Assessing venous thromboembolism risk in lung cancer: a comprehensive evaluation of the khorana score, clinical characteristics, and incidence of thrombosis

Introduction: Venous thromboembolism (VTE) occurrence in patients with lung cancer is associated with significant morbidity and mortality. The Khorana Score (KRS) is a validated risk stratification tool developed to estimate risk of VTE in patients with cancer that incorporates clinical and laboratory parameters to calculate individual risk. However, limitations of the KRS in lung cancer have been reported, and identification of other risk factors associated with increased risk of VTE are needed in this population.

Methods: We retrospectively evaluated 485 patients with advanced non-small cell lung cancer (NSCLC) and 67 patients with small cell lung cancer (SCLC) treated at our institution between March 2015 and April 2023 to assess the performance of the KRS in estimating risk of VTE in multiple clinically distinct subtypes of lung cancer.

Results: The median patient age at diagnosis was 66 years (range, 30-94 years). Patients were predominantly male (51.1%), Caucasian (81%), and had a history of smoking (79.4%). VTE events were observed in 123 patients (22.3%); 51 patients had DVT (41.5%), 50 patients had PE (40.7%), 13 patients had concurrent DVT and PE (7.3%), and other VTE occurred in 9 patients (7.3%). 478 patients (86.6%) had low-risk KRS (score = 0-2) and 74 patients (13.4%) had high-risk KRS (score ≥3). No statistically significant difference in VTE incidence was observed based on baseline KRS. Clinical factors that were noted to be associated with increased risk of VTE were NSCLC histology, Black race, and the presence of an oncogenic driver mutation. No significant differences in overall survival were noted in patients that experienced VTE.

Discussion: This study highlights the limitations of the KRS in predicting risk of development of VTE in patients with NSCLC and SCLC and identifies multiple clinical parameters associated with increased risk of VTE. Development of further risk stratification tools incorporating these clinical factors in addition to those in the KRS should be considered.

1 Introduction

Cancer increases risk of venous thromboembolism (VTE) and this risk varies based on specific histologic diagnosis, stage, and type of cancer treatment (1–4). VTE occurrence in patients with cancer can delay treatment, eliminate therapeutic options, increase risk of bleeding from therapeutic anticoagulation, and increase mortality (5–14). Patients with lung cancer, specifically, have increased risk of VTE with a reported incidence as high as 8% in advanced lung cancer patients undergoing treatment with chemotherapy (15, 16). Chew et al. showed that patients with newly diagnosed lung cancer had a 3% 1-year cumulative incidence of VTE and that mortality increased by 2.3 and 1.5 times in patients with non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), respectively, after developing VTE (17).

Pharmacologic VTE prophylaxis has been shown to reduce risk of VTE without significantly increasing major bleeding rates in ambulatory patients with cancer (18). However, the most recent American Society of Clinical Oncology guidelines from 2023 suggest that routine VTE prophylaxis for all cancer patients is not supported. Instead, the guidelines specify with moderate strength that only high-risk outpatients, using a cut-off of Khorana Risk Score (KRS) of 2 or higher, should be offered thromboprophylaxis provided there are minimal risk factors for bleeding and a risk-benefit discussion is held with the patient (19). The KRS is a validated risk stratification tool developed to estimate risk of VTE in patients with cancer. The KRS incorporates clinical (body mass index, cancer type) and laboratory (pre-treatment platelet, hemoglobin and white blood cell counts) parameters to calculate individual risk. Following development of their model, Khorana et al. showed that using a cutoff point of ≥3 to define high risk, the KRS had a negative predictive value of 98.5% and a positive predictive value of 6.7% for VTE in cancer patients (20).

In subsequent studies attempting to validate the KRS in lung cancer, limitations have been noted. In 2016, Mansfield et al. published a retrospective study of patients with lung cancer which showed that a high KRS, defined as KRS ≥3, was not associated with increased risk of VTE compared with an intermediate score in patients with both early stage and metastatic lung cancer (21). Despite the lack of association with VTE, however, a high KRS was a predictor of mortality (hazard ratio (HR) 1.7, 95% confidence interval (CI) 1.4 - 2.2). In 2020, van Es et al. showed that while KRS did accurately predict VTE risk in colorectal, pancreatic, gastric, ovarian, breast, brain, and bladder cancer, it did not perform well in assessing VTE risk in lung cancer patients (22).

Given the limitations of the KRS for predicting VTE risk in lung cancer patients, other clinical risk factors have been examined to improve the accuracy of estimating risk of VTE. Among these other risk factors, certain oncogenic driver mutations have been shown to potentially impact VTE risk. Previous studies have identified ALK and ROS1 rearrangements as being associated with increased VTE risk in NSCLC, while EGFR and KRAS mutations either have no effect or are negatively associated with VTE risk (23–26). Cancer stage, histology subtype, and type of treatment are additional risk factors for VTE that have been reported (16, 27–30). While multiple individual risk factors have been examined in retrospective studies, a comprehensive assessment of these risk factors within a large cohort of lung cancer patients with multiple histologic subtypes, oncogene driver mutations, and receiving a wide variety of treatment modalities for advanced lung cancer is lacking.

