Immune activation and inflammatory biomarkers as predictors of venous thromboembolism in lymphoma patients

Inflammatory Biomarkers as Predictors of Symptomatic Venous Thromboembolism in Hospitalized Patients with AECOPD: A Multicenter Cohort Study

AIM

Venous thromboembolism (VTE) risk significantly increases in patients with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD), which is characterized by an enhanced inflammatory response. This study aimed to evaluate the predictive value of inflammatory biomarkers for VTE in AECOPD.

METHODS

A prospective, multicenter study was conducted to include patients hospitalized for AECOPD. Inflammatory biomarkers on admission were compared between the patients who developed VTE during hospitalization and the patients without VTE. A logistic regression analysis was used to identify inflammatory biomarkers with an independently predictive value.

RESULTS

Among the 13,531 AECOPD inpatients, 405 (2.99%) developed VTE during hospitalization. Patients who developed VTE had higher levels of inflammatory biomarkers, including the white blood cell count, neutrophil percentage, systemic immune/inflammatory index, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio, C-reactive protein (CRP), procalcitonin (PCT), and lactate dehydrogenase (LDH), and lower lymphocyte and eosinophil ratios (ESOR), platelet, and albumin (p all ＜0.05). NLR, LDH, CRP, PCT, and ESOR were identified as independent predictors of VTE (odds ratios (ORs) were 2.22, 1.95, 1.64, 1.59, and 1.37, respectively). The incidence of VTE increased with increasing NLR, LDH, CRP, and PCT quartiles, and a decreasing ESOR quartile. Among them, NLR and LDH had predictive capabilities for VTE that were comparable to the widely used Padua and IMPROVE scores.

CONCLUSION

Easily available inflammatory parameters, such as NLR and LDH, can identify AECOPD patients at increased risk for VTE who may therefore be candidates for thromboprophylaxis.

Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP

OBJECTIVE

To investigate the predictive value of machine learning-based PET/CT radiomics and clinical risk factors in predicting interim efficacy in patients with follicular lymphoma (FL).

METHODS

This study retrospectively analyzed data from 97 patients with FL diagnosed via histopathological examination between July 2012 and November 2023. Lesion segmentation was performed using LIFEx software, and radiomics features were extracted through the uAI Research Portal (uRP) platform, including first-order features, shape features, and texture features. Fourteen filters were applied to the raw images to extract higher-order features from the derived images. Univariate analysis was employed to identify clinical risk factors, and correlation coefficients, MRMR, and LASSO algorithms were used for dimensionality reduction and selection of radiomics features. Finally, a logistic regression machine learning model was developed to predict the interim efficacy of FL using a five-fold cross-validation strategy. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve, accuracy, and the Delong test to compare AUC differences.

RESULT

Among the 97 patients, 42 (43.30%) achieved complete response (CR) for interim efficacy, while 55 (56.70%) had non-complete response (non-CR). A total of 2264 radiomics features were extracted from the images. Seven clinical risk factors and ten radiomics features associated with interim efficacy were selected to construct the clinical, radiomics, and radiomics-clinical combined models. Among the three logistic regression machine learning models developed, the radiomics-clinical combined model demonstrated the best performance, achieving a mean AUC of 0.849 (95% CI, 0.676-1.000) and an accuracy of 0.795, outperforming the other two models.

CONCLUSION

Our preliminary results demonstrate that a radiomics-clinical combined model, based on baseline [18F]FDG PET/CT radiomics features and clinical risk factors, may contribute to predicting interim efficacy in FL patients.

From Cell Interactions to Bedside Practice: Complete Blood Count-Derived Biomarkers with Diagnostic and Prognostic Potential in Venous Thromboembolism

Venous thromboembolism (VTE), encompassing deep vein thrombosis and pulmonary embolism, is a significant burden on health and economic systems worldwide. Improved VTE management calls for the integration of biomarkers into diagnostic algorithms and scoring systems for risk assessment, possible complications, and mortality. This literature review discusses novel biomarkers with potential diagnostic and prognostic value in personalized VTE management. The pathophysiology of thrombosis starts with cell interactions in the vascular environment and continues with more complex, recently discussed processes such as immunothrombosis and thromboinflammation. Their clinical applicability is in the use of complete blood count (CBC)-derived immuno-inflammatory indices as attractive, readily available biomarkers that reflect pro-thrombotic states. Indices such as the neutrophil-to-lymphocyte ratio (NLR = neutrophil count divided by lymphocyte count), platelet-to-lymphocyte ratio (PLR = platelet count divided by lymphocyte count), and systemic immune-inflammation index (SII = NLR multiplied by platelet count) have demonstrated predictive value for thromboembolic events. Nevertheless, confounding data regarding cutoffs that may be implemented in clinical practice limit their applicability. This literature review aims to investigate neutrophil and platelet interactions as key drivers of immunothrombosis and thromboinflammation while summarizing the relevant research on the corresponding CBC-derived biomarkers, as well as their potential utility in day-to-day clinical practice.

