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

Abstract

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.

Risk assessment of venous thromboembolism in head and neck cancer patients and its establishment of a prediction model

“…For example, D‐D, a specific fragment resulting from the degradation of cross‐linked fibrin, is used as sensitive biomarker of endogenous fibrinolysis and hypercoagulability status. It has been established by several studies that D‐D level was an independent risk predictor of VTE in various types of cancer, 17,27–30 and these results are highly in line with those from our study ( β 95% CI: 1.000–1.002, p = 0.009).…”

“…Our current data suggested the forecast accuracy of the ROC curve for nomogram model (AUC = 0.981, 95% CI: 0.964-0.998, p < 0.001) was significantly higher than those of other models, concerning the comparison of the current model with other existing models. 17,[27][28][29][30] However, some limitations of this investigation should not be ignored. First, relatively small sample size, single-center for screening resulted in selection bias to a certain extent.…”

“…Many randomized controlled trials have hitherto developed several scoring scales which are suitable to assess VTE risk for cancer patients, including Caprini, Khorana, COMPASS-CAT, Padua model, and so on. 30 This present study preliminarily explored independent factors influencing the risk of VTE in hospitalized HNC patients via using readily obtainable demographic, clinical, and experimental characteristics, to develop and validate a nomogram for the prediction of VTE occurrence. Based on previous literature reports as well as our clinical observations, five predictors were selected into a specific risk-stratification nomogram model by Lasso algorithm and directed acyclic graph.…”

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

“…Beside those factors, previous studies [ 22 , 23 ] mainly focused on the postoperative VTE risk in lung cancer. A recently reported study [ 27 ] established a nomogram-based risk assessment model for venous thromboembolism, but the number of enrolled patients was relatively limited. Compared with previous models, our study developed a nomogram model based on a large number of patients with a more general representation for lung cancer VTE risk prediction.…”

Profile of Solid Tumor Patients Complicated With Venous Thromboembolism: A 10-Year Retrospective Cross-Sectional Study Based on 1482 Cases

“…According to remarkable evidence from systematic review as well as cohort and case-control studies, obesity is an independent factor for VTE and might predispose to thromboembolic events. 10 , 31 – 33 With respect to BMI, it was shown that the majority of participants had a baseline BMI within the normal range according to WHO cut-off points (18.5-24.9 kg/m 2 ) and only 10.26% of the patients were obese with BMI values over 30 kg/m 2 . Nevertheless, 18.76% of patients were classified as obese according to the Chinese population standards.…”