The primary objective of this study was to assess the performance of the KRS in estimating risk of VTE in multiple clinically distinct subtypes of lung cancer, including histologic and genomic subtypes. The secondary objectives of this study were to further investigate the impacts of histology subtype, presence of a driver mutation, treatment type, and other clinical parameters on VTE risk in our cohort of patients with advanced stage NSCLC and SCLC.

2 Materials and methods

We retrospectively evaluated patients diagnosed with advanced lung cancer treated at our institution between March 2015 and April 2023. Patient demographics, treatment information, and clinical outcomes were obtained by review of the electronic medical record. Our cohort included patients with advanced, stage IV NSCLC and extensive-stage SCLC (ES-SCLC). Patients with NSCLC had either de novo metastatic disease or metastatic recurrence of disease; see Figure 1 for details regarding patient selection. Patients were treated with chemotherapy only (CTX), immunotherapy only (IO), chemotherapy plus immunotherapy (CTX+IO), or tyrosine kinase inhibitor (TKI) monotherapy. Patients that did not receive treatment or were lost to follow-up were excluded. The study was approved by the Institutional Review Board for Health Sciences at the University of Virginia (HSR 18465 and HSR 19083).

Figure 1

Figure 1. Patient selection flowchart. Flowchart illustrating selection of patients for the study cohort. Inclusion and exclusion criteria are shown; numbers excluded at each step are indicated with the associated reasons for exclusion.

A baseline KRS for VTE was calculated for all patients using clinical and laboratory data from the most proximal clinical evaluation prior to initiation of treatment. The score was calculated using the following parameters: primary site of malignancy, pre-treatment platelet count ≥350×109/L, hemoglobin levels <10 g/dL (or use of red blood cell growth factors), pre-treatment leukocyte count >11×109/L, and BMI ≥35 kg/m2. Each component was assigned a score of 1 point, except for site of primary malignancy (high-risk cancers, such as gastric or pancreatic cancer, were assigned a value of 2 points). All patients in this study had a minimum score of 1 given their diagnosis of lung cancer. We classified patients as low risk (score = 1-2) or high risk (score ≥3).

Univariate and multivariate statistics were used to analyze primary and secondary variables. Means, medians, standard deviations, and either 95% confidence intervals (CI) or interquartile ranges (IQR) were computed for all continuous variables, including demographic and treatment variables; proportions and percentages were computed for categorical variables. In univariate analysis, one-way analysis of variance (ANOVA) was used to compare groups for continuous outcome variables, and chi-square tests were used to compare groups on categorical outcome variables. HR with 95% CI were reported for univariate and multivariate models. Landmark analysis was employed to adjust for potential bias in survival outcomes. Kaplan-Meier (KM) curves and Cox proportional hazards regression analyses were used to evaluate overall survival (OS). A p-value less than 0.05 was used to determine statistical significance.

3 Results

3.1 Patient characteristics

This retrospective cohort study consisted of 552 patients with advanced-stage lung cancer, including 485 patients with NSCLC and 67 patients with SCLC. Patient baseline characteristics and demographics are summarized in Table 1. The median patient age at diagnosis was 66 years (range, 30–94 years). Patients were predominantly male (51.1%), Caucasian (81%), and had a history of smoking (79.4%). The median body mass index (BMI) was 25.2 (range 13.4 - 50.9). The median Eastern Cooperative Oncology Group (ECOG) performance status was 1, and median Charlson Comorbidity Index (CCI) was 9 (range, 6-16). 184 patients (33.3%) had brain metastasis present at time of diagnosis. Median follow-up for all patients included in the analysis was 18.6 months (range, 0-138.5 months).

Table 1

Table 1. Patient characteristics.

The observed oncogenic driver alterations included the following genes: anaplastic lymphoma kinase (ALK), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), epidermal growth factor receptor (EGFR), kirsten rat sarcoma viral oncogene homolog (KRAS), mesenchymal epithelial transition factor receptor (MET), rearranged during transfection (RET), and c-ros oncogene 1 (ROS1). All driver mutations were identified in patients with NSCLC. We observed ALK rearrangements in 20 patients (3.6%), BRAF mutations in 22 patients (4.0%), EGFR mutations in 124 patients (22.5%), KRAS mutations in 100 patients (18.1%), RET rearrangements in 6 patients (1.1%), and ROS1 rearrangements in 8 patients (1.4%).

For first-line treatment regimens, in the entire cohort, 102 patients (18.5%) received CTX only, 191 patients received CTX + IO (34.6%), 125 patients (22.6%) received IO only, and 134 patients (24.3%) received TKI only.

3.2 VTE incidence

VTEs included in this analysis were deep vein thrombosis (DVT), pulmonary embolism (PE), superior mesenteric vein thrombosis, splenic vein thrombosis, cerebral venous sinus thrombosis, and internal jugular vein thrombosis. Arterial thrombi were excluded. VTE events other than DVT and PE will be grouped and classified as “Other” VTE from this point onwards.

