The Perioperative Deep Vein Thrombosis in Lower Extremities in Patients With Pelvic Fracture: A Case-Control Study

Effect of percutaneous vertebroplasty on the treatment of osteoporotic spinal fractures in elderly patients and risk factors for postoperative lower extremity deep vein thrombosis

OBJECTIVE

To evaluate the effectiveness of PVP (PVP) in treating osteoporotic spinal fractures in the elderly and analyze the risk factors for postoperative deep vein thrombosis (DVT) in the lower extremities.

METHODS

A total of 100 elderly patients with osteoporotic spinal fractures, treated between August 2019 and July 2021, were divided into two groups: PVP (research group, n=50) and conservative treatment (control group, n=50). Outcome measures, including injured vertebrae, pain levels, and treatment outcomes, were retrospectively analyzed. Patients who underwent PVPs were further categorized based on the presence of lower extremity DVT one month post-surgery. Logistic regression analysis was used to identify risk factors for post-surgical lower limb DVT.

RESULTS

PVP resulted in a significantly smaller posterior convexity angle of the injured spine and higher anterior, midline, and posterior edges of the injured spine compared to conservative treatment (P<0.001). Patients in the research group had significantly lower visual analogue scale (VAS) scores and higher treatment efficiency compared to those in the conservative treatment group (all P<0.05). Nine cases of lower extremity DVT were observed after PVP. Logistic regression analysis identified age, body mass, smoking, and diabetes as independent risk factors for post-surgical lower extremity DVT.

CONCLUSION

PVP improves spinal function and relieves pain in elderly patients with osteoporotic fractures. However, age, body mass, smoking, and diabetes are independent risk factors for postoperative lower extremity DVT.

Analysis of High-Risk Factors and Construction of a Nomogram Predictive Model for Deep Venous Thrombosis in Pelvic and Acetabular Fracture Patients Treated Conservatively

PURPOSE

This study aims to develop a predictive nomogram model to assist physicians in making evidence-based decisions and potentially reduce the incidence of deep venous thrombosis (DVT).

METHODS

We conducted a retrospective study, including patients admitted to the hospital from January 2014 to January 2022 with a closed, single pelvic or acetabular fracture. Comprehensive data were collected for each patient, encompassing demographics, injury characteristics, comorbidities, and results from laboratory tests and lower extremity ultrasounds. Potential risk factors were identified by univariate and multivariate logistic regression analyses. The predictive model was constructed and then internally validated. Calibration accuracy was assessed using a calibration slope and the Hosmer-Lemeshow goodness-of-fit test. The discrimination of the nomogram model was evaluated using the C-statistic.

RESULTS

Out of 232 individuals who underwent conservative treatment, 57 (24.6%) were classified into the DVT group and 175 (75.4%) into the non-DVT group based on lower extremity ultrasound findings. Predominantly, patients were aged between 41 and 65 in both groups. Body mass index (BMI) comparison showed that 54.29% (95/175) of the non-DVT group fell within the healthy weight range, while 45.61% (26/57) in the DVT group were overweight. Notably, the proportion of obesity in the DVT group was more than double that in the non-DVT group, indicating a higher DVT risk with increasing BMI (P=0.0215). Lower red blood cell (RBC) counts were observed in DVT patients compared to non-DVT ones (P<0.001). A similar pattern emerged for D-dimer, a marker for blood clot formation and dissolution, with significant differences noted (P=0.029). Multivariable analysis identified age, BMI, associated organ injury (AOI), American Society of Anesthesiologists score, hemoglobin (HGB), RBC, and D-dimer as candidate predictors. Significant variables included age (OR, 3.04; 95% CI, 1.76-5.26; P<0.001), BMI (OR, 1.97; 95% CI, 1.22-3.18; P=0.006), AOI (OR, 2.05; 95% CI, 1.07-3.95; P=0.031), and HGB (HR, 0.59; 95% CI, 0.39-0.88; P=0.010). The discrimination was 0.787, with a corrected c-index of 0.753. Calibration plots and the Hosmer-Lemeshow test indicated a good fit (P=0.7729). Decision curve analysis revealed a superior net clinical benefit when the predicted probability threshold ranged from 0.05 to 0.95.

CONCLUSIONS

We developed a nomogram predictive model, and it could act as a practical tool in clinical workflows to assist physicians in making favorable medical decisions, which potentially reduces the incidence of DVT in those patients with pelvic and acetabular fractures treated conservatively.

Nomogram for predicting postoperative deep vein thrombosis in patients with spinal fractures caused by high-energy injuries

OBJECTIVE

Deep venous thrombosis (DVT) is a common complication in patients with spinal fractures caused by high-energy injuries. Early identification of patients at high risk of postoperative DVT is essential for the prevention of thrombosis. This study aimed to develop and validate a prediction model based on a nomogram to predict DVT in patients with spinal fractures caused by high-energy injuries.

METHODS

Clinical data were collected from 936 patients admitted to our hospital between January 2016 and December 2021 with spinal fractures caused by high-energy injuries. Multivariate logistic regression analysis was used to identify the risk factors for postoperative DVT and to develop a nomogram. The predictive performance of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve.

