Analysis of high-risk factors for preoperative DVT in elderly patients with simple hip fractures and construction of a nomogram prediction model

Prognostic value of the systemic immune-inflammation index in critically ill elderly patients with hip fracture: evidence from MIMIC (2008-2019)

Background

The systemic immune-inflammation index (SII) showed an extensive link between immunological dysfunction and the activation of systemic inflammation. Several studies have confirmed the application of SII to orthopedic diseases. However, the significance of SII in critically ill elderly individuals with hip fracture who require intensive care unit (ICU) admission is not yet known. This study centered on exploring the relationship between SII and clinical outcomes among critically ill elderly hip fracture individuals.

Methods

The study centered around elderly patients experiencing severe illness following hip fractures and requiring admission to the ICU. These patients from the MIMIC-IV database formed the basis of this study's cohort. We stratified them into quartiles according to their SII levels. The results involved the mortality at 30 days and 1 year post-admission. Then we employ Cox proportional hazards regression analysis as well as restricted cubic splines to explore the association between the SII and clinical results in critically ill elderly patients with hip fracture.

Results

The study encompassed 991 participants, among whom 63.98% identified as females. Notably, the mortality rates attributed to any cause within 30 days and 1 year after hospitalization stood at 19.68 and 33.40%, respectively. The multivariate Cox proportional hazards model disclosed a significant correlation between an elevated SII and all-cause mortality. Following adjustments for confounding variables, individuals with a high SII showed a notable correlation with 30-day mortality [adjusted hazard ratio (HR), 1.065; 95% confidence interval (CI), 1.044-1.087; p < 0.001] and 1-year mortality (adjusted HR, 1.051; 95% CI, 1.029-1.074; p < 0.001). Furthermore, the analysis of restricted cubic splines demonstrated a progressive increase in the risk of all-cause death as the SII value rose.

Conclusion

Among critically ill elderly patients with hip fracture, the SII exhibits a non-linear association that positively correlates with both 30-day and 1-year all-cause mortality rates. The revelation indicates that the SII may play a vital role in identifying patients with hip fractures who face an escalated risk of mortality due to any cause.

Prevalence and risk factors for proximal deep vein thrombosis at admission in patients with traumatic fractures: a multicenter retrospective study

Objective

This study aimed to determine the associated risk factors for proximal deep vein thrombosis (DVT) in patients with lower extremity and pelvic-acetabular fractures.

Methods

The medical records of 4,056 patients with lower extremity and pelvic-acetabular fractures were retrospectively reviewed. The patients were classified into proximal or non-proximal DVT groups. Logistic regression models were used to determine the independent risk variables for proximal DVT. The predictive value of the related risk factors was further analyzed using receiver operating characteristic curves.

Results

The prevalence of proximal DVT was 3.16%. Sex, body mass index (BMI), fracture site, injury mechanism, diabetes, coronary heart disease (CHD), injury-to-admission interval, hematocrit, platelet counts, and D-dimer levels differed significantly between the two groups. BMI ≥ 24.0 kg/m2, femoral shaft fractures, high-energy injury, diabetes, injury-to-admission interval >24 h were independent risk factors for proximal DVT. CHD decreased the risk of proximal DVT. The platelet and D-dimer had high negative predictive value for predicting proximal DVT formation, with cut-off values of 174 × 109/L and 2.18 mg/L, respectively.

Conclusion

BMI ≥ 24.0 kg/m2, femoral shaft fractures, high-energy injury, diabetes, injury-to-admission interval >24 h were independent risk factors for proximal DVT in patients with lower extremity and pelvic-acetabular fractures. Platelet count and D-dimer level were effective indicators for excluding proximal DVT occurrence. CHD decreased the risk of proximal DVT.

Injury-Admission Time is an Independent Risk Factor for Deep Vein Thrombosis in Older Patients with Osteoporotic Hip Fracture

BACKGROUND Deep vein thrombosis is a common pre- and post-operative complication in older patients with osteoporotic hip fractures. Pre-operative thrombus can increase the risk of surgery. This study examined the association between the time from fracture to admission (injury-admission time) and deep vein thrombosis in older patients with osteoporotic hip fractures. MATERIAL AND METHODS Doppler ultrasound screening of deep lower-extremity veins was performed in patients with osteoporotic hip fractures between June 2019 and December 2021. Clinical data, including medical history, injury-admission time, and laboratory tests, were collected retrospectively. RESULTS Of the 439 patients, deep vein thrombosis was found in 139 (31.66%). The injury-admission time was significantly longer in the thrombosis group, which was positively associated with deep vein thrombosis (odds ratio 1.010, 95% confidence interval 1.003-1.017). The area under the curve to predict deep vein thrombosis was 0.619. The best cut-off value, sensitivity, and specificity were 21 h, 46.76%, and 75%, respectively. When the injury-admission period exceeded 21 h, the prevalence of deep vein thrombosis was 45.8% and the thrombosis incidence was significantly higher than in the <21 h group (24.9%). CONCLUSIONS Our results suggest that screening for deep vein thrombosis should be routinely performed for patients with osteoporotic hip fractures, particularly for those with injury-admission time ≥21 h.