VTE events were observed in 123 patients (22.3%) during the entire follow-up period. Detailed data on VTE are provided in Figure 2, Table 2, and Table 3. 51 patients had DVT (41.5%), 50 patients had PE (40.7%) and 13 had concurrent DVT and PE (7.3%). Other VTE occurred in 8 patients (6.5%); of hese events, 7 were central-line-associated VTE. One patient was diagnosed with PE in addition to an Other VTE. Median time from lung cancer diagnosis to development of VTE was 239 days (IQR 76–913 days); 5 thrombosis-related mortalities were observed. Of patients with VTE, the majority were Caucasian (96 patients, 78.0%); Black race was associated with a statistically significant increase in VTE (Pearson chi-square test, p=0.02), with VTE observed in 24 patients (4.3%). By histology, VTE occurred in 115 patients with NSCLC and 8 patients with SCLC (Pearson chi-square test, p=0.03). No statistically significant difference in VTE incidence was observed by treatment type (Pearson chi-square test, p=0.13).

Figure 2

Figure 2. VTE incidence stratified by histology and oncogene driver mutation. (A) Patients with non-small cell lung cancer (NSCLC) experienced significantly more VTE events compared to those with small cell lung cancer (SCLC) (p = 0.03). (B) NSCLC patients with oncogene driver mutations (MUT+) had significantly higher VTE events than those without identified mutations (MUT−) (p=0.002). (C) Distribution of VTE events across specific oncogene driver mutations (EGFR, KRAS, MET, BRAF, ALK, RET, ROS1). Data are plotted on a log₁₀ scale to illustrate differences in magnitude across subgroups. Yellow bars indicate patients with VTE; blue bars indicate patients without VTE. * = p ≤ 0.05, ** = p ≤ 0.01.

Table 2

Table 2. Types and timing of VTEs.

Table 3

Table 3. VTE incidence by patient characteristic.

3.3 VTE incidence by khorana risk score

Baseline KRSs were calculated for all 552 patients in our cohort: 478 patients (86.6%) had a low-risk KRS (score = 0-2), and 74 patients (13.4%) had a high-risk KRS (score ≥3). VTE events occurred in 108 patients with low-risk KRS (22.6%) and 15 patients with high-risk KRS (20.3%) during the entire follow-up period. No statistically significant difference in VTE incidence was observed based on KRS categorization (Pearson chi-square test, p=0.66). Among patients with low-risk KRS, cumulative VTE incidences (percent with event) were 10.7% at 6 months and 13.0% at 12 months (Figure 3A). Among patients with high-risk KRS, cumulative VTE incidences were 8.1% at 6 months and 9.5% at 12 months.

Figure 3

Figure 3. Cumulative VTE incidence over 12 months by Khorana Risk Score. (A) Cumulative VTE incidence for low (0-2, green line) and high (≥3, red line) KRS for the entire patient cohort over 12 months. (B) Cumulative VTE incidence for low and high KRS in NSCLC patients over 12 months. (C) Cumulative VTE incidence for low and high KRS in SCLC patients over 12 months.

Similar trends were observed by histology sub-type: in NSCLC patients with low-risk KRS (n=415), cumulative VTE incidences were 11.6% at 6 months and 13.5% at 12 months (Figure 3B). Among NSCLC patients with high-risk KRS (n=69), cumulative VTE incidences were 7.2% at 6 months and 8.7% at 12 months. In SCLC patients with low-risk KRS (n=63), cumulative VTE incidences were 4.8% at 6 months and 9.5% at 12 months (Figure 3C). Among SCLC patients with high-risk KRS (n=4), cumulative VTE incidences were 25.0% at 6 months and 25.0% at 12 months.

3.4 VTE incidence by driver mutation

VTEs were observed in 82 patients harboring a driver mutation and 41 patients without a driver mutation (Figure 2). By driver mutation type, VTEs occurred in 4 out of 20 patients with ALK rearrangements, 5 out of 23 patients with BRAF mutations, 35 out of 124 patients with EGFR mutations, 29 out of 100 patients with KRAS mutations, 7 out of 18 patients with MET mutations, 1 out of 6 patients with RET rearrangements, and 1 out of 8 patients with ROS1 rearrangements. Patients with a driver mutation had a statistically significant increased risk of VTE compared to patients that did not (Pearson chi-square test, p=0.002). No statistically significant increased risk of VTE was observed by individual oncogene driver mutation, although trends towards significance were seen in patients with EGFR mutations (Pearson chi-square test, p=0.08), MET mutations (Pearson chi-square test, p=0.09), and KRAS mutations (Pearson chi-square test, p=0.07).

