Background Postoperative lung cancer patients belong to the high-risk group for venous thromboembolism (VTE). The standardized preventive measures for perioperative VTE in lung cancer are not perfect, especially for the prevention and treatment of catheter-related thrombosis (CRT) caused by carried central venous catheters (CVCs) in lung cancer surgery. Patients and methods This study included 460 patients with lung cancer undergoing video-assisted thoracic surgery (VATS) in our center from July 2020 to June 2021. Patients were randomized into two groups, and intraoperatively-placed CVCs would be carried to discharge. During hospitalization, the control group was treated with low-molecular-weight heparin (LMWH), and the experimental group with LMWH + intermittent pneumatic compression (IPC). Vascular ultrasound was performed at three time points which included before surgery, before discharge, and one month after discharge. The incidence of VTE between the two groups was studied by the Log-binomial regression model. Results CRT occurred in 71.7% of the experimental group and 79.7% of the control group. The multivariate regression showed that the risk of developing CRT in the experimental group was lower than in the control group (Adjusted RR = 0.889 [95%CI0.799–0.989], p = 0.031), with no heterogeneity in subgroups (P for Interaction > 0.05). Moreover, the fibrinogen of patients in the experimental group was lower than control group at follow-up ( P = 0.019). Conclusion IPC reduced the incidence of CRT during hospitalization in lung cancer patients after surgery. Trial registration No. ChiCTR2000034511.

Background Immunotherapy has become increasingly important in the perioperative period of non-small-cell lung cancer (NSCLC). In this study, we intended to develop a mutation-based model to predict the therapeutic effificacy of immune checkpoint inhibitors (ICIs) in patients with NSCLC. Methods Random Forest (RF) classifiers were generated to identify tumor gene mutated features associated with immunotherapy outcomes. Then the best classifier with the highest accuracy served for the development of the predictive model. The correlations of some reported biomarkers with the model were analyzed, such as TMB, PD-(L)1, KEAP1-driven co-mutations, and immune subtypes. The training cohort and validation cohorts performed survival analyses to estimate the predictive efficiency independently. Results An 18-gene set was selected using random forest (RF) classififiers. A predictive model was developed based on the number of mutant genes among the candidate genes, and patients were divided into the MT group (mutant gene ≥ 2) and WT group (mutant gene < 2). The MT group (N = 54) had better overall survival (OS) compared to the WT group (N = 290); the median OS was not reached vs. nine months (P < 0.0001, AUC = 0.73). The robust predictive performance was confifirmed in three validation cohorts, with an AUC of 0.70, 0.57, and 0.64 (P < 0.05). The MT group was characterized by high tumor neoantigen burden (TNB), increased immune infifiltration cells such as CD8 T and macrophage cells, and upregulated immune checkpoint molecules, suggesting potential biological advantages in ICIs therapy. Conclusions The predictive model could precisely predict the immunotherapeutic efficacy in NSCLC based on the mutant genes within the model. Furthermore, some immune-related features and cell expression could support robust efficiency.