Risk Of Recurrence After Withdrawal Of Anticoagulation In Patients With Unprovoked Venous Thromboembolism: External Validation Of The Vienna Nomogram And The Dash Prediction Score

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

Background

A specific risk-stratification tool is needed to facilitate safe and cost-effective approaches to the prophylaxis of acute pulmonary thromboembolism (PTE) in lung cancer surgery patients. This study aimed to develop and validate a simple nomogram model for the prediction of PTE after lung cancer surgery using readily obtainable clinical characteristics.

Methods

A total of 14,427 consecutive adult patients who underwent lung cancer surgery between January 2015 and July 2018 in our institution were retrospectively reviewed. Included in the cohort were 136 patients who developed PTE and 544 non-PTE patients. The patients were randomly divided into the derivation group (70%, 95 PTE patients and 380 non-PTE patients) and the validation group (30%, 41 PTE patients and 164 non-PTE patients). A nomogram model was developed based on the results of multivariate logistic analysis in the derivation group. The cut-off values were defined using Youden's index. The prognostic accuracy was measured by area under the curve (AUC) values.

Results

In the derivation group, multivariate logistic analysis was carried out to evaluate the risk score. The risk assessment model contained five variables: age [95% confidence interval (CI): 1.008-1.083, P=0.016], body mass index (95% CI: 1.077-1.319, P=0.001), operation time (95% CI: 1.002-1.014, P=0.008), the serum level of cancer antigen 15-3 (CA15-3) before surgery (95% CI: 1.019-1.111, P=0.005), and the abnormal results of compression venous ultrasonography before surgery (95% CI: 2.819-18.838, P<0.001). All of them were independent risk factors of PTE. To simplify the risk assessment model, a nomogram model was established, which showed a good predictive performance in the derivation group (AUC 0.792, 95% CI: 0.734-0.853) and in the validation group (AUC 0.813, 95% CI: 0.737-0.890).

Conclusions

A high-performance nomogram was established on the risk factors for PTE in patients undergoing lung cancer surgery. The nomogram could be used to provide an individual risk assessment and guide prophylaxis decisions for patients. Further external validation of the model is needed in lung cancer surgery patients in other clinical centers.

From Classical Laboratory Parameters to Novel Biomarkers for the Diagnosis of Venous Thrombosis

Venous thrombosis is a common and potentially fatal disease, because of its high morbidity and mortality, especially in hospitalized patients. To establish the diagnosis of venous thrombosis, in the last years, a multi-modality approach that involves not only imaging modalities but also serology has been evolving. Multiple studies have demonstrated the use of some biomarkers, such as D-dimer, selectins, microparticles or inflammatory cytokines, for the diagnosis and treatment of venous thrombosis, but there is no single biomarker available to exclusively confirm the diagnosis of venous thrombosis. Considering the fact that there are some issues surrounding the management of patients with venous thrombosis and the duration of treatment, recent studies support the idea that these biomarkers may help guide the length of appropriate anticoagulation treatment, by identifying patients at high risk of recurrence. At the same time, biomarkers may help predict thrombus evolution, potentially identifying patients that would benefit from more aggressive therapies. This review focuses on classic and novel biomarkers currently under investigation, discussing their diagnostic performance and potential benefit in guiding the therapy for venous thrombosis.