3.5 Impact of anticoagulant and anti-platelet use on VTE incidence

Seventy-one patients (12.9%) in our cohort were prescribed and taking therapeutic anticoagulation for prior VTE events, atrial fibrillation, arterial thrombi, or another indication prior to their lung cancer diagnosis and treatment initiation. Of these patients, 4 patients developed VTE while on therapeutic anticoagulation, while 67 did not develop VTE. The use of therapeutic anticoagulation was associated with a statistically significant reduction in VTE incidence (Pearson chi-square test, p=0.001). Eleven patients with a high KRS were prescribed therapeutic anticoagulation prior to lung cancer diagnosis and initiation of treatment. Of these patients, 0 developed VTE while on therapeutic anticoagulation.

177 patients (32.1%) in our cohort were prescribed and taking an anti-platelet agent prior to lung cancer diagnosis and initiation of treatment. No statistically significant difference in VTE incidence was observed based on use of an anti-platelet agent; 35 patients developed VTE while receiving anti-platelet therapy, while 142 did not develop VTE (Pearson chi-square test, p=0.331).

3.6 Impact of VTE on overall survival

Landmark OS was calculated at the 3-, 6-, and 12-month time points for the entire cohort and for the NSCLC and SCLC histologic subgroups. Detailed data on OS are provided in Table 4. For the entire cohort, landmark OS was 21.1, 24.4, and 30.4 months at the 3-, 6-, and 12-month time points, respectively. Stratified by VTE status, landmark OS was 27.4, 28.8, and 34.8 months at the 3-, 6-, and 12-month time points, respectively, for those that experienced a VTE and 19.5, 22.8, and 29.8 months for those that did not. No statistically significant difference in OS was observed by VTE status for the entire cohort or by histologic subtype (Figure 4).

Table 4

Table 4. Landmark overall survival stratified by presence or absence of VTE.

Figure 4

Figure 4. Kaplan-Meier curves of overall survival stratified by VTE status. Survival probability over time (months) is shown for patients with VTE (blue line) and without VTE (gold line) for the entire patient cohort (A), those with NSCLC histology (B), and those with SCLC histology (C). No statistically significant difference in overall survival was observed when stratified by the occurrence of a VTE.

Univariate and multivariate Cox proportional hazards regression was performed to evaluate the association between baseline clinical factors and OS, stratified by VTE status. In multivariate analysis, NSCLC histology was independently associated with a lower hazard of death (HR 0.56, 95% CI 0.41-0.76, p<0.001) when compared to SCLC histology. Similarly, the presence of an oncogenic driver mutation (HR 0.72, 95% CI 0.58-0.91, p=0.005) and Caucasian/White race (HR 0.67, 95% CI 0.51-0.88, p=0.004) were associated with improved OS. Baseline KRS and sex were not significantly associated with survival; see Table 5 for full univariate and multivariate results.

Table 5

Table 5. Univariate and multivariate cox regression for overall survival stratified by presence or absence of VTE.

4 Discussion

In this real-world, retrospective analysis of patients with advanced lung cancer, the KRS did not effectively differentiate between patients at low and high risk for VTE, as we did not observe a significant difference in VTE incidence between patients with low and high baseline KRS. These results are consistent with previous reports. We observed higher rates of VTE in Black patients, patients with NSCLC histology, and patients harboring a driver mutation. We did not observe differences in VTE incidence based on initial treatment type, and the occurrence of a VTE did not impact OS for the entire cohort nor for the NSCLC and SCLC histologic subgroups.

Multiple previous retrospective studies have identified the limitations of the KRS in the context of lung cancer, similar to our findings (21, 22, 30). The KRS has also been evaluated prospectively in lung cancer patients in the noninterventional CANTARISK study, where it was not significantly associated with VTE in either univariate or multivariate analyses (10). Given our observed associations between NSCLC histology, the presence of a driver mutation, and increased risk of VTE, the limitations of the KRS may be because lung cancer specific risk factors were not incorporated into the score. Other clinical factors such as stage at diagnosis, disease burden, and the presence of visceral metastasis may impact VTE risk but are not integrated into the KRS (16, 27). The Caprini and Padua scores have also been studied as predictive models for assessing risk of VTE; however, these models have not been prospectively validated in patients with malignancy (31–33). Our findings highlight the limited utility of the KRS for VTE risk stratification in advanced lung cancer patients, particularly those with NSCLC, and the need for further risk stratification tools in this population. In addition to the parameters currently included in the KRS, lung cancer histology and the presence or absence of a driver mutation would be the most useful characteristics to validate and incorporate into future risk assessment models.

We observed higher rates of VTE in those patients with NSCLC compared to those with SCLC, similar to prior reports (17, 34). As shown in Figure 3, the majority of observed VTEs occurred during the first 3 months after initial lung cancer diagnosis, with a plateau in incidence in the subsequent months. If these findings are validated prospectively, future strategies incorporating prophylactic anticoagulation could consider shorter durations of anticoagulation in lung cancer patients based in part on this observation. Differing rates of VTE among the various histologic subtypes of lung cancer are thought to be due to multiple reasons. Among NSCLC patients, those with adenocarcinoma histology have been shown to be at an increased risk of VTE compared to those with squamous cell carcinoma histology (17, 35). Pre-clinical studies have shown that mucins produced by carcinomas can activate platelets through interactions with leukocyte l‐selectin and platelet P‐selectin, resulting in the generation of microthrombi and increased risk of VTE (36). Furthermore, we observed increased rates of VTE in Black patients compared to Caucasian and other patients, although our cohort included a small number of Black patients overall. This observation has been previously reported across multiple tumor types; however, the underlying reasons for this increased risk are currently unknown (37, 38).