RESULTS

The incidence of preoperative DVT was 15.38% (144/936). The postoperative incidence of DVT was 20.5% (192/936). The multivariate analysis revealed that age, operation time, blood transfusion, duration of bed rest, American Spinal Injury Association (ASIA) score and D-dimer were risk factors for postoperative DVT. The area under the ROC curve of the nomogram was 0.835 and the calibration curve showed good calibration.

CONCLUSIONS

The nomogram showed a good ability to predict postoperative DVT in patients with spinal fractures caused by high-energy injuries, which may benefit pre- and postoperative DVT prophylaxis strategy development.

Development and validation of a nomogram for predicting deep venous thrombosis in patients with pelvic and acetabular fractures: a retrospective cohort study : Predictive model for pelvic/acetabular fractures

PURPOSE

To construct a novel nomogram model that can predict DVT and avoid unnecessary examination.

METHODS

Patients admitted to the hospital with pelvis/acetabular fractures were included between July 2014 and July 2018. The potential predictors associated with DVT were analyzed using Univariate and multivariable logistic regression analysis. The predictive nomogram was constructed and internally validated.

RESULTS

230 patients were finally enrolled. There were 149 individuals in the non-DVT group and 81 in the DVT group. Following analysis, we obtained the final nomogram model. The risk factors included age (OR, 1.037; 95% CI, 1.013-1.062; P = 0.002), body mass index (BMI) (OR, 1.253; 95% CI, 1.120-1.403; P < 0.001); instant application of anticoagulant after admission (IAA) (OR, 2.734; 95% CI, 0.847-8.829; P = 0.093), hemoglobin (HGB) (OR, 0.970; 95% CI, 0.954-0.986; P < 0.001), D-Dimer(OR, 1.154; 95% CI, 1.016-1.310; P = 0.027) and fibrinogen (FIB) (OR, 1.286; 95% CI, 1.024-1.616; P = 0.002). The apparent C-statistic was 0.811, and the adjusted C-statistic was 0.777 after internal validations, demonstrating good discrimination. Hosmer and Lemeshow's goodness of fit (GOF) test of the predictive model showed a good calibration for the probability of prediction and observation (χ2 = 3.285, P = 0.915; P > 0.05). The decision curve analysis (DCA) and Clinical impact plot (CIC) demonstrated superior clinical use of the nomogram.

CONCLUSIONS

An easy-to-calculate nomogram model for predicting DVT in patients with pelvic-acetabular fractures were developed. It could help clinicians to reduce DVT and avoid unnecessary examinations.

A nomogram to predict postoperative deep vein thrombosis in patients with femoral fracture: a retrospective study

OBJECTIVE

The implementation of more active anticoagulant prevention and treatment measures has indeed led to a significant reduction in the incidence of perioperative deep vein thrombosis (DVT) among patients with bone trauma. However, it is important to note that despite these efforts, the incidence of DVT still remains relatively high. According to the Caprini score, all patients undergoing major orthopedic surgery were defined as the high-risk group for DVT. Stratifying the risk further within high-risk groups for DVT continues to present challenges. As a result, the commonly used Caprini score during the perioperative period is not applicable to orthopedic patients. We attempt to establish a specialized model to predict postoperative DVT risk in patients with femoral fracture.

METHODS

We collected the clinical data of 513 patients undergoing femoral fracture surgery in our hospital from May 2018 to December 2019. According to the independent risk factors of DVT obtained by univariate and multivariate logistic regression analysis, the corresponding nomogram model was established and verified internally. The discriminative capacity of nomogram was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The calibration curve used to verify model consistency was the fitted line between predicted and actual incidences. The clinical validity of the nomogram model was assessed using decision curve analysis (DCA) which could quantify the net benefit of different risk threshold probabilities. Bootstrap method was applied to the internal validation of the nomogram model. Furthermore, a comparison was made between the Caprini score and the developed nomogram model.

RESULTS

The Caprini scores of subjects ranged from 5 to 17 points. The incidence of DVT was not positively correlated with the Caprini score. The predictors of the nomogram model included 10 risk factors such as age, hypoalbuminemia, multiple trauma, perioperative red blood cell infusion, etc. Compared with the Caprini scale (AUC = 0.571, 95% CI 0.479-0.623), the calibration accuracy and identification ability of nomogram were higher (AUC = 0.865,95% CI 0.780-0.935). The decision curve analysis (DCA) indicated the clinical effectiveness of nomogram was higher than the Caprini score.

CONCLUSIONS

The nomogram was established to effectively predict postoperative DVT in patients with femoral fracture. To further reduce the incidence, more specialized risk assessment models for DVT should take into account the unique risk factors and characteristics associated with specific patient populations.

Risk factors for deep vein thrombosis in patients with pelvic or lower-extremity fractures in the emergency intensive care unit

Introduction

This study aimed to investigate the incidence of deep vein thrombosis (DVT) in patients with pelvic or lower-extremity fractures in the emergency intensive care unit (EICU), explore the independent risk factors for DVT, and investigate the predictive value of the Autar scale for DVT in these patients.

