Abstract
Background
The association between body mass index (BMI) and the risk of catheter-related thrombosis in breast cancer patients remains unclear, particularly regarding potential non-linear relationships and clinically relevant thresholds. This study aimed to investigate the impact of BMI on the risk of port-related thrombosis and to characterize its dose–response pattern.
Methods
This retrospective cohort study included 1,247 breast cancer patients who underwent totally implantable venous access port (TIVAP) implantation at a single center between January 2019 and September 2025. Restricted cubic spline (RCS) analysis was performed to explore the non-linear association between BMI and thrombosis risk and to identify a potential threshold. Based on the RCS results, patients were categorized into two groups (BMI ≤ 23 vs. > 23 kg/m²). Logistic regression analyses were conducted to identify independent risk factors, and Kaplan–Meier analysis was used to evaluate thrombosis-free survival.
Results
RCS analysis revealed a non-linear association between BMI and the risk of port-related thrombosis, with a threshold at approximately 23 kg/m², beyond which the risk increased progressively. The incidence of thrombosis was significantly higher in patients with BMI > 23 kg/m² compared with those with BMI ≤ 23 kg/m² (9.30% vs. 5.58%, P = 0.013). In multivariate logistic regression analysis, BMI > 23 kg/m² remained an independent risk factor for thrombosis (OR = 1.58, 95% CI: 1.02–2.47, P = 0.042), while hypertension was also associated with increased risk (OR = 2.53, 95% CI: 1.37–4.66, P = 0.003), and HER2-positive status was associated with a lower risk of thrombosis (OR = 0.58, 95% CI: 0.36–0.93, P = 0.024). Kaplan–Meier analysis showed that patients with higher BMI had significantly lower thrombosis-free survival, with early curve separation and a progressively widening difference over time.
Conclusions
Elevated BMI is significantly associated with an increased risk of port-related thrombosis in breast cancer patients, with a non-linear dose–response relationship and a threshold effect at approximately 23 kg/m². These findings suggest that BMI may serve as a clinically useful marker for risk stratification and highlight the importance of tailored thromboprophylaxis strategies in high-risk patients.
Keywords: Breast cancer, Body mass index, Port-related thrombosis, Risk factors
Introduction
Breast cancer is the most commonly diagnosed malignancy among women worldwide, with approximately 2.3 million new cases annually, accounting for 11.7% of all cancer diagnoses [1]. With the increasing use of totally implantable venous access ports (TIVAPs) to facilitate long-term systemic therapy, catheter-related thrombosis has emerged as a clinically relevant complication. Recent evidence indicates that the incidence of catheter-related thrombosis in breast cancer patients ranges from 7.0% to 12.9% in meta-analyses [1], while cohort studies focusing on implanted ports report symptomatic thrombosis rates of approximately 3.3%, with higher rates when asymptomatic events are included [2]. Given its potential to cause catheter dysfunction, treatment interruption, and severe complications such as pulmonary embolism, identifying modifiable risk factors is of substantial clinical importance.
Body mass index (BMI), defined as weight in kilograms divided by height in meters squared (kg/m²), is a widely used measure of adiposity and metabolic status. Accumulating evidence suggests that elevated BMI contributes to a prothrombotic state through multiple mechanisms, including chronic low-grade inflammation, endothelial dysfunction, increased procoagulant activity, and impaired fibrinolysis. In cancer populations, obesity has been consistently associated with an increased risk of venous thromboembolism and adverse clinical outcomes. In breast cancer patients, several recent studies have further indicated that higher BMI may be associated with an increased risk of catheter-related thrombosis [1], highlighting its potential role as a readily available clinical predictor.
However, the association between BMI and catheter-related thrombosis remains inconclusive. While some studies have identified elevated BMI—commonly defined using conventional thresholds such as ≥ 25 kg/m²—as a significant risk factor, others have reported null findings. These discrepancies may be attributed to heterogeneity in study populations, catheter types, sample sizes, and statistical approaches [3, 4]. Notably, most previous studies have relied on categorical BMI classifications or assumed linear relationships, which may fail to capture the complexity of the association [5, 6]. The potential non-linear dose–response relationship and clinically relevant thresholds between BMI and thrombosis risk remain insufficiently explored.
Therefore, we conducted a retrospective cohort study to systematically evaluate the association between BMI and port-related thrombosis in breast cancer patients. By integrating restricted cubic spline (RCS) analysis, multivariable logistic regression, and survival analysis, we aimed to characterize the dose–response relationship, identify potential non-linear patterns, and determine clinically meaningful thresholds. This data-driven approach may provide a more precise framework for risk stratification and contribute to the optimization of thrombosis prevention strategies in this population. To our knowledge, few studies have specifically addressed the non-linear relationship between BMI and port-related thrombosis in breast cancer patients.