The presence of a driver mutation in metastatic NSCLC has both prognostic and predictive significance. Patients in our study harboring a driver mutation had a statistically significant increase in VTE compared to those without a mutation. We did not observe a significant increase in VTE incidence when stratified by individual mutation type. However, a trend towards increased risk of VTE was observed in patients with EGFR, MET, and KRAS mutations. A meta-analysis by Qian et al. found that patients whose tumors harbor ROS1 or ALK rearrangements had the highest incidence of VTE, while no statistically significant increase in risk of VTE was seen in those with KRAS or EGFR mutations (23). EGFR mutations have been shown to be negatively associated with risk of VTE in other analyses (25). Our results differ from these studies, as we observed a trend towards increased risk of in VTEs in patients with EGFR (p=0.08) and KRAS (p=0.09) mutations compared to wild-type patients. The impact of treatment modality on VTE incidence in EGFR mutation positive NSCLC is unknown; however, in the MARIPOSA trial comparing amivantamab and lazertinib to osimertinib, patients treated with the combination had a 37% risk of VTE compared to 9% in patients receiving osimertinib (39). Following this observed increase in VTE risk, the trial protocol was amended to include recommendations for prophylactic anticoagulation for the first 4 months (40). Our study did not include patients treated with this regimen, however, and thus this does not explain the increase in VTEs in patients with EGFR mutations in our cohort. Given the small number of patients in our cohort with ROS1 and ALK rearrangements, we cannot draw conclusions about these patients and VTE risk.

Our study is limited by its single center, retrospective design. A survival bias may also exist, as patients with NSCLC and or a driver mutation may have longer OS and thus may have more time to develop VTE compared to those with SCLC or no observed driver mutation. Additionally, given low use of VTE primary prophylaxis in our cohort, we are unable to evaluate the impact of prophylactic anticoagulation on VTE risk in this population. Despite these limitations, our study adds valuable real-world insights into the patterns and predictors of VTE in lung cancer patients, with long-term, longitudinal follow-up that complements prior registry-based and prospective research.

In conclusion, our results suggest that incorporating patient, molecular, and histology-specific factors may improve risk prediction models for VTE in lung cancer. Currently, the KRS is the most widely used risk stratification tool for VTE in patients with cancer. A high KRS score suggests potential need for prophylactic anticoagulation, however as this study shows, application of the KRS to lung cancer patients may not be appropriate given our observation that a high KRS does not correlate with increased VTE incidence. Further studies to evaluate the risk of VTE in mutational subsets identified in our study are warranted, and it remains unclear whether these particular subsets could benefit from prophylactic anticoagulation. It is also unclear whether prophylactic anticoagulation in high-risk patients would have an impact on OS. Taken together, our findings support the need for revised or alternative VTE risk stratification tools tailored to patients with lung cancer. Prospective studies are warranted to validate our findings and to evaluate whether integrating certain clinical factors can guide prophylactic anticoagulation strategies more effectively than current models.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institutional Review Board for Health Sciences at the University of Virginia (HSR 18465 and HSR 19083). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

Author contributions

SD: Investigation, Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing, Data curation. RS: Writing – review & editing, Investigation, Formal analysis, Writing – original draft, Data curation, Conceptualization. CM: Conceptualization, Investigation, Writing – review & editing, Data curation, Writing – original draft, Formal analysis. AS: Writing – review & editing, Writing – original draft, Data curation. JM: Writing – review & editing, Data curation, Writing – original draft. AL: Writing – review & editing, Supervision, Writing – original draft, Conceptualization, Project administration. RG: Methodology, Conceptualization, Writing – review & editing, Investigation, Writing – original draft, Supervision. WN: Writing – original draft, Formal analysis, Data curation, Writing – review & editing. NR: Writing – original draft, Project administration, Methodology, Supervision, Data curation, Conceptualization, Investigation, Writing – review & editing. RH: Writing – original draft, Conceptualization, Investigation, Methodology, Project administration, Writing – review & editing, Supervision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. Publication costs were supported by the Anne A. Wallen, Daniel L. Wallen, and Jane S. Anderson Endowment Fund.