Evaluation of preoperative coagulation function changes and deep vein thrombosis incidence in elderly patients with hip fractures

OBJECTIVE

This study involved an analysis of preoperative deep vein thrombosis (DVT) incidence and changes in coagulation function among elderly patients suffering from hip fractures. The objective was to offer guidance on the prevention and management of preoperative DVT in the lower extremities of elderly individuals with hip fractures.

METHODS

A total of 282 elderly individuals with a hip fracture were enrolled and divided into two groups based on the location of the fracture: femoral intertrochanteric fracture (FIF, 161 individuals) and femoral neck fracture (FNF, 121 individuals). The two groups were compared with respect to baseline characteristics, including gender, age, and comorbid chronic diseases. Furthermore, the analysis encompassed the incidence of preoperative DVT in both lower extremities, along with seven coagulation parameters and platelet count before the surgical procedure.

RESULTS

There was no significant difference in baseline information between the two groups. The incidence of preoperative DVT in the FIF group was higher than that in the FNF group, along with a significantly higher percentage of patients exhibiting increased levels of D-dimer and fibrinogen/fibrin degradation products (FDPs).

CONCLUSION

Preoperative hypercoagulability and a greater prevalence of DVT were observed in elderly individuals with FIF compared to individuals with FNF. This indicates that clinicians should pay attention to elderly patients with FIFs, especially those with increased D-dimer and FDP levels.

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.

Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning

This study developed a deep vein thrombosis (DVT) risk prediction model based on multiple machine learning methods for patients with digestive system tumors undergoing surgical treatment. Data of 1048 patients with digestive system tumors admitted to Shanxi Provincial People's Hospital (College of Shanxi Medical University) from January 2020 to January 2023 were retrospectively analyzed, and 845 cases were screened according to the inclusion and exclusion criteria. The patients were divided into a training group (586 patients), and a validation group (259 patients), then feature selection was performed using six models, including Lasso regression, XGBoost, Random Forest, Decision Tree, Support Vector Machine, and Logistics. Predictive models were subsequently constructed from column-line plots, and the predictive validity of the models was assessed using receiver operating characteristic curves, precision-recall curves, and decision-curve analysis. In the model comparison, the XGBoost model showed the largest area under the curve (AUC) on the validation set (P < 0.05), demonstrating excellent predictive performance and generalization ability. We selected the common characteristic factors in the six models to further develop the column line plots to assess the DVT risk. The model performed well in clinical validation and effectively differentiated high-risk and low-risk patients. The differences in BMI, procedure time, and D-dimer were statistically significant between patients in the thrombus group and those in the non-thrombus group (P < 0.05). However, the AUC of the Xgboost model was found to be greater than that of the column chart model by the Delong test (P < 0.05). BMI, procedure time, and D-dimer are critical predictors of DVT risk in patients with digestive system tumors. Our model is an adequate assessment tool for DVT risk, which can help improve the prevention and treatment of DVT.

Analysis of risk factors for postoperative deep vein thrombosis after craniotomy and nomogram model construction

BACKGROUND

Deep vein thrombosis (DVT) of the lower extremity is one of the most common postoperative complications, especially after craniocerebral surgery. DVT may lead to pulmonary embolism, which has a devastating impact on patient prognosis. This study aimed to investigate the incidence and risk factors of DVT in the lower limbs following craniocerebral surgery.

AIM

To identify independent risk factors for the development of postoperative DVT and to develop an effective risk prediction model.

METHODS

The demographic and clinical data of 283 patients who underwent craniocerebral surgery between December 2021 and December 2022 were retrospectively analyzed. The independent risk factors for lower extremity DVT were identified by univariate and multivariate analyses. A nomogram was created to predict the likelihood of lower extremity DVT in patients who had undergone craniocerebral surgery. The efficacy of the prediction model was determined by receiver operating characteristic curve using the probability of lower extremity DVT for each sample.

