Derivation and validation of a nomogram model for pulmonary thromboembolism in patients undergoing lung cancer surgery

Li, Yuping; Shen, Lei; Ding, Junrong; Xie, Dong; Yang, Jian; Zhao, Yanfeng; Carretta, Angelo; Petersen, René Horsleben; Gilbert, Sébastien; Hida, Yasuhiro; Bölükbas, Servet; Fernando, Hiran C.; Jiang, Gening; Zhu, Yuming

doi:10.21037/tlcr-21-109

Cited by 10 publications

(11 citation statements)

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“…According to the results of the logistic regression analyses, we established a simplified eleven-variables nomogram model, which contains four clinical variables(BMI, histology, clinical stage and CVC history), four biomarkers(D-dimer, PT, Fbg and TG), oncogenic abnormalities(ROS-1 rearrangement) and history of previous radiotherapy and chemotherapy treatment. Many of the risk factors of cancer-associated VTE identified in this analysis, particularly in lung cancer, were mostly consistent with those of the previous literatures ( 18 – 21 ). Among the numerous risk factors, it is widely accepted that higher BMI were patient-related risk factors related to VTE.…”

Section: Discussionsupporting

confidence: 89%

“…Among the numerous risk factors, it is widely accepted that higher BMI were patient-related risk factors related to VTE. Several studies have revealed that being obesity or overweight was associated with higher risk of VTE ( 8 , 18 , 32 ), while being underweight was associated with a lower risk of VTE ( 33 ). This study confirmed the previous data with a calculated OR of 1.90(95%CI: 1.19-3.07) for overweight.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, comprehensive treatments such as surgery, chemotherapy, radiotherapy and more recently immunotherapy have been recognized as additional risk factors for VTE in patients with lung cancer ( 15 – 17 ). In particular, the current analysis demonstrates that nomogram has good risk-prediction ability for VTE in postoperative lung cancer patients ( 18 ). However, studies on the use of a nomogram model for predicting the risk of CAT are limited, especially for patients with LC receiving first-line systemic therapy ( 19 – 21 ).…”

Section: Introductionmentioning

confidence: 86%

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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

Tian

Niu

et al. 2022

Front. Oncol.

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PurposeThis 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.MethodsWe 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.ResultsThe 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).ConclusionA 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.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

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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

Tian

Niu

et al. 2022

Front. Oncol.

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“…The nomogram is an intuitive predictive tool that enables precise individualized assessment by adding independent correlates and potential biomarkers to make the predictive model more targeted. Nomogram has now been applied to the study of various malignancies and has been shown to be a reliable tool for predicting cancer prognosis, cancer-associated thrombosis (CAT) [12][13][14].…”

Section: Venous Thromboembolism (Vte) Including Deep Vein Thrombosis ...mentioning

confidence: 99%

Derivation and validation of a nomogram model for venous thromboembolism in patients undergoing borderline resectable pancreatic cancer surgery

Cui

Chen

Lyu

et al. 2023

Preprint

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Background: Venous thromboembolism (VTE) is a common postoperative complication in patients with borderline resectable pancreatic cancer (BRPC), which seriously affects the quality of life and prognosis. We aimed to establish new nomogram to predict the VTE risk after BRPC surgery. Method: A total of 301 consecutive adult patients who underwent BRPC surgery between Jan. 2011 and Dec. 2021 in our institution were retrospectively reviewed. Included in the cohort were 67 patients who developed VTE and 79 non-VTE patients. The patients were randomly divided into a training set and a validation set at a ratio of 7:3. A nomogram model was developed based on the results of univariate and multivariate logistic regression analysis in the training group. The predictive power of the nomogram was evaluated by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results: The incidence of VTE in the present study was 45.9%. Age, albumin, operation time, tumor diameter and preoperative D-dimer were independent related factors. The areas under the ROC curve of the nomogram were 0.843 (95% CI: 0.782-0.904) and 0.893 (95% CI: 0.834-0.953) in the training and validation groups, respectively. For clinical usefulness, the model offered a net benefit in decision curve analysis, suggesting the new nomogram predict VTE events accurately enough. Conclusion: We first established a high-performance nomogram of VTE risk in patients undergoing BRPC surgery, which helps clinicians to screen out people at high risk of VTE and perform prophylactic anticoagulation.

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“…Nomograms for VTE risk assessment have been studied in various cancers, including breast cancer [ 17 ], ovarian cancer [ 18 , 19 ], lymphoma [ 20 ], and spinal metastasis tumors [ 21 ]. Recently, two studies [ 22 , 23 ] developed a nomogram model to predict the risk probability of postoperative VTE in patients with early-stage lung cancer. However, in the era of precision medicine, there is still a lack of a nomogram model predicting VTE risk for the general population with lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer

et al. 2023

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Objective The aim of this study was to establish a nomogram graph model to accurately predict the venous thromboembolism (VTE) risk probability in the general population with lung cancer. Methods Based on data from patients with lung cancer in Chongqing University Cancer Hospital of China, the independent risk factors of VTE were identified by the logistic univariable and multivariable analysis and were integrated to construct a nomogram, which was validated internally. The predictive effectiveness of the nomogram was evaluated by the receiver operating characteristic curve (ROC) and calibration curve. Results A total of 3398 lung cancer patients were included for analysis. The nomogram incorporated eleven independent VTE risk factors including karnofsky performance scale (KPS), stage of cancer, varicosity, chronic obstructive pulmonary disease (COPD), central venous catheter (CVC), albumin, prothrombin time (PT), leukocyte counts, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), dexamethasone, and bevacizumab. The C-index of the nomogram model was 0.843 and 0.791 in the training and validation cohort, respectively, demonstrating good discriminative power. The calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities. Conclusions We established and validated a novel nomogram for predicting the risk of VTE in patients with lung cancer. The nomogram model could precisely estimate the VTE risk of individual lung cancer patients and identify high-risk patients who are in need of a specific anticoagulation treatment strategy.

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Derivation and validation of a nomogram model for pulmonary thromboembolism in patients undergoing lung cancer surgery

Cited by 10 publications

References 50 publications

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

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

Derivation and validation of a nomogram model for venous thromboembolism in patients undergoing borderline resectable pancreatic cancer surgery

Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer

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