Conflict of interest

RDG reports the following conflicts of interest for the prior 3 years: research funding to institution from Eli Lilly, Prelude Therapeutics, Pfizer, Tempus, Nalo Therapeutics, Puma, Mirati, Bristol Myers Squibb, Dizal, Chugai, Amgen, AstraZeneca, Janssen, Daiichi Sankyo, Jounce Therapeutics, Takeda, Merck, Alliance Foundation, ECOG/ACRIN, NCI, Big Ten Research Consortium, Hoosier Cancer Research Network, and SWOG; Honoraria from Curio, Bio Ascend, Academy for Continued Healthcare Learning, Curio, OncLive, Aptitude Health, MedStar Health, Clinical Care Options, Society for Immunotherapy of Cancer SITC and American Society of Clinical Oncology; Travel support to meetings from Dava Oncology, Tempus, American Society of Clinical Oncology ASCO, and International Association for the Study of Lung Cancer IASLC; Consulting fees from Amgen, Genentech, AstraZeneca, Regeneron, Merus, Takeda, Gilead, Abbvie, Daiichi Sankyo, Glaxo Smith Kline; and Leadership roles with Hoosier Cancer Research Network, ASCO, Journal of Clinical Oncology, NCI Investigational Drug Steering Committee, and IASLC Conference Planning Committees.

RDH reports the following conflicts of interest for the prior 3 years: research funding to the institution from Amgen, AstraZeneca, Alliance Foundation, Big Ten Research Consortium, Daiichi Sankyo, ECOG-ACRIN, Genentech, Hoosier Cancer Research Network, Lilly, Merck, Mirati, NCI, Regeneron; consulting fees from Bayer, EMD Serono, Jazz, Regeneron, Takeda.

SCD reports the following conflicts of interest for the prior 3 years: Honoraria from OncLive; Travel support to meetings from OncLive and International Association for the Study of Lung Cancer IASLC.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Heit JA, Silverstein MD, Mohr DN, Petterson TM, O’Fallon WM, and Melton LJ 3rd. Risk factors for deep vein thrombosis and pulmonary embolism: a population-based case-control study. Arch Intern Med. (2000) 160:809–15. doi: 10.1001/archinte.160.6.809

PubMed Abstract | Crossref Full Text | Google Scholar

2. Green KB and Silverstein RL. Hypercoagulability in cancer. Hematol Oncol Clin North Am. (1996) 10:499–530. doi: 10.1016/S0889-8588(05)70349-X

PubMed Abstract | Crossref Full Text | Google Scholar

3. Sallah S, Wan JY, and Nguyen NP. Venous thrombosis in patients with solid tumors: determination of frequency and characteristics. Thromb Haemost. (2002) 87:575–9.

PubMed Abstract | Google Scholar

4. Levitan N, Dowlati A, Remick SC, Tahsildar HI, Sivinski LD, Beyth R, et al. Rates of initial and recurrent thromboembolic disease among patients with Malignancy versus those without Malignancy. Risk analysis using Medicare claims data. Med (Baltimore). (1999) 78:285–91. doi: 10.1097/00005792-199909000-00001

PubMed Abstract | Crossref Full Text | Google Scholar

5. Chee CE, Ashrani AA, Marks RS, Petterson TM, Bailey KR, Melton LJ 3rd, et al. Predictors of venous thromboembolism recurrence and bleeding among active cancer patients: a population-based cohort study. Blood. (2014) 123:3972–8. doi: 10.1182/blood-2014-01-549733

PubMed Abstract | Crossref Full Text | Google Scholar

6. Prandoni P. Cancer and thromboembolic disease: how important is the risk of thrombosis? Cancer Treat Rev. (2002) 28:133–6. doi: 10.1016/s0305-7372(02)00041-5

PubMed Abstract | Crossref Full Text | Google Scholar

7. Ambrus JL, Ambrus CM, Mink IB, and Pickren JW. Causes of death in cancer patients. J Med. (1975) 6:61–4.

PubMed Abstract | Google Scholar

8. Khorana AA, Francis CW, Culakova E, Kuderer NM, and Lyman GH. Thromboembolism is a leading cause of death in cancer patients receiving outpatient chemotherapy. J Thromb Haemost. (2007) 5:632–4. doi: 10.1111/j.1538-7836.2007.02374.x

PubMed Abstract | Crossref Full Text | Google Scholar

9. Sorensen HT, Mellemkjaer L, Olsen JH, and Baron JA. Prognosis of cancers associated with venous thromboembolism. N Engl J Med. (2000) 343:1846–50. doi: 10.1056/NEJM200012213432504

PubMed Abstract | Crossref Full Text | Google Scholar

10. Kuderer NM, Poniewierski MS, Culakova E, Lyman GH, Khorana AA, Pabinger I, et al. Predictors of venous thromboembolism and early mortality in lung cancer: results from a global prospective study (CANTARISK). Oncologist. (2018) 23:247–55. doi: 10.1634/theoncologist.2017-0205

PubMed Abstract | Crossref Full Text | Google Scholar

11. Chew HK, Wun T, Harvey D, Zhou H, and White RH. Incidence of venous thromboembolism and its effect on survival among patients with common cancers. Arch Intern Med. (2006) 166:458–64. doi: 10.1001/archinte.166.4.458

PubMed Abstract | Crossref Full Text | Google Scholar

12. Chew HK, Wun T, Harvey DJ, Zhou H, and White RH. Incidence of venous thromboembolism and the impact on survival in breast cancer patients. J Clin Oncol. (2007) 25:70–6. doi: 10.1200/JCO.2006.07.4393