Development and validation of a nomogram for predicting venous thromboembolism risk in post-surgery patients with cervical cancer

OBJECTIVE

Postoperative venous thromboembolism (VTE) is a potentially life-threatening complication. This study aimed to develop a predictive model to identify independent risk factors and estimate the likelihood of VTE in patients undergoing surgery for cervical cancer.

METHODS

We conducted a retrospective cohort study involving 1,174 patients who underwent surgery for cervical carcinoma between 2019 and 2022. The cohort was randomly divided into training and validation sets at 7:3. Univariate and multivariate logistic regression analyses were used to determine the independent factors associated with VTE. The results of the multivariate logistic regression were used to construct a nomogram. The nomogram's performance was assessed via the concordance index (C-index) and calibration curve. Additionally, its clinical utility was assessed through decision curve analysis (DCA).

RESULTS

The predictive nomogram model included factors such as age, pathology type, FIGO stage, history of chemotherapy, the neutrophil-lymphocyte ratio (NLR), fibrinogen degradation products (FDP), and D-dimer levels. The model demonstrated robust discriminative power, achieving a C-index of 0.854 (95% CI: 0.799-0.909) in the training cohort and 0.757 (95% CI: 0.657-0.857) in the validation cohort. Furthermore, the nomogram showed excellent calibration and clinical utility, as evidenced by the calibration curve and decision curve analysis (DCA) results.

CONCLUSIONS

We developed a high-performance nomogram that accurately predicts the risk of VTE in cervical cancer patients undergoing surgery, providing valuable guidance for thromboprophylaxis decision-making.

Circulating Blood Biomarkers and Risk of Venous Thromboembolism in Cancer Patients: A Systematic Review and Meta-Analysis

BACKGROUND

 Cancer patients have an increased risk of venous thromboembolism (VTE). Currently, the availability of highly discriminatory prediction models for VTE in cancer patients is limited. The implementation of biomarkers in prediction models might lead to refined VTE risk prediction. In this systematic review and meta-analysis, we aimed to evaluate candidate biomarkers and their association with cancer-associated VTE.

METHODS

 We searched Medline, EMBASE, and Cochrane Central for studies that evaluated biomarkers in adult cancer patients from inception to September 2022. We included studies reporting on VTE after a cancer diagnosis with biomarker measurements performed at a defined time point. Median/mean differences (for continuous measures) and odds ratios (for dichotomous measures) with 95% confidence intervals were estimated and pooled using random-effects models.

RESULTS

 We included 113 studies in the systematic review. Of these, 50 studies were included in the meta-analysis. We identified two biomarkers at cancer diagnosis (factor VIII and time to peak thrombin), three biomarkers pre-chemotherapy (D-dimer, fibrinogen, and mean platelet volume), and one biomarker preoperatively (platelet count) that had significant median or mean differences. Additionally, we found that hemoglobin <100 g/L and white blood count >11 × 109/L were significantly associated with future VTE risk only when measured at cancer diagnosis. Pre-chemotherapy neutrophil-to-lymphocyte ratio ≥3 and preoperative platelet count ≥400 × 109/L were also found to be associated with future VTE risk.

CONCLUSION

 In conclusion, this study identified nine candidate blood biomarkers that may help in optimizing VTE prediction in cancer patients that should be further explored in future studies.

Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio as novel prognostic biomarkers in BCR-ABL negative myeloproliferative neoplasms

Higher neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been associated with increased risk of thrombosis, cardiovascular mortality, but their role in myeloproliferative neoplasms (MPN) remains unclear. We analyzed NLR and PLR as prognostic markers for thrombosis and overall survival (OS) in the study that included 461 consecutive MPN patients who were diagnosed from 2018 to 2022 at University center. Twenty age-matched patients without hematological disorder were used as controls. NLR and PLR were significantly increased in whole MPN group compared to controls. NLR was highest in PV > PMF > ET (p < 0.001) while PLR was highest in ET > PMF > PV (p < 0.001). Thrombosis occurrence during follow-up correlated with NLR, NLR ≥ 4.5, presence of ≥ 2 CV factors and previous thrombosis. Arterial thrombosis was associated with previous thrombosis, NLR and NLR ≥ 4.5. Similarly in venous thrombosis previous thrombosis was risk factor, together with NLR, NLR ≥ 4.5, PLR, but also secondary malignancy and female gender. In multivariate Cox model, most important factors for thrombosis development during follow-up were previous thrombosis, NLR ≥ 4.5 and PLR ≥ 500; for arterial thrombosis, NLR ≥ 4.5 and previous thrombosis; for venous thrombosis PLR ≥ 500 and previous thrombosis. Patients with pre-PMF had significantly higher NLR than ET patients. In multivariate Cox regression model, most important factors associated with survival were NLR ≥ 4.5 and PLR ≥ 500. This study highlights strong prognostic correlation of NLR ≥ 4.5 and PLR ≥ 500 with development of thrombosis and OS in MPN. Besides previous thrombosis, most important factor associated with development of arterial thrombosis is NLR ≥ 4.5 and for venous PLR ≥ 500. Our results revealed that NLR ≥ 4.5 could be used as additional marker to distinguish ET from prePMF.

Evaluation of the incidence, predictors, risk assessment scores and outcomes of thromboembolism in a cohort of Egyptian NHL patients - Real World Experience

Non-Hodgkin's Lymphoma (NHL) is the most common subtype of lymphoma. The incidence of venous thromboembolism (VTE) in aggressive NHL was estimated recently to be 11%. Several risk assessment scores and factors are available to help identify cancer patients at risk for developing VTE. Patients with a pathologically confirmed diagnosis of NHL were identified at the Oncology Center of Mansoura University. The study included 777 patients: 719 with DLBCL-NOS, 26 with Anaplastic-B-cell, and 32 with T-cell-rich-NHL. Data were retrospectively collected from electronic medical records, including clinical, radiological, and laboratory information related to VTE and NHL. The median age at NHL diagnosis was 53 years, (range: 18-98). There was a male predominance, 51.4% of the cases. At initial lymphoma diagnosis, VTE was identified in 46 (5.9%) patients, and 61 (7.9%) patients experienced VTE while undergoing chemotherapy. According to logistic regression analysis, a PS (performance status) ≥ 2, bulky lesions, and mediastinal masses were significant predictors of VTE at presentation, with P-values of 0.022, 0.002, and < 0.001, respectively. Meanwhile, NHL patients who developed VTE during chemotherapy had significantly poorer PS, higher absolute neutrophilic counts (ANC), neutrophil/lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lactate dehydrogenase (LDH) levels than lymphoma patients without VTE, with P-values of 0.003,  0.034, 0.049, 0.01 and 0.007, respectively, as determined by multivariate analysis. The ROC curve identified the cut-off values of 4.875 × 109/L for ANC, 2.985 for NLR, 144.85 for PLR, and 417.5 U/L for LDH as potential markers for predicting VTE in NHL patients. Patients with a PS ≥ 2 and values exceeding these cut-offs for ANC, NLR, and PLR experienced significantly higher incidences of VTE than other groups, with P-values of 0.003, < 0.001, < 0.001, and < 0.001, respectively. At the end of the follow-up, the overall survival was significantly shortened by VTE occurring during chemotherapy, hypoalbuminemia,  intermediate-high and high international prognostic index (IPI) scores (intermediate-high and high), responses other than CR and relapse, all with P-values < 0.05. ECOG PS and Inflammatory markers such as NLR, PLR, and neutrophilic count could serve as predictors of the development of thrombotic events in patients with NHL-DLBCL. Additionally, the occurrence of VTE during chemotherapy is an independent poor prognostic marker for overall survival (OS).

Systemic immune-inflammation index as a potential biomarker for predicting acute pulmonary embolism: A systematic review

BACKGROUND

Acute pulmonary embolism (APE) is a life-threatening condition with a high mortality rate. The pathophysiology involves various complex processes. The systemic immune-inflammatory index (SII) is a well-known biomarker that reflects the intricate balance between pro-inflammatory and anti-inflammatory immune components. In this systematic review, we aim to determine the significance of SII as a potential biomarker for APE.