Methods
Study design and population
This retrospective cohort study included consecutive patients with breast cancer who underwent TIVAP implantation at Quanzhou First Hospital between January 2019 and September 2025. Eligible patients had a histologically confirmed diagnosis of breast cancer and received TIVAP placement for systemic therapy. Patients were excluded if they had a history of venous thromboembolism prior to port implantation, incomplete clinical or follow-up data, or a follow-up duration of less than 2 months. After applying the inclusion and exclusion criteria, a total of 1,247 patients were ultimately included in the final analysis. As this was a retrospective cohort study, no formal a priori sample size calculation was performed. Instead, all consecutive eligible patients undergoing TIVAP implantation during the predefined study period were included to minimize selection bias. Additionally, 94 thrombosis events were observed, providing sufficient outcome events to support multivariable regression analyses. The study protocol was approved by the Ethics Committee of Quanzhou First Hospital, and complied with the principles of the Declaration of Helsinki. Due to the retrospective design and the use of anonymized clinical data, the requirement for informed consent was waived by the Ethics Committee.
Data collection and variable definition
Clinical and pathological data were extracted from the electronic medical record system, including age, BMI, hypertension status, platelet count, catheter-related variables (insertion side, catheter tip position, and catheter adjustment), tumor stage (T, N, and M stage), hormone receptor status, HER2 status, and endocrine therapy. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m²). To investigate the dose–response relationship between BMI and the risk of port-related thrombosis and to explore the presence of a potential threshold effect, RCS analysis was performed with BMI treated as a continuous variable. In the RCS model, knots were placed at the 5th, 35th, 65th, and 95th percentiles of the BMI distribution to allow flexible modeling of potential non-linear associations. The spline model revealed a non-linear association between BMI and thrombosis risk, with an apparent inflection point at approximately 23 kg/m², beyond which the risk increased progressively. Based on this data-driven inflection point, patients were subsequently categorized into two groups (BMI ≤ 23 kg/m² and BMI > 23 kg/m²) for exploratory analyses aimed at improving clinical interpretability. Endocrine therapy was recorded as a dichotomous variable (Yes/No) and included in the multivariate regression model as a covariate. In clinical practice, although TIVAPs are primarily used during chemotherapy, a proportion of patients retain their ports during subsequent endocrine therapy due to individualized clinical decision-making, including cost considerations and potential need for future intravenous access. Therefore, endocrine therapy may overlap with the period of port indwelling in some patients.
Study endpoint and follow-up
The primary endpoint of this study was port-related thrombosis, defined as catheter-associated thrombosis confirmed by imaging during the follow-up period. As part of routine clinical management, all patients underwent vascular ultrasound examination before infusion port removal regardless of symptom status, enabling routine imaging surveillance for thrombosis detection. Patients were followed from the date of TIVAP implantation until the occurrence of thrombosis, catheter removal, death, or the end of follow-up, with a median follow-up duration of 23 months. Thrombosis-free survival was defined as the time interval from port implantation to the first documented catheter-related thrombosis event, with patients who did not experience thrombosis censored at the time of catheter removal, death, or the end of follow-up.
Statistical analysis
Categorical variables were compared between groups using the chi-square test or Fisher’s exact test, as appropriate. To evaluate the association between BMI and the risk of thrombosis, BMI was analyzed as a categorical variable (≤ 23 vs. > 23 kg/m²). Univariate logistic regression analysis was first performed to identify potential risk factors for port-related thrombosis. Variables with a P value < 0.05 in the univariate analysis or those considered clinically relevant were subsequently included in the multivariate logistic regression model to adjust for potential confounders. Kaplan–Meier survival curves were generated to estimate thrombosis-free survival, and differences between groups were assessed using the log-rank test. All statistical analyses were performed using R software (version 4.2.3). All tests were two-sided, and a P value < 0.05 was considered statistically significant.
Results
Non-linear association between bmi and thrombosis risk
RCS analysis was performed to evaluate the potential non-linear relationship between BMI and the risk of catheter-related thrombosis. As shown in Fig. 1, a significant non-linear association between BMI and thrombosis risk was observed. The risk of thrombosis remained relatively stable at lower BMI levels but increased progressively when BMI exceeded approximately 23 kg/m². This pattern indicates a dose–response relationship, with higher BMI associated with an increased risk of thrombosis.
Fig. 1.