PubMed Abstract | Crossref Full Text | Google Scholar

13. Zer A, Moskovitz M, Hwang DM, Hershko-Klement A, Fridel L, Korpanty GJ, et al. ALK-rearranged non-small-cell lung cancer is associated with a high rate of venous thromboembolism. Clin Lung Cancer. (2017) 18:156–61. doi: 10.1016/j.cllc.2016.10.007

PubMed Abstract | Crossref Full Text | Google Scholar

14. Lyman GH, Culakova E, Poniewierski MS, and Kuderer NM. Morbidity, mortality and costs associated with venous thromboembolism in hospitalized patients with cancer. Thromb Res. (2018) 164 Suppl 1:S112–S8. doi: 10.1016/j.thromres.2018.01.028

PubMed Abstract | Crossref Full Text | Google Scholar

15. Hicks LK, Cheung MC, Ding K, Hasan B, Seymour L, Le Maitre A, et al. Venous thromboembolism and nonsmall cell lung cancer: a pooled analysis of National Cancer Institute of Canada Clinical Trials Group trials. Cancer. (2009) 115:5516–25. doi: 10.1002/cncr.24596

PubMed Abstract | Crossref Full Text | Google Scholar

16. Yang R, Wang H, Liu D, and Li W. Incidence and risk factors of VTE in lung cancer: a meta-analysis. Ann Med. (2024) 56:2390200. doi: 10.1080/07853890.2024.2390200

PubMed Abstract | Crossref Full Text | Google Scholar

17. Chew HK, Davies AM, Wun T, Harvey D, Zhou H, and White RH. The incidence of venous thromboembolism among patients with primary lung cancer. J Thromb Haemost. (2008) 6:601–8. doi: 10.1111/j.1538-7836.2008.02908.x

PubMed Abstract | Crossref Full Text | Google Scholar

18. Becattini C, Verso M, Munoz A, and Agnelli G. Updated meta-analysis on prevention of venous thromboembolism in ambulatory cancer patients. Haematologica. (2020) 105:838–48. doi: 10.3324/haematol.2019.221424

PubMed Abstract | Crossref Full Text | Google Scholar

19. Key NS, Khorana AA, Kuderer NM, Bohlke K, Lee AYY, Arcelus JI, et al. Venous thromboembolism prophylaxis and treatment in patients with cancer: ASCO guideline update. J Clin Oncol. (2023) 41:3063–71. doi: 10.1200/JCO.23.00294

PubMed Abstract | Crossref Full Text | Google Scholar

20. Khorana AA, Kuderer NM, Culakova E, Lyman GH, and Francis CW. Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood. (2008) 111:4902–7. doi: 10.1182/blood-2007-10-116327

PubMed Abstract | Crossref Full Text | Google Scholar

21. Mansfield AS, Tafur AJ, Wang CE, Kourelis TV, Wysokinska EM, and Yang P. Predictors of active cancer thromboembolic outcomes: validation of the Khorana score among patients with lung cancer. J Thromb Haemost. (2016) 14:1773–8. doi: 10.1111/jth.13378

PubMed Abstract | Crossref Full Text | Google Scholar

22. van Es N, Ventresca M, Di Nisio M, Zhou Q, Noble S, Crowther M, et al. The Khorana score for prediction of venous thromboembolism in cancer patients: An individual patient data meta-analysis. J Thromb Haemost. (2020) 18:1940–51. doi: 10.1111/jth.14824

PubMed Abstract | Crossref Full Text | Google Scholar

23. Qian X, Fu M, Zheng J, Zhou J, and Zhou J. Driver genes associated with the incidence of venous thromboembolism in patients with non-small-cell lung cancer: A systematic review and meta-analysis. Front Oncol. (2021) 11:680191. doi: 10.3389/fonc.2021.680191

PubMed Abstract | Crossref Full Text | Google Scholar

24. Abufarhaneh M, Pandya RK, Alkhaja A, Iansavichene A, Welch S, and Lazo-Langner A. Association between genetic mutations and risk of venous thromboembolism in patients with solid tumor Malignancies: A systematic review and meta-analysis. Thromb Res. (2022) 213:47–56. doi: 10.1016/j.thromres.2022.02.022

PubMed Abstract | Crossref Full Text | Google Scholar

25. Dou F, Li H, Zhu M, Liang L, Zhang Y, Yi J, et al. Association between oncogenic status and risk of venous thromboembolism in patients with non-small cell lung cancer. Respir Res. (2018) 19:88. doi: 10.1186/s12931-018-0791-2

PubMed Abstract | Crossref Full Text | Google Scholar

26. Zhu VW, Zhao JJ, Gao Y, Syn NL, Zhang SS, Ou SI, et al. Thromboembolism in ALK+ and ROS1+ NSCLC patients: A systematic review and meta-analysis. Lung Cancer. (2021) 157:147–55. doi: 10.1016/j.lungcan.2021.05.019