METHOD

We utilized PubMed, ProQuest, EBSCOHost, and Google Scholar to search for articles. We assessed bias risk using the Newcastle Ottawa Scale (NOS). The outcomes we examined included in-hospital and long-term mortality, the severity of APE, and the sensitivity and specificity of the SII in predicting APE.

RESULTS

Four studies, involving 2,038 patients, were included for analysis. These studies discuss the use of SII in predicting APE severity, APE mortality, high-risk APE, and the occurrence of APE. SII demonstrates significant results in predicting each of these variables. Furthermore, each study establishes different SII cut-off values. Specifically, a cut-off of 1161 predicts massive APE events with a sensitivity of 91% and a specificity of 90%. A cut-off of >1235.35 differentiates high-risk APE with a sensitivity of 87.32% and a specificity of 68.85%. A cut-off of >1111x109 predicts overall mortality with a sensitivity of 72% and a specificity of 51%. Finally, a cut-off at 1839.91 predicts APE events with a sensitivity of 75.8% and a specificity of 61.9%.

CONCLUSION

The SII can be employed as a potential new biomarker to predict outcomes in APE patients, particularly the occurrence, severity, and mortality of APE.

Comprehensive evaluation of genetic and acquired thrombophilia markers for an individualized prediction of clinical thrombosis in patients with lymphoma and multiple myeloma

Patients diagnosed with lymphoma or multiple myeloma are at elevated risk of venous thromboembolism (VTE). Optimum risk stratification and effective thromboprophylaxis can only be achieved through the development of a multiple-specific risk score that successfully captures all aspects of the heterogeneous prothrombotic environment existing in these patients. Our aim was to identify risk factors for thrombosis and suggest an improved tool combining clinical data, thrombo-inflammatory biomarkers and genetic (Thrombo inCode® test) variables for predicting thrombotic risk in patients with lymphoma and multiple myeloma. A prospective longitudinal study was conducted on newly-diagnosed lymphoma and multiple myeloma patients who presented at our institution between February 2020 and January 2021. The study included 47 patients with lymphoma and 16 patients with multiple myeloma. We performed a follow-up of 1 year or until September 2021. The incidence of venous thrombosis and associated risk factors were analysed, including the genetic Thrombo inCode® test. Khorana and ThroLy scores for lymphoma patients and IMPEDE VTE score for myeloma patients were calculated. At a median follow-up of 9.1 months, VTE incidence was 9.5% (6/63), with 4 and 2 patients with lymphoma and myeloma who developed the events, respectively. Univariate analysis showed that the incidence of thrombosis was significantly higher in patients with ECOG ≥ 2 and prior immobility. Median factor VIII levels were significantly higher in patients with thrombosis (with increased values in all of them). Moreover, there was a trend in genetic variant rs5985 (factor XIII) as a protective factor, and a trend to higher thrombotic risk in patients with factor V Leiden, rs2232698 variant (serpinA10), low total protein S activity, elevated D-dimer, aggressive lymphoma and treatment with dexamethasone. The results of our study demonstrate promise for the potential use of widely accessible markers to increase precision in risk prediction for VTE in patients with lymphoma and multiple myeloma, particularly ECOG ≥ 2, immobility and higher factor VIII levels, as well as lymphoma aggressiveness, treatment with dexamethasone and the haemostatic biomarkers D-dimer and total protein S activity. Additionally, genetic variants factor V Leiden, serpinA10 rs2232698 and factor XIII-A Val34Leu warrant further investigation for use in the research setting.

Cellular Components Contributing to the Development of Venous Thrombosis in Patients with Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive type of cancer and has a poor prognosis. Patients with PDAC are at high risk of developing thromboembolic events, which is a leading cause of morbidity and mortality following cancer progression. Plasma-derived coagulation is the most studied process in cancer-associated thrombosis. Other blood components, such as platelets, red blood cells, and white blood cells, have been gaining less attention. This narrative review addresses the literature on the role of cellular components in the development of venous thromboembolism (VTE) in patients with PDAC. Blood cells seem to play an important role in the development of VTE. Altered blood cell counts, i.e., leukocytosis, thrombocytosis, and anemia, have been found to associate with VTE risk. Tumor-related activation of leukocytes leads to the release of tissue factor-expressing microvesicles and the formation of neutrophil extracellular traps, initiating coagulation and forming a scaffold for thrombi. Tissue factor-expressing microvesicles are also thought to be released by PDAC cells. PDAC cells have been shown to stimulate platelet activation and aggregation, proposedly via the secretion of podoplanin and mucins. Hypofibrinolysis, partially explained by increased plasminogen activator inhibitor-1 activity, is observed in PDAC. In short, PDAC-associated hypercoagulability is a complex and multifactorial process. A better understanding of cellular contributions to hypercoagulability might lead to the improvement of diagnostic tests to identify PDAC patients at highest risk of VTE.

