting DVT to assess the likelihood of DVT in a patient admitted to the hospital. Typically, the seven necessary variables for assessing the risk of DVT are easily accessible. As showed in Fig. 1, the nomogram aligns well with our everyday clinical practice and is user-friendly. The calibration graphs and decision curve analysis of the nomogram for predicting DVT showed promise for practical use in clinical settings. In cases where the patient's likelihood of developing DVT is deemed low, the healthcare provider may opt for observation, while high-risk assessments could warrant intensive treatment or hospitalization in the ICU

The research has some restrictions, particularly due to its retrospective nature that limits the study's design. During our investigation, the detection of DVT was mainly based on ultrasound tests performed while the patient was in the hospital, which could result in missing DVT cases after discharge and minor DVT incidents. Furthermore, the inclusion of both surgical and non-surgical patients in the study may introduce an additional source of bias. Moreover, while the nomogram demonstrated strong performance, it is essential to conduct multicenter clinical validation to assess the external applicability of our nomogram.

Our research investigated the frequency of DVT in polytrauma patients with TBI and highlighted its significant impact on health outcomes. Additionally, we developed a nomogram that incorporates relevant factors to aid in the personalized prediction of DVT in this patient population.

Author Contributions: H.L. participated in the designing of the experiment, collection, analysis, and interpretation of data, and drafting the manuscript. T.C., D.C., J.L., S.C.,P.Z. and Z.L. collected and analyzed data. C.Z. contributed to the collection and analysis of data and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee at Tongji Hospital (approval date: 22 July 2020).

Informed Consent Statement: Informed consent was obtained from each patient or the patient’s legally authorized representative involved in the study.

Funding: Not applicable

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical, legal and privacy issues.

Acknowledgments: We thank the staff at the Department of Clinical Statistics for their assistance in statistical analysis.

Conflicts of Interest: The authors declare no conflict of interest.

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No competing interests reported.

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Risk Estimation of Deep Venous Thrombosis in Polytrauma Patients with Traumatic Brain Injury: A Nomogram Approach

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Abstract

Figures

1. Introduction

2. Patients and methods

2.1 Study design and Patients

2.2.Data Collection

2.3.Diagnosis

2.4 Statistical Analysis

3. Results

3.1 Sociodemographic Characteristics, laboratory parameters and events of the hospital course in Trauma Patients

3.2.Multivariate analyses of relative factors for DVT

3.3. Construction of the Risk Score and Nomogram-Based Calculator

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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