Development and validation of a predictive score for venous thromboembolism in newly diagnosed non-small cell lung cancer

The value of performance status in predicting venous thromboembolism in lung cancer patients treated with immune checkpoint inhibitors

INTRODUCTION

The incidence of venous thromboembolism (VTE) is notably high in lung cancer patients, particularly among those treated with immune checkpoint inhibitors (ICIs). Previous studies have focused on the relationship between Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) and VTE risk in immune checkpoint inhibitor therapy, but available evidence is inconsistent.

METHODS

The clinical data of lung cancer patients treated with ICIs were collected and analyzed from West China Hospital between January 2018 and March 2022. ECOG PS score was measured on admission. The primary outcome was the incidence of VTE, encompassing both deep vein thrombosis (DVT) and pulmonary embolism (PE). Multivariate logistic regression analysis was conducted to calculate the odds ratio (OR) and 95% confidence interval (95% CI).

RESULTS

A total of 1115 lung cancer patients receiving ICIs were eligible for this study, VTE developed in 105 (9.4%) during the 12-month follow-up, of which 95 (8.5%) had DVT,14 (1.3%) had definite PE. Poor ECOG PS (PS ≥ 2) was associated with an increased risk for VTE (OR = 5.405, 95% CI = 3.067-9.525, P < 0.001), DVT (OR = 4.669, 95% CI = 2.588-8.427, P < 0.001) and PE (OR = 8.413, 95% CI = 2.565-27.600, P < 0.001) after multivariable adjustment in the study cohort.

CONCLUSION

VTE occurred in 9.4% of lung cancer patients treated with ICIs, and poor performance status was associated with an increased risk of VTE.

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

BACKGROUND

Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high-risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management.

METHODS

Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748).

RESULTS

Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D-dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation.

CONCLUSION

VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.

Models for predicting venous thromboembolism in ambulatory patients with lung cancer: A systematic review and meta-analysis

AIMS

The incidence of venous thromboembolism (VTE) in patients with lung cancer is relatively high, and risk stratification models are vital for the targeted application of thromboprophylaxis. We aimed to review VTE risk prediction models that have been developed in patients with lung cancer and evaluated their performance.

METHODS AND RESULTS

Twenty-four eligible studies involving 123,493 patients were included. The pooled incidence of VTE within 12 months was 11 % (95 % CI 8 %-14 %). With the identified four VTE risk assessment tools, meta-analyses did not show a significant discriminatory capability of stratifying VTE risk for Khorana, PROTECHT and CONKO scores. The pooled sensitivity and specificity of the Khorana score were 24 % (95 % CI 11 %-44 %) and 84 % (95 % CI 73 %-91 %) at the 3-point cut-off, and 43 % (95 % CI 35 %-52 %) and 61 % (95 % CI 52 %-69 %) at the 2-point cut-off. However, a COMPASS-CAT score of ≥ 7 points indicated a significantly high VTE risk, with a RR of 4.68 (95 % CI 1.05-20.80).

CONCLUSIONS

The Khorana score lacked discriminatory capability in identifying patients with lung cancer at high VTE risk, regardless of the cut-off value. The COMPASS-CAT score had better performance, but further validation is needed. The results indicate the need for robust VTE risk assessment tools specifically designed and validated for lung cancer patients. Future research should include relevant biomarkers as important predictors and consider the combined use of risk tools. PROSPERO registration number: CRD42021245907.

Development and Validation of a Modified Khorana Score for Predicting Venous Thromboembolism in Newly Diagnosed Stage IV Lung Cancer

We aimed to establish an effective model to identify metastatic lung cancer patients at high risk of venous thromboembolism (VTE). Patients diagnosed with stage IV lung cancer from January 2011 to June 2019 were included in the development cohort; those recruited from July 2019 to June 2021 were included in the validation cohort. Univariable and multivariable analyses determined the risk factors for VTE. Then we assessed the value for predicting VTE of the Khorana score and modified Khorana score in these two cohorts; 575 patients were included in the development cohort, and 202 patients in the validation cohort. Adenocarcinoma, D-dimer, and the Khorana score were independent risk factors for VTE. In the development cohort, the area under the receiver operating characteristic curve (AUC) of the Khorana score in patients with newly diagnosed stage IV lung cancer was 0.598 (95% CI, 0.512-0.684). The AUC of the modified Khorana score was 0.747 (95% CI, 0.689-0.805). The difference was statistically significant (P <.001). The AUC of the modified Khorana score in the validation cohort was 0.763 (95% CI, 0.661-0.865). The modified Khorana score is more able to accurately predict VTE in patients with newly diagnosed stage IV lung cancer than the Khorana score.