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.

The Diagnostic Value of the Systemic Immune-Inflammation Index for Venous Thromboembolism in Lung Cancer Patients: A Retrospective Study

Background

Venous thromboembolism (VTE) is considered a common complication in lung cancer patients. Despite its widespread use, the Khorana score performed moderately in predicting VTE risk. This study aimed to determine the diagnostic utility of the Systemic Immunoinflammatory Index (SII) and to create a novel nomogram for predicting VTE in patients with pulmonary carcinoma.

Materials and Methods

The data, like clinical features and laboratory indicators, of inpatients diagnosed with lung cancer from March 2019 to March 2020 were collected and analyzed. Univariate and multivariate logistic analyses were performed to confirm the risk factors and then construct a nomogram model. The calibration curve and clinical decision curve analysis (DCA) were used to assess the model's fitting performance. The receiver-operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the diagnostic value of SII and the nomogram.

Results

This study enrolled 369 lung patients with a VTE morbidity rate of 23.33%. The patients with VTE had higher SII levels than the non-VTE group (1441.47 ± 146.28 vs. 626.76 ± 26.04, P < 0.001). SII is the stronger correlator for VTE among inflammatory markers, of which the optimal cut-off value was 851.51. Univariate and multivariate analysis revealed that the age, metastasis, antitumor treatment, hemoglobin<100 g/L, SII>851.51 × 10⁹/L, and D-dimer>2 folds were independent risk factors for lung cancer-related VTE, and a new prediction nomogram model was constructed based on them. ROC curve analysis showed the AUC of the new model and Khorana score were 0.708 (0.643-0.772) and 0.600 (0.531-0.699).

Conclusion

The SII was a simple and valuable biomarker for VTE, and the new nomogram model based on it can accurately forecast the occurrence of VTE. They can be utilized in clinical practice to identify those at high risk of VTE in lung cancer patients.

Cellular Indices and Outcome in Patients with Acute Venous Thromboembolism

Background

Cellular indices provide integrative information about systemic inflammation status which is readily available from routine laboratory parameters. This study aimed to evaluate the prognostic role of three cellular indices in patients with venous thromboembolism (VTE).

Methods

The RIETE registry database was used to determine the association between the baseline neutrophil-to-lymphocyte-ratio (NLR), platelet-to-lymphocyte-ratio (PLR) and systemic-immune-inflammation-index (SII) for 90-day adverse outcomes in patients with acute VTE.

Results

From January 2020 to April 2021, 4487 patients with acute VTE were recruited in the RIETE registry. Of these, 2683 presented with symptomatic pulmonary embolism (PE); 283 with incidental PE; 1129 with lower-limb deep vein thrombosis (DVT); 175 with upper-limb DVT; 69 with splanchnic vein thrombosis; 142 with superficial vein thrombosis and 20 with retinal vein thrombosis. Mean values were: NLR 5.9 ± 7.1, PLR 190 ± 158 and SII 1459 ± 2028. During the first 90-days, 38 patients (0.8%) developed recurrent DVT, 45 (1.0%) had recurrent PE, 152 (3.4%) suffered major bleeding, and 484 (11%) died. On multivariable analysis, patients with NLR >4.41 were at an increased risk for major bleeding and patients with NLR >4.96 were at the risk of death, while those with SII >1134.5 were at increased risk for death.

Conclusions

This study reports the results of a large cohort to date which evaluate the prognostic value of three cellular indices simultaneously in patients with acute VTE. Results support that none of the three baseline cellular indices were sufficient for prediction of VTE recurrences in acute VTE patients. The patients with higher baseline NLR values were at an increased risk of major bleeding or death, those with high SII values were only at an increased risk for mortality.