Methods

The clinical data of patients with single fractures of the pelvis, femur, or tibia in the EICU from August 2016 to August 2019 were retrospectively examined. The incidence of DVT was statistically analyzed. Logistic regression was used to analyze the independent risk factors for DVT in these patients. The receiver-operating characteristic (ROC) curve was used to evaluate the predictive value of the Autar scale for the risk of DVT.

Results

A total of 817 patients were enrolled in this study; of these, 142 (17.38%) had DVT. Significant differences were found in the incidence of DVT among the pelvic fractures, femoral fractures, and tibial fractures (P < 0.001). The multivariate logistic regression analysis showed multiple injuries (OR = 2.210, 95% CI: 1.166-4.187, P = 0.015), fracture site (compared with tibia fracture group, femur fracture group OR = 4.839, 95% CI: 2.688-8.711, P < 0.001; pelvic fracture group OR = 2.210, 95% CI: 1.225-3.988, P = 0.008), and Autar score (OR = 1.198, 95% CI: 1.016-1.353, P = 0.004) were independent risk factors for DVT in patients with pelvic or lower-extremity fractures in the EICU. The area under the ROC curve (AUROC) of the Autar score for predicting DVT was 0.606. When the Autar score was set as the cutoff value of 15.5, the sensitivity and specificity for predicting DVT in patients with pelvic or lower-extremity fractures were 45.1% and 70.7%, respectively.

Discussion

Fracture is a high-risk factor for DVT. Patients with a femoral fracture or multiple injuries have a higher risk of DVT. In the case of no contraindications, DVT prevention measures should be taken for patients with pelvic or lower-extremity fractures. Autar scale has a certain predictive value for the occurrence of DVT in patients with pelvic or lower-extremity fractures, but it is not ideal.

Dynamic Changes and Relevant Factors of Perioperative Deep Vein Thrombosis in Patients with Thoracolumbar Fractures Caused by High-Energy Injuries

OBJECTIVE

To investigate the dynamic changes and relevant factors of deep vein thrombosis (DVT) in patients with thoracolumbar fractures caused by high-energy injuries.

METHODS

From January 2016 to June 2021, a total of 655 patients with thoracolumbar fractures who underwent surgical treatment in our hospital were retrospectively analyzed. The patients were examined by preoperative and postoperative ultrasonography, and divided into thrombus growth group, thrombus invariant group, and thrombus regression group according to the preoperative and postoperative ultrasonographic results. Medical record data, including demographic data, surgical data, and laboratory results, were collected and the differences in various factors among the groups were compared.

RESULTS

DVT was found in 99 patients (15.1%, 99/655) before surgery, including 79 cases of distal thrombus, 7 cases of proximal thrombus, and 13 cases of mixed thrombus. The incidence of postoperative DVT increased to 20.6% (134/655), including 96 cases of distal thrombus, 15 cases of proximal thrombus, and 23 cases of mixed thrombus. Among them, 39.7% had thrombus growth, 49.3% had thrombus basically unchanged and 11.0% had thrombolysis. There were significant differences in age, lower extremity muscle strength, time from trauma to surgery, operation time, blood loss, blood transfusion, and post 3-D-dimer among the three groups.

CONCLUSIONS

In patients with thoracolumbar fractures caused by high-energy injuries, the majority of patients with DVT do not change or grow after surgery, and only a few of them have thrombolysis. Younger age, lower extremity motor, and fewer blood transfusion contribute to thrombolysis. Delayed surgical intervention, longer operation time, and higher blood loss can lead to thrombosis growth. Post 3-D-dimer is closely related to the dynamic changes of thrombus.

Hyperglycemia may increase deep vein thrombosis in trauma patients with lower limb fracture

Diabetes mellitus is associated with prothrombotic states and thrombotic events. This study examined the association between preoperative glucose levels and deep vein thrombosis (DVT) in trauma patients undergoing surgery for lower limb fracture. Data from 1,591 patients who underwent fracture surgery between January 2017 and March 2022 at the Affiliated Hospital of Qingdao University were queried from institutional electronic medical records. A total study population of 1,086 patients was identified, comprising 138 patients who experienced DVT and 948 controls. The primary outcome was DVT. Multiple logistic regression analyses were performed and a receiver operating characteristic (ROC) curve was generated. Age, D-dimer level, preoperative RBC count, and preoperative glucose level were independent predictors of DVT. The two highest categories of D-dimer level (≥ 960, < 2,102; ≥ 2,102 ng/ml) increased the odds ratio for DVT by 4.215 times [95% confidence interval (CI) 1.820–9.761] and 7.896 times (95% CI 3.449–18.074), respectively, compared with the lowest reference category (< 490 ng/ml). The area under the curve (AUC) for the preoperative glucose level was 0.605. Hyperglycemia (glucose ≥ 6.1, < 7.0 mmol/l) increased the odds of DVT by 1.889-fold [95% CI (1.085–3.291); p < 0.0001] compared with euglycemia (glucose < 6.1 mmol/l). We therefore observed an association between preoperative hyperglycemia and DVT in patients with lower limb fractures. There are several modalities for controlling hyperglycemia, offering potential targets for future improvement.