The association of chest computed tomography-defined visual emphysema and prognosis in patients with nonsmall cell lung cancer

Background

Although computed tomography (CT)-defined emphysema is considered a predictor of lung cancer risk, it is not fully clear whether CT-defined emphysema is associated with the prognosis of lung cancer. We aimed to assess the clinical impact of CT-defined emphysema on the survival of lung cancer.

Methods

In the prospective cohort study of nonsmall cell lung cancer (NSCLC), the correlation between CT-defined emphysema and clinical variables was analysed. A multivariable Cox regression model was built to assess the association between CT-defined emphysema and overall survival (OS) for up to 8.8 years. The differences in survival analyses were derived by Kaplan-Meier analysis and log-rank testing. Low attenuation area (LAA%) was defined as the per cent of voxels below -950 HU.

Results

854 patients were included and CT-defined emphysema was present in 300 (35.1%) at diagnosis. Epidermal growth factor receptor (EGFR) wild-type (OR 1.998; p<0.001) and anaplastic lymphoma kinase (ALK) wild-type (OR 2.277; p=0.004) were associated with CT-defined emphysema. CT-defined emphysema remained a significant predictor of prognosis adjusting for age, sex, smoking history, tumour histology and Eastern Cooperative Oncology Group Performance Status (ECOG PS), whether in I-IIIA stage (adjusted hazard ratio (HR) 1.745; p=0.017) or in IIIB-IV stage (adjusted HR 1.291; p=0.022). Stratified analyses showed that OS rate among the driver oncogene groups with different CT-defined emphysema status differed significantly (log-rank p<0.001). Furthermore, patients with centrilobular emphysema (CLE) with LAA% >17% displayed poorer survival than those with LAA% ≤17% (median 432 versus 670 days; HR 1.564; p=0.020).

Conclusions

CT-defined emphysema, especially CLE with LAA%>17%, is an independent predictor of NSCLC prognosis. Moreover, prospective studies are needed to further explore this association.

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.

Standardization of risk prediction model reporting in cancer-associated thrombosis: Communication from the ISTH SSC subcommittee on hemostasis and malignancy

Since the development of the Khorana score to predict risk of cancer-associated venous thromboembolism (VTE), many modified and de novo risk prediction models (RPMs) have been proposed. Comparison of the prognostic performance across models requires comprehensive reporting and standardized methods for model development, validation and evaluation. To improve the standardization of RPM reporting, the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) tool was published in 2015. To better understand the quality of reporting and development of RPMs for cancer-associated VTE, we performed a literature search of published RPMs and assessed each model using the TRIPOD checklist. Our results yielded 29 RPMs for which 30 items were evaluated. There was a non-significant (p = 0.15) improvement in reporting of the 30 items in the post-TRIPOD era (81%) versus the pre-TRIPOD era (75%). Of seven items (title, sample size, missing data handling, baseline demographics, methods and results for model performance, and supplemental resources) with the lowest reporting in the pre-TRIPOD era (<70%), there was an average improvement of 22% in the post-TRIPOD era. Only two of the 22 studies published in the post-TRIPOD era acknowledged compliance with TRIPOD. Informed by the results of this assessment, the Scientific and Standardization Committee (SSC) Subcommittee on Hemostasis & Malignancy of the International Society on Thrombosis and Hemostasis (ISTH) advocates for standardization of four key elements of RPMs for cancer-associated VTE: (1) inclusion of the TRIPOD checklist, (2) clear definition of the derivation population, with justification of sample size, (3) clear definition of predictors, and (4) external validation prior to implementation.