RESULTS

Among all patients included in the analysis, 47.7% developed lower extremity DVT following craniocerebral surgery. The risk of postoperative DVT was higher in those with a longer operative time, and patients with intraoperative intermittent pneumatic compression were less likely to develop postoperative DVT.

CONCLUSION

The incidence of lower extremity DVT following craniocerebral surgery is significant, highlighting the importance of identifying independent risk factors. Interventions such as the use of intermittent pneumatic compression during surgery may prevent the formation of postoperative DVT.

Development and validation of a nomogram for predicting the risk of immediate postoperative deep vein thrombosis after open wedge high tibial osteotomy

PURPOSE

This study aimed to identify independent risk factors for immediate postoperative deep vein thrombosis (DVT) in patients with open wedge high tibial osteotomy (OWHTO) and to develop and validate a predictive nomogram.

METHODS

Patients who underwent OWHTO for knee osteoarthritis (KOA) from June 2017 to December 2021 were retrospectively analyzed. Baseline data and laboratory test results were collected, and the occurrence of DVT in the immediate postoperative period was regarded as the study outcome event. Multivariable logistic regression identified independent risk factors associated with a higher incidence of immediate postoperative DVT. The predictive nomogram was constructed based on the analysis results. The stability of the model was further assessed in this study using patients from January to September 2022 as an external validation set.

RESULTS

741 patients were enrolled in the study, of which 547 were used in the training cohort and the other 194 for the validation cohort. Multivariate analysis revealed a higher Kellgren-Lawrence (K-L) grade (III vs. I-II OR 3.09, 95% CI 0.93-10.23. IV vs. I-II OR 5.23, 95% CI 1.27-21.48.), platelet to hemoglobin ratio (PHR) > 2.25 (OR 6.10, 95% CI 2.43-15.33), Low levels of albumin (ALB) (OR 0.79, 95% CI 0.70-0.90), LDL-C > 3.40 (OR 3.06, 95% CI 1.22-7.65), D-dimer > 1.26 (OR 2.83, 95% CI 1.16-6.87) and BMI ≥ 28 (OR 2.57, 95% CI 1.02-6.50) were the independent risk factors of immediate postoperative DVT. The concordance index (C-index) and Brier score of the nomogram were 0.832 and 0.036 in the training set, and the corrected values after internal validation were 0.795 and 0.038, respectively. The receiver-operating characteristic (ROC) curve, the calibration curve, the Hosmer-Lemeshow test, and the decision curve analysis (DCA) performed well in both the training and validation cohorts.

CONCLUSION

This study developed a personalized predictive nomogram with six predictors, which allows surgeons to stratify risk and recommended immediate ultrasound scans for patients with any of these factors.

LEVEL OF EVIDENCE

III.

A nomogram model based on the combination of the systemic immune-inflammation index, body mass index, and neutrophil/lymphocyte ratio to predict the risk of preoperative deep venous thrombosis in elderly patients with intertrochanteric femoral fracture: a retrospective cohort study

OBJECTIVES

Deep vein thrombosis (DVT) has been considered as a frequent and serious consequence of intertrochanteric femoral fractures in the elderly. Several negative repercussions of DVT can be considerably mitigated by its timely recognition and treatment. The current work was aimed at exploring the factors independently predicting DVT among cases suffering from intertrochanteric femoral fractures and validate their predictive usefulness in diagnosing DVT.

METHODS

Between April 2017 and July 2022, clinical information from 209 cases showing preoperative DVT for femoral intertrochanteric fractures were retrospectively evaluated. In patients with femoral intertrochanteric fractures, logistic regression analysis with a backward stepwise method was adopted for detecting independent predictors for the diagnosis of preoperative DVT. Using multivariate logistic regression, a nomogram prediction model was developed and verified with the testing group.

RESULTS

According to multivariate logistic regression model, body mass index (BMI) (OR 0.79, 95% CI 0.63-0.99, P = 0.042), neutrophil/lymphocyte ratio (NLR) (OR 7.29, 95% CI 1.53, 34.64, P = 0.0012), and systemic immune-inflammation index (SII) (OR 6.61, 95% CI 2.35, 18.59, P = 0.001) were independent predictors for DVT before surgery among cases developing intertrochanteric femoral fracture. AUC values were 0.862 and 0.767 for training and testing groups, separately, while their mean errors in the calibration curve were 0.027 and 0.038 separately. Decision curve analysis (DCA) curve revealed a high value of clinical application for both groups.