Restricted cubic spline(RCS) analysis demonstrating the non-linear association between body mass index (BMI) and the risk of port-related thrombosis. The solid line represents the estimated odds ratio (OR), and the shaded area indicates the 95% confidence interval (CI)
Baseline characteristics
A total of 1,247 patients were included in the analysis and stratified into two groups according to BMI (≤ 23 vs. > 23 kg/m²), comprising 591 (47.39%) and 656 (52.61%) patients, respectively (Table 1). Compared with patients in the BMI ≤ 23 kg/m² group, those with BMI > 23 kg/m² had a significantly higher prevalence of hypertension (9.30% vs. 4.74%, P = 0.002). Significant differences between the two groups were also observed in catheter tip position (P < 0.001), T stage (P = 0.002), and the rate of catheter adjustment (2.44% vs. 0.85%, P = 0.029). Notably, the incidence of port-associated thrombosis was significantly higher in patients with BMI > 23 kg/m² than in those with BMI ≤ 23 kg/m² (9.30% vs. 5.58%, P = 0.013). No statistically significant differences were observed between the two groups in age, platelet count, catheter insertion side, N stage, M stage, hormone receptor status, HER2 status, or endocrine therapy (all P > 0.05).
Table 1.
Baseline clinicopathological characteristics of patients stratified by BMI
| Variables | Total (n = 1247) | BMI ≤ 23 (n = 591) |
BMI>23 (n = 656) | P value |
|---|---|---|---|---|
| Age, n(%) | 0.256 | |||
| <50 y | 671 (53.81) | 328 (55.50) | 343 (52.29) | |
| ≥50 y | 576 (46.19) | 263 (44.50) | 313 (47.71) | |
| Hypertension, n(%) | 0.002 | |||
| No | 1158 (92.86) | 563 (95.26) | 595 (90.70) | |
| Yes | 89 (7.14) | 28 (4.74) | 61 (9.30) | |
| Platelet count, n(%) | 0.445 | |||
| >350 × 10⁹/L | 169 (13.55) | 74 (12.52) | 95 (14.48) | |
| <125 × 10⁹/L | 11 (0.88) | 4 (0.68) | 7 (1.07) | |
| 125–350 × 10⁹/L | 1067 (85.57) | 513 (86.80) | 554 (84.45) | |
| Catheter insertion side, n(%) | 0.512 | |||
| Left | 603 (48.36) | 280 (47.38) | 323 (49.24) | |
| Right | 644 (51.64) | 311 (52.62) | 333 (50.76) | |
| Catheter tip position, n(%) | < 0.001 | |||
| T4 | 42 (3.37) | 10 (1.69) | 32 (4.88) | |
| T5 | 207 (16.60) | 75 (12.69) | 132 (20.12) | |
| T6 | 506 (40.58) | 245 (41.46) | 261 (39.79) | |
| T7 | 428 (34.32) | 220 (37.23) | 208 (31.71) | |
| T8 | 64 (5.13) | 41 (6.94) | 23 (3.51) | |
| T stage, n(%) | 0.002 | |||
| 1 | 384 (30.79) | 209 (35.36) | 175 (26.68) | |
| 2 | 696 (55.81) | 301 (50.93) | 395 (60.21) | |
| 3 | 107 (8.58) | 47 (7.95) | 60 (9.15) | |
| 4 | 60 (4.81) | 34 (5.75) | 26 (3.96) | |
| N stage, n(%) | 0.334 | |||
| 0 | 509 (40.82) | 254 (42.98) | 255 (38.87) | |
| 1 | 452 (36.25) | 213 (36.04) | 239 (36.43) | |
| 2 | 187 (15.00) | 83 (14.04) | 104 (15.85) | |
| 3 | 99 (7.94) | 41 (6.94) | 58 (8.84) | |
| M stage, n(%) | 0.951 | |||
| 0 | 1180 (94.63) | 559 (94.59) | 621 (94.66) | |
| 1 | 67 (5.37) | 32 (5.41) | 35 (5.34) | |
| Hormone receptor status, n(%) | 0.964 | |||
| Negative | 398 (31.92) | 189 (31.98) | 209 (31.86) | |
| Positive | 849 (68.08) | 402 (68.02) | 447 (68.14) | |
| HER2 status, n(%) | 0.101 | |||
| Negative | 789 (63.27) | 360 (60.91) | 429 (65.40) | |
| Positive | 458 (36.73) | 231 (39.09) | 227 (34.60) | |
| Endocrine therapy, n(%) | 0.306 | |||
| No | 504 (40.42) | 230 (38.92) | 274 (41.77) | |
