Solid Tumor Complicated With Venous Thromboembolism: A 10-Year Retrospective Cross-Sectional Study

Preoperative Systemic Immune-Inflammatory Index as a Predictive Biomarker for Lower Extremity Deep Vein Thrombosis after Breast Cancer Surgery: Stratified Nursing Intervention to Reduce Incidence

BACKGROUND Lower extremity deep vein thrombosis (LEDVT), a common postoperative breast cancer complication, depends on multiple factors, such as systemic inflammation and immune status. We assessed the preoperative systemic immune-inflammatory index (SII) as a LEDVT risk predictor and did stratified nursing intervention based on it. MATERIAL AND METHODS A retrospective analysis was conducted on 552 patients who underwent breast cancer surgery from January 2019 to May 2020. Univariate and multivariate methods were used to screen independent risk factors for postoperative LEDVT, and effects of risk stratification and nursing intervention on the validation cohort were observed. RESULTS A total of 46 patients (8.3%) developed postoperative LEDVT. Compared with the control group, the LEDVT group had older age, higher proportion of menopause, higher preoperative Autar score, SII, and plasma D-dimer level, lower albumin level, and later postoperative pathological stage, with statistically significant differences (P<0.05). Multivariate logistic regression showed SII, plasma D-dimer, and menopause were independent predictors of postoperative LEDVT. ROC curve analysis showed SII had the strongest predictive ability, with an AUC of 0.714. Subsequently, 126 patients in the validation set were stratified according to the preoperative SII score, and intensified nursing intervention was implemented for high-risk patients, resulting in a significant reduction in the incidence of LEDVT (3.3% vs 8.3%, P=0.046). CONCLUSIONS Preoperative SII level can be used as an independent risk predictor of postoperative LEDVT in breast cancer patients. Applying it for risk stratification and implementing intensified nursing intervention for high-risk patients can significantly reduce the incidence of postoperative LEDVT.

Derivation, validation and assessment of a novel nomogram-based risk assessment model for venous thromboembolism in hospitalized patients with lung cancer: A retrospective case control study

Purpose

This study aimed to develop and validate a specific risk-stratification nomogram model for the prediction of venous thromboembolism(VTE) in hospitalized patients with lung cancer using readily obtainable demographic, clinical and therapeutic characteristics, thus guiding the individualized decision-making on thromboprophylaxis on the basis of VTE risk levels.

Methods

We performed a retrospective case–control study among newly diagnosed lung cancer patients hospitalized between January 2016 and December 2021. Included in the cohort were 234 patients who developed PTE and 936 non-VTE patients. The patients were randomly divided into the derivation group (70%, 165 VTE patients and 654 non-VTE patients) and the validation group (30%, 69 VTE patients and 282 non-VTE patients). Cut off values were established using a Youden´s Index. Univariate and multivariate regression analyses were used to determine independent risk factors associated with VTE. Variance Inflation Factor(VIF) was used for collinearity diagnosis of the covariates in the model. The model was validated by the consistency index (C-index), receiver operating characteristic curves(ROC) and the calibration plot with the Hosmer-Lemeshow goodness-of-fit test. The clinical utility of the model was assessed through decision curve analysis(DCA). Further, the comparison of nomogram model with current models(Khorana, Caprini, Padua and COMPASS-CAT) was performed by comparing ROC curves using the DeLong’s test.

Results

The predictive nomogram modle comprised eleven variables: overweight(24-28) defined by body mass index (BMI): [odds ratio (OR): 1.90, 95% confidence interval (CI): 1.19-3.07], adenocarcinoma(OR:3.00, 95% CI: 1.88-4.87), stageIII-IV(OR:2.75, 95%CI: 1.58-4.96), Central venous catheters(CVCs) (OR:4.64, 95%CI: 2.86-7.62), D-dimer levels≥2.06mg/L(OR:5.58, 95%CI:3.54-8.94), PT levels≥11.45sec(OR:2.15, 95% CI:1.32-3.54), Fbg levels≥3.33 g/L(OR:1.76, 95%CI:1.12-2.78), TG levels≥1.37mmol/L (OR:1.88, 95%CI:1.19-2.99), ROS1 rearrangement(OR:2.87, 95%CI:1.74-4.75), chemotherapy history(OR:1.66, 95%CI:1.01-2.70) and radiotherapy history(OR:1.96, 95%CI:1.17-3.29). Collinearity analysis with demonstrated no collinearity among the variables. The resulting model showed good predictive performance in the derivation group (AUC 0.865, 95% CI: 0.832-0.897) and in the validation group(AUC 0.904,95%CI:0.869-0.939). The calibration curve and DCA showed that the risk-stratification nomogram had good consistency and clinical utility. Futher, the area under the ROC curve for the specific VTE risk-stratification nomogram model (0.904; 95% CI:0.869-0.939) was significantly higher than those of the KRS, Caprini, Padua and COMPASS-CAT models(Z=12.087, 11.851, 9.442, 5.340, all P<0.001, respectively).