CONCLUSION

Upon admission, BMI, NLR, and SII are independent predictors of DVT before surgery among cases developing intertrochanteric femoral fractures. Additionally, the nomogram based on the BMI, NLR, and SII can assist clinicians in determining if preventive and symptomatic therapies are required to improve DVT prognosis and reduce its associated mortality.

Association between systemic immune-inflammation index and risk of lower extremity deep venous thrombosis in hospitalized patients: a 10-year retrospective analysis

Background

The systemic immune-inflammation index (SII), as a novel inflammatory biomarker, has recently attracted attention in cardiovascular disease research. However, the relationship between SII and risk of lower extremity deep venous thrombosis (LEDVT) remains unclear to date. Thus, this study aimed to explore the association in a large sample over a 10-year period (2012-2022).

Methods

All hospitalized patients undergoing lower extremity compression ultrasonography (CUS) examination were consecutively screened by searching our hospital information system database. The receiver operating characteristic (ROC) curve analysis was used to identify the optimal cut-off value for high and low SII group. Multivariate logistic regression analyses were performed to investigate the relationship between SII and LEDVT risk. Propensity score matching (PSM), subgroup and sensitivity analyses were also conducted. Moreover, restricted cubic spline (RCS) regression and two-piecewise linear regression models were used to assess the dose-response relationship between natural log transformed SII [ln(SII)] and risk of LEDVT.

Results

A total of 16,725 consecutive hospitalized patients were included, and 1,962 LEDVT events occurred. After adjusting for confounding factors, patients in the high SII group (≥ 574.2 × 10⁹/L) showed a 1.740-fold risk of LEDVT (95% CI: 1.546-1.959, P < 0.001), and elevated ln(SII) was associated with a 36.1% increased risk of LEDVT (95% CI: 1.278-1.449, P < 0.001). PSM, subgroup and sensitivity analyses confirmed the robustness of the association. A non-linear relationship was observed (P _non-linear< 0.001), with a threshold value of 5.6 × 10⁹/L for all LEDVT events. Above the threshold, each unit increase in ln(SII) had a 1.369-fold higher risk of LEDVT (95% CI: 1.271-1.475, P < 0.001). The association also existed in both distal and proximal LEDVT.

Conclusion

Elevated SII is significantly associated with an increased risk of LEDVT in hospitalized patients. Additionally, the association is non-linear and exhibit a threshold effect.

Haemostasis and Inflammatory Parameters as Potential Diagnostic Biomarkers for VTE in Trauma-Immobilized Patients

Venous thromboembolism (VTE), which encompasses deep venous thrombosis (DVT) and pulmonary embolism (PE), is a major public health concern due to its high incidences of morbidity and mortality. Patients who have experienced trauma with prolonged immobilization are at an increased risk of developing VTE. Plasma D-dimer levels have been known to be elevated in trauma patients, and they were closely correlated with the number of fractures. In other words, plasma D-dimer levels cannot be used as the only indicator of VTE in trauma cases. Given the limitations, further study is needed to explore other potential biomarkers for diagnosing VTE. To date, various established and novel VTE biomarkers have been studied in terms of their potential for predicting VTE, diagnostic performance, and improving clinical therapy for VTE. Therefore, this review aims to provide information regarding classic and essential haemostasis (including prothrombin time (PT), activated partial thromboplastin time (aPTT), D-dimer, fibrinogen, thrombin generation, protein C, protein S, antithrombin, tissue factor pathway inhibitor, and platelet count) and inflammatory biomarkers (C-reactive protein, erythrocyte sedimentation rate, and soluble P-selectin) as potential diagnostic biomarkers that can predict the risk of VTE development among trauma patients with prolonged immobilization. Thus, further advancement in risk stratification using these biomarkers would allow for a better diagnosis of patients with VTE, especially in areas with limited resources.

Analysis of the Risk Factors for Elevated D-Dimer Level After Breast Cancer Surgery: A Multicenter Study Based on Nursing Follow-Up Data

D-dimer level is often used to assess the severity of trauma as well as the risk of thrombosis. This study investigated the risk factors for high postoperative D-dimer level. This study included a total of 2706 patients undergoing breast cancer surgery to examine the associations between various clinicopathological factors and variation in D-dimer levels. After adjusting for other factors, T stage, neoadjuvant chemotherapy, blood loss, surgery type, diabetes, and elevated leukocyte and neutrophil counts were found to be significant risk factors for D-dimer variation. This study identified several factors associated with elevated D-dimer levels and consequent thrombosis after breast cancer surgery, which may aid in the development of more precise preventive measures and interventions as well as serve as a reference for future research.