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

External validation of the Khorana score for the prediction of venous thromboembolism in cancer patients: A systematic review and meta-analysis

BACKGROUND

Venous thromboembolism is the leading cause of death in cancer patients, second only to tumor progression. The Khorana score is recommended by clinical guidelines for identifying ambulatory cancer patients at high risk of venous thromboembolism during chemotherapy. However, its predictive performance is debated among cancer patients.

OBJECTIVES

To map the applicability of the Khorana score in cancer patients and to assess its predictive performance across various cancer types, providing guidance for clinicians and nurses to use it more appropriately.

DESIGN

Systematic review and meta-analysis.

METHODS

A comprehensive literature search of the electronic database was first conducted on August 30, 2023, and updated on May 20, 2024. Studies examining the Khorana score's predictive performance (including but not limited to the areas under the curve, C-index, and calibration plot) in cancer patients were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was applied to evaluate the methodological quality of the included studies. Data synthesis was achieved via random-effects meta-analysis using the R studio software. The subgroup analysis was performed according to the study design, clinical setting, cancer type, anti-cancer treatment stage, and country.

RESULTS

The review incorporated 67 studies, including 58 observational studies and nine randomized controlled trials. All included studies assessed the Khorana score's discrimination, with the C-index ranging from 0.40 to 0.84. The pooled C-index for randomized controlled trials was 0.61 (95 % CI 0.51-0.70), while observational studies showed a pooled C-index of 0.59 (95 % CI 0.57-0.60). Subgroup analyses revealed the pooled C-index for lung cancer, lymphoma, gastrointestinal cancer, and mixed cancer patients as 0.60 (95 % CI 0.53-0.67), 0.56 (95 % CI 0.51-0.61), 0.59 (95 % CI 0.39-0.76), and 0.60 (95 % CI 0.58-0.61), respectively. Inpatient and outpatient settings had the pooled C-index of 0.60 (95 % CI 0.58-0.63) and 0.58 (95 % CI 0.55-0.61), respectively. Calibration was assessed in only four studies. All included studies were identified to have a high risk of bias according to PROBAST.

CONCLUSION

The Khorana score has been widely validated in various types of cancer patients; however, it exhibited poor capability (pooled C-index<0.7) in accurately discriminating VTE risk among most types of cancer patients either in inpatient or outpatient settings. The Khorana score should be used with caution, and high-quality studies are needed to further validate its predictive performance.

REGISTRATION

The protocol for this study is registered with PROSPERO (registration number: CRD42023470320).

Developing a risk assessment tool for cancer-related venous thrombosis in China: a modified Delphi-analytic hierarchy process study

OBJECTIVE

To develop a Risk Assessment Tool for Cancer-related Venous Thrombosis in China.

METHODS

A modified two-round Delphi method was employed to establish consensus within a field to reach an agreement via a questionnaire or by interviewing a multidisciplinary panel of experts by collecting their feedback to inform the next round, exchanging their knowledge, experience, and opinions anonymously, and resolving uncertainties. Furthermore, The AHP (Analytic Hierarchy Process) was used to determine the final quality indicators' relative importance.

RESULTS

The expert's positive coefficient was 85.19% in the first round and 82.61% in the second round, with authoritative coefficients of 0.89 and 0.92 in the respective surveys. The P-value of Kendall's W test was all less than 0.001 for each round, and the W-value for concordance at the end of the two rounds was 0.115. The final Risk Assessment Tool for Cancer-related Venous Thrombosis consisted of three domains, ten subdomains, and 39 indicators, with patient factors weighing 0.1976, disease factors weighing 0.4905, and therapeutic factors weighing 0.3119.

CONCLUSION

The tool is significantly valid and reliable with a strong authority and coordination degree, and it can be used to assess the risk of cancer-related VTE and initiate appropriate thrombophylactic interventions in China.