| Yes | 743 (59.58) | 361 (61.08) | 382 (58.23) | |
| Catheter adjustment, n(%) | 0.029 | |||
| No | 1226 (98.32) | 586 (99.15) | 640 (97.56) | |
| Yes | 21 (1.68) | 5 (0.85) | 16 (2.44) | |
| Port-associated thrombosis, n(%) | 0.013 | |||
| No | 1153 (92.46) | 558 (94.42) | 595 (90.70) | |
| Yes | 94 (7.54) | 33 (5.58) | 61 (9.30) |
Abbreviations: BMI body mass index, HER2 human epidermal growth factor receptor 2
Risk factors for thrombosis
Univariate and multivariate logistic regression analyses were conducted to identify factors associated with port-related thrombosis (Table 2). In the univariate analysis, BMI > 23 kg/m² was significantly associated with an increased risk of thrombosis (OR = 1.73, 95% CI: 1.12–2.69, P = 0.014). After adjustment for potential confounders, BMI > 23 kg/m² remained independently associated with a higher risk of thrombosis (OR = 1.58, 95% CI: 1.02–2.47, P = 0.042). Hypertension was also identified as an independent risk factor for thrombosis (OR = 2.53, 95% CI: 1.37–4.66, P = 0.003). In contrast, HER2-positive status was associated with a reduced risk of thrombosis (OR = 0.58, 95% CI: 0.36–0.93, P = 0.024). Age ≥ 50 years showed a trend toward an increased risk of thrombosis; however, this association did not reach statistical significance in the multivariate analysis (P = 0.075). Other variables, including platelet count, catheter insertion side, catheter tip position, tumor stage, and endocrine therapy, were not significantly associated with thrombosis risk.
Table 2.
Univariate and multivariate logistic regression analysis for port-related thrombosis
| Variables | Univariate OR (95% CI) |
P value | Multivariate OR (95% CI) |
P value |
|---|---|---|---|---|
| BMI | ||||
| ≤23 | 1.00 (Reference) | 1.00 (Reference) | ||
| >23 | 1.73 (1.12 ~ 2.69) | 0.014 | 1.58 (1.02 ~ 2.47) | 0.042 |
| Age | ||||
| <50 y | 1.00 (Reference) | 1.00 (Reference) | ||
| ≥50 y | 1.63 (1.07 ~ 2.50) | 0.024 | 1.49 (0.96 ~ 2.33) | 0.075 |
| Hypertension | ||||
| No | 1.00 (Reference) | 1.00 (Reference) | ||
| Yes | 3.03 (1.69 ~ 5.46) | < 0.001 | 2.53 (1.37 ~ 4.66) | 0.003 |
| Platelet count | ||||
| >350 × 10⁹/L | 1.00 (Reference) | |||
| <125 × 10⁹/L | 3.95 (0.74 ~ 21.05) | 0.107 | ||
| 125–350 × 10⁹/L | 1.50 (0.74 ~ 3.04) | 0.262 | ||
| Catheter insertion side | ||||
| Left | 1.00 (Reference) | |||
| Right | 1.23 (0.80 ~ 1.88) | 0.340 | ||
| Catheter tip position | ||||
| T4 | 1.00 (Reference) | |||
| T5 | 2.38 (0.54 ~ 10.52) | 0.254 | ||
| T6 | 1.49 (0.34 ~ 6.41) | 0.595 | ||
| T7 | 1.56 (0.36 ~ 6.77) | 0.551 | ||
| T8 | 1.33 (0.23 ~ 7.63) | 0.746 | ||
| T stage | ||||
| 1 | 1.00 (Reference) | |||
| 2 | 1.20 (0.74 ~ 1.95) | 0.449 | ||
| 3 | 0.67 (0.25 ~ 1.80) | 0.433 | ||
| 4 | 1.82 (0.75 ~ 4.40) | 0.184 | ||
| N stage | ||||
| 0 | 1.00 (Reference) | |||
| 1 | 1.54 (0.95 ~ 2.50) | 0.082 | ||
| 2 | 1.44 (0.77 ~ 2.70) | 0.253 | ||
| 3 | 0.99 (0.40 ~ 2.45) | 0.991 | ||
| M stage | ||||
| 0 | 1.00 (Reference) | |||
| 1 | 0.77 (0.27 ~ 2.16) | 0.618 | ||
| Hormone receptor status | ||||
| Negative | 1.00 (Reference) | |||
| Positive | 1.18 (0.74 ~ 1.87) | 0.490 | ||
| HER2 status | ||||
| Negative | 1.00 (Reference) | 1.00 (Reference) | ||
| Positive | 0.60 (0.38 ~ 0.97) | 0.036 | 0.58 (0.36 ~ 0.93) | 0.024 |
| Endocrine therapy | ||||
| No | 1.