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

Abstract

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.

A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer

“…A total of 5155 relevant studies were retrieved, and after screening, six studies were finally included. [29][30][31][32][33][34] The literature screening process is shown in Figure 2.…”

“…Three studies used decision curve analysis to confirm the clinical utility of the prediction model. 31,32,34 Three studies carried out external validation, 29,31,33 two studies carried out internal validation, 30,32 and one carried out both internal and external validation. 34 The model is presented in three main forms: additive scoring, scoring system and nomogram.…”

“…31,32,34 Three studies carried out external validation, 29,31,33 two studies carried out internal validation, 30,32 and one carried out both internal and external validation. 34 The model is presented in three main forms: additive scoring, scoring system and nomogram. The predictors can be divided into three categories: demographic factors, disease-related factors and laboratory examination indicators.…”

“…Demographic factors are less involved, mainly gender and age. 30,33 Disease-related factors include clinical stage, 34,35 pathological type, 29,30,32 chemotherapy history, 30,32 central venous catheter, 30,32,34 Eastern Cooperative Oncology Group (ECOG) performance score, and so forth. 29,31 Laboratory indicators such as D-dimer, neutrophil count and hemoglobin level are more common, [29][30][31][32][33] as shown in Table 3.…”

“…29,31,32 Another three studies have not mentioned the time interval and were evaluated as "unclear." 30,33,34 In the data analysis domain, all included studies generally performed poorly in terms of "analysis" and were at high risk of bias. The events per variable (EPV) of three studies was less than 10, 29,30,34 and the EPV of the other three studies was between 10 and 20, which failed to meet the requirements.…”