A nomogram model to predict the portal vein thrombosis risk after surgery in patients with pancreatic cancer

Background

Portal vein thrombosis (PVT) is a common postoperative complication in patients with pancreatic cancer (PC), significantly affecting their quality of life and long-term prognosis. Our aim is to establish a new nomogram to predict the risk of PVT after PC surgery.

Method

We collected data from 416 patients who underwent PC surgery at our hospital between January 2011 and June 2022. This includes 87 patients with PVT and 329 patients without PVT. The patients were randomly divided into a training group and a validation group at a ratio of 7:3. We constructed a nomogram model using the outcomes from both univariate and multivariate logistic regression analyses conducted on the training group. The nomogram's predictive capacity was assessed using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

In the study, the prevalence of PVT was 20.9%. Age, albumin, vein reconstruction and preoperative D-dimer were independent related factors. The model achieved a C-index of 0.810 (95% confidence interval: 0.752-0.867), demonstrating excellent discrimination and calibration performance. The area under the ROC curve of the nomogram was 0.829 (95% CI: 0.750-0.909) in the validation group. DCA confirmed that the nomogram model was clinically useful when the incidence of PVT in patients was 5%-60%.

Conclusion

We have established a high-performance nomogram for predicting the risk of PVT in patients undergoing PC surgery. This will assist clinical doctors in identifying individuals at high risk of PVT and taking appropriate preventive measures.

Nomogram model combined thrombelastography for venous thromboembolism risk in patients undergoing lung cancer surgery

Background: The aim of this study was to develop a nomogram model in combination with thromboelastography (TEG) to predict the development of venous thromboembolism (VTE) after lung cancer surgery. Methods: The data of 502 patients who underwent surgical treatment for lung cancer from December 2020 to December 2022 were retrospectively analyzed. Patients were then randomized into training and validation groups. Univariate and multivariate logistic regression analyses were carried out in the training group and independent risk factors were included in the nomogram to construct risk prediction models. The predictive capability of the model was assessed by the consistency index (C-index), receiver operating characteristic curves (ROC), the calibration plot and decision curve analysis (DCA). Results: The nomogram risk prediction model comprised of the following five independent risk factors: age, operation time, forced expiratory volume in one second and postoperative TEG parameters k value(K) and reaction time(R). The nomogram model demonstrated better predictive power than the modified Caprini model, with the C-index being greater. The calibration curve verified the consistency of nomogram between the two groups. Furthermore, DCA demonstrated the clinical value and potential for practical application of the nomogram. Conclusion: This study is the first to combine TEG and clinical risk factors to construct a nomogram to predict the occurrence of VTE in patients after lung cancer surgery. This model provides a simple and user-friendly method to assess the probability of VTE in postoperative lung cancer patients, enabling clinicians to develop individualized preventive anticoagulation strategies to reduce the incidence of such complications.

Management of venous thromboembolism in patients with lung cancer: a state-of-the-art review

Venous thromboembolism (VTE) is common and life-threatening in patients with lung cancer. Management of VTE is critical for patients with lung cancer. Risk assessment, thromboprophylaxis and treatment of VTE constitute the core issues of VTE management in patients with lung cancer. Although its overall principles should follow recommendations in authoritative guidelines, VTE management in patients with lung cancer may be slightly special in some specific aspects. Despite the extensive validation of Khorana score for patients with all cancer types, its value in VTE risk assessment of patients with lung cancer is controversial. It is important to determine the VTE risk assessment score that can accurately and specifically assess the VTE risk of patients with lung cancer. Clinical practice patterns of thromboprophylaxis may vary by cancer types, since different sites of cancer may have different levels of VTE risk. To understand the thromboprophylaxis specific for lung cancer is of vital importance for patients with lung cancer. Although it is essential to comply with authoritative guidelines, the duration and timing of initiation of thromboprophylaxis in surgical patients with lung cancer may need further study. Taken together, the purpose of this review is to provide an overview of state-of-the-art VTE stewardship specific for patients with lung cancer.

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

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.

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.