Conclusion

A high-performance nomogram model incorporated available clinical parameters, genetic and therapeutic factors was established, which can accurately predict the risk of VTE in hospitalized patients with lung cancer and to guide individualized decision-making on thromboprophylaxis. Notably, the novel nomogram model was significantly more effective than the existing well-accepted models in routine clinical practice in stratifying the risk of VTE in those patients. Future community-based prospective studies and studies from multiple clinical centers are required for external validation.

Cancer-associated venous thromboembolism

Cancer-associated thrombosis (including venous thromboembolism (VTE) and arterial events) is highly consequential for patients with cancer and is associated with worsened survival. Despite substantial improvements in cancer treatment, the risk of VTE has increased in recent years; VTE rates additionally depend on the type of cancer (with pancreas, stomach and primary brain tumours having the highest risk) as well as on individual patient's and cancer treatment factors. Multiple cancer-specific mechanisms of VTE have been identified and can be classified as mechanisms in which the tumour expresses proteins that alter host systems, such as levels of platelets and leukocytes, and in which the tumour expresses procoagulant proteins released into the circulation that directly activate the coagulation cascade or platelets, such as tissue factor and podoplanin, respectively. As signs and symptoms of VTE may be non-specific, diagnosis requires clinical assessment, evaluation of pre-test probability, and objective diagnostic testing with ultrasonography or CT. Risk assessment tools have been validated to identify patients at risk of VTE. Primary prevention of VTE (thromboprophylaxis) has long been recommended in the inpatient and post-surgical settings, and is now an option in the outpatient setting for individuals with high-risk cancer. Anticoagulant therapy is the cornerstone of therapy, with low molecular weight heparin or newer options such as direct oral anticoagulants. Personalized treatment incorporating risk of bleeding and patient preferences is essential, especially as a diagnosis of VTE is often considered by patients even more distressing than their cancer diagnosis, and can severely affect the quality of life. Future research should focus on current knowledge gaps including optimizing risk assessment tools, biomarker discovery, next-generation anticoagulant development and implementation science.

Integrated Analysis of Microbiome and Transcriptome Data Reveals the Interplay Between Commensal Bacteria and Fibrin Degradation in Endometrial Cancer

The gut-uterus axis plays a pivotal role in the pathogenesis of endometrial cancer (EC). However, the correlations between the endometrial microbiome and endometrial tumor transcriptome in patients with EC and the impact of the endometrial microbiota on hematological indicators have not been thoroughly clarified. In this prospective study, endometrial tissue samples collected from EC patients (n = 30) and healthy volunteers (n = 10) were subjected to 16S rRNA sequencing of the microbiome. The 30 paired tumor and adjacent nontumor endometrial tissues from the EC group were subjected to RNAseq. We found that Pelomonas and Prevotella were enriched in the EC group with a high tumor burden. By integrating the microbiome and hematological indicators, a correlation was observed between Prevotella and elevated serum D-dimer (DD) and fibrin degradation products (FDPs). Further transcriptome analysis identified 8 robust associations between Prevotella and fibrin degradation-related genes expressed within ECs. Finally, the microbial marker of Prevotella along with DD and FDPs showed a high potential to predict the onset of EC (AUC = 0.86). Our results suggest that the increasing abundance of Prevotella in endometrial tissue combined with high serum DD and FDP contents may be important factors associated with tumor burden. The microbe-associated transcripts of host tumors can partly explain how Prevotella promotes DD and FDPs.