PubMed Abstract | Crossref Full Text | Google Scholar

27. Morkved AL, Sogaard M, Skjoth F, Ording AG, Jensen M, Larsen TB, et al. Risk and timing of venous thromboembolism after surgery for lung cancer: A nationwide cohort study. Ann Thorac Surg. (2024) 117:289–96. doi: 10.1016/j.athoracsur.2023.10.015

PubMed Abstract | Crossref Full Text | Google Scholar

28. Walker AJ, Baldwin DR, Card TR, Powell HA, Hubbard RB, and Grainge MJ. Risk of venous thromboembolism in people with lung cancer: a cohort study using linked UK healthcare data. Br J Cancer. (2016) 115:115–21. doi: 10.1038/bjc.2016.143

PubMed Abstract | Crossref Full Text | Google Scholar

29. Ording AG, Christensen TD, Skjoth F, Noble S, Hojen AA, Morkved AL, et al. Risk of venous thromboembolism in patients with stage III and IV non-small-cell lung cancer: nationwide descriptive cohort study. Clin Lung Cancer. (2024) 25:407–16.e1. doi: 10.1016/j.cllc.2024.04.004

PubMed Abstract | Crossref Full Text | Google Scholar

30. Madison CJ, Melson RA, Conlin MJ, Gundle KR, Thompson RF, and Calverley DC. Thromboembolic risk in patients with lung cancer receiving systemic therapy. Br J Haematol. (2021) 194:179–90. doi: 10.1111/bjh.17476

PubMed Abstract | Crossref Full Text | Google Scholar

31. Caprini JA, Arcelus JI, Hasty JH, Tamhane AC, and Fabrega F. Clinical assessment of venous thromboembolic risk in surgical patients. Semin Thromb Hemost. (1991) 17 Suppl 3:304–12.

Google Scholar

32. Obi AT, Pannucci CJ, Nackashi A, Abdullah N, Alvarez R, Bahl V, et al. Validation of the caprini venous thromboembolism risk assessment model in critically ill surgical patients. JAMA Surg. (2015) 150:941–8. doi: 10.1001/jamasurg.2015.1841

PubMed Abstract | Crossref Full Text | Google Scholar

33. Barbar S, Noventa F, Rossetto V, Ferrari A, Brandolin B, Perlati M, et al. A risk assessment model for the identification of hospitalized medical patients at risk for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost. (2010) 8:2450–7. doi: 10.1111/j.1538-7836.2010.04044.x

PubMed Abstract | Crossref Full Text | Google Scholar

34. Vitale C, D’Amato M, Calabro P, Stanziola AA, Mormile M, and Molino A. Venous thromboembolism and lung cancer: a review. Multidiscip Respir Med. (2015) 10:28. doi: 10.1186/s40248-015-0021-4

PubMed Abstract | Crossref Full Text | Google Scholar

35. Blom JW, Osanto S, and Rosendaal FR. The risk of a venous thrombotic event in lung cancer patients: higher risk for adenocarcinoma than squamous cell carcinoma. J Thromb Haemost. (2004) 2:1760–5. doi: 10.1111/j.1538-7836.2004.00928.x

PubMed Abstract | Crossref Full Text | Google Scholar

36. Wahrenbrock M, Borsig L, Le D, Varki N, and Varki A. Selectin-mucin interactions as a probable molecular explanation for the association of Trousseau syndrome with mucinous adenocarcinomas. J Clin Invest. (2003) 112:853–62. doi: 10.1172/JCI200318882

PubMed Abstract | Crossref Full Text | Google Scholar

37. Datta T, Brunson A, Mahajan A, Keegan T, and Wun T. Racial disparities in cancer-associated thrombosis. Blood Adv. (2022) 6:3167–77. doi: 10.1182/bloodadvances.2021006209

PubMed Abstract | Crossref Full Text | Google Scholar

38. Addo-Tabiri NO, Chudasama R, Vasudeva R, Leiva O, Garcia B, Ravid JD, et al. Black patients experience highest rates of cancer-associated venous thromboembolism. Am J Clin Oncol. (2020) 43:94–100. doi: 10.1097/COC.0000000000000639

PubMed Abstract | Crossref Full Text | Google Scholar

39. Cho BC, Lu S, Felip E, Spira AI, Girard N, Lee JS, et al. Amivantamab plus lazertinib in previously untreated EGFR-mutated advanced NSCLC. N Engl J Med. (2024) 391:1486–98. doi: 10.1056/NEJMoa2403614

PubMed Abstract | Crossref Full Text | Google Scholar

40. Passaro A, Wang J, Wang Y, Lee SH, Melosky B, Shih JY, et al. Amivantamab plus chemotherapy with and without lazertinib in EGFR-mutant advanced NSCLC after disease progression on osimertinib: primary results from the phase III MARIPOSA-2 study. Ann Oncol. (2024) 35:77–90. doi: 10.1016/j.annonc.2023.10.117

PubMed Abstract | Crossref Full Text | Google Scholar