ABSTRACT
Objective:To investigate the treatment efficacy of adjuvant anti-VEGF/VEGFR targeted therapy in patients with non-metastatic (cM 0) non-clear cell renal cell carcinoma and tumor thrombus (nccRCC-VTT). Methods:This retrospective study enrolled 26 patients who underwent radical nephrectomy combined with inferior vena cava tumor thrombectomy at Peking University Third Hospital from January 2014 to July 2021. Patients were divided into adjuvant therapy group (10 cases) and control group (16 cases)based on the use of postoperative targeted therapy. The distribution of baseline clinical characteristics in the adjuvant therapy group and the control group were as follows: gender (6 males and 4 females in the adjuvant therapy group, 12 males and 4 females in the control group, P=0.66), age (56.2±18.5 years old in the adjuvant therapy group; 54.6±14.5 years old in the control group; P=0.80), BMI(24.0±3.5 in the adjuvant therapy group; 24.3±3.3 in the control group; P=0.80), presence of clinical symptoms (8 cases in the adjuvant therapy group; 15 cases in the control group; P=0.54), tumor laterality(6 cases on the left and 4 cases on the right in the adjuvant therapy group; 6 cases on the left and 10 cases on the right in the control group; P=0.42), location of tumor thrombus (2 cases with renal vein tumor thrombus and 8 cases with inferior vena cava tumor thrombus in the adjuvant therapy group; 2 cases with renal vein tumor thrombus and 14 cases with inferior vena cava tumor thrombus in the control group; P=0.67), ASA classification (2 cases in ASA class 1 and 8 cases in ASA class 2 in the adjuvant therapy group; 2 cases in ASA class 1 and 14 cases in ASA class 2 in the control group; P=0.63), surgical approach (7 minimally invasive surgeries and 3 open surgeries in the adjuvant therapy group; 9 minimally invasive surgeries and 7 open surgeries in the control group; P=0.68), conversion to open surgery (2 cases in the adjuvant therapy group; 2 cases in the control group; P=0.63), operation time [287.5(222.2, 456.0) minutes in the adjuvant therapy group; 344.0(287.8, 482.5) minutes in the control group; P=0.34), blood loss [400.0(250.0, 600.0)ml in the adjuvant therapy group; 575.0(175.0, 800.0)ml in the control group; P=0.63), Clavien-Dindo classification of postoperative complications (8 cases with no postoperative complications, 2 cases with level 1-2 complications, and 0 cases with level ≥3 complications in the adjuvant therapy group; 10 cases with no postoperative complications, 4 cases with level 1-2 complications, and 2 cases with level ≥3 complications in the control group; P=0.68), postoperative hospital stay (8.5 [5.5, 11.5] days in the adjuvant therapy group; 7.5 [6.0, 13.0] days in the control group; P=1.00), maximum tumor diameter[ (9.2±2.7)cm in the adjuvant therapy group; (8.9±3.3)cm in the control group; P=0.81], sarcomatoid differentiation (0 cases in the adjuvant therapy group; 1 case in the control group; P=1.00), perinephric fat invasion (2 cases in the adjuvant therapy group; 7 cases in the control group; P=0.40), tumor necrosis (6 cases in the adjuvant therapy group; 5 cases in the control group; P=0.23), pathological subtype (1 case of PRCC type 1, 6 cases of PRCC type 2, and 3 cases of TFE3 rearrangement RCC in the adjuvant therapy group; 2 cases of PRCC type 1, 10 cases of PRCC type 2, and 1 case each of oncocytic PRCC, TFE3 rearrangement RCC, FH-deficient RCC, and unclassified RCC in the control group; P=0.72), WHO/ISUP nuclear grade (10 cases of grades 3-4 in the adjuvant therapy group; 4 cases of grades 1-2 and 12 cases of grades 3-4 in the control group; P=0.14), invasion of tumor thrombus into the vessel wall (5 cases in the adjuvant therapy group; 5 cases in the control group; P=0.43), T stage (1 case of T 3a, 3 cases of T 3b, 5 cases of T 3c, and 1 case of T 4 in the adjuvant therapy group; 1 case of T 3a, 4 cases of T 3b, 10 cases of T 3c, and 1 case of T 4 in the control group; P=1.00), and positive lymph nodes metastasis(3 cases in the adjuvant therapy group; 0 cases in the control group; P<0.05). The recommended doses for sunitinib, axitinib, and pazopanib are 50mg qd, 5mg q12h, and 800mg qd, respectively. The primary endpoint of this study was disease-free survival (DFS), and the secondary endpoint was overall survival (OS). Statistical analyses were performed using R v4.2.2. Confounding factors were adjusted using propensity score weighting. Results:The median follow-up time for DFS was 29 months in the adjuvant therapy group and not reached in the control group, while median follow-up time for OS was 28 and 26 months, respectively. In the univariate Cox regression analysis, there were no statistically significant difference in the impact of all baseline characteristics and exposure factors on DFS and OS between the two groups. In survival analysis, there were no significant difference between DFS and OS curves of patients in the adjuvant therapy group and the control group (DFS, P=0.62; OS, P=0.74). The median DFS of patients in the adjuvant therapy group and the control group were 17 and 19 months, respectively, while the median OS was 43 and 27 months. After adjusting for confounding factors, the median DFS of patients in the adjuvant therapy group and the control group were 26 and 12 months, respectively, and the median OS remained 43 and 27 months, with no significant difference (DFS, P=0.81; OS, P=0.40). Conclusion:There is currently a lack of definitive evidence for survival benefit from adjuvant anti-VEGF/VEGFR targeted therapy in patients with cM0 nccRCC-VTT after surgery.
ABSTRACT
Objective In observational studies or non-randomized design,the researchers' ability to make causal inferences from data was hampered by confounding factors.This study used this method to analyze a group of observational medical data in order to instruct relevant medical personnel to carry out their own causal inference studies.Methods At present,the four main types of propensity scoring methods:matching,stratification,inverse probability weighting and covariate adjustment have been widely used in the study of causal inference.Propensity score method can theoretically eliminate the bias of the observable confounding factors,so that the treatments variables are close to the result of random assignment design,thus,it is estimated that the treatment factor has a causal effect on the outcome.Results Considering the advantages of the inverse probability weighting method over other methods,this paper summarizes the applicable conditions for the estimate of causal effect,particularly illustrates the use of a modern nonparametric statistical technology--Generalized Boosted Models (GBM) and its advantages and disadvantages.Conclusion When there is a lot of different types of confounding factors,and uncertain functional forms for their associations with treatment selection in linear,non-linear or interaction effect,and other issues,GBM propensity score weighting method can overcome the obstacles in the process of accurately estimating propensity score.
ABSTRACT
In this article,we presented the rationale and calculation procedures of a propensity score weighting method,with its application in epidemiological studies.The rationale for propensity score weighting method is similar to those for traditional standardization methods.Propensity score is used to estimate the weight for each individual.As the propensity score serves the function of observed covariates,the propensity score weighting can balance the distribution of the observed covariates between the comparison groups.There are two weighting methods according to the target standard populations:the Inverse probability of treatment weighting(IPTW)and the Standardized mortality ratio weighting(SMRW).Results of the example show that the distribution of the covariates tended to be consistent after weighting,and the IPTW and SMRW methods showed similar effect estimates.Propensity score weighting method can effectively balance the distribution of the confounding factors between the compared groups in non-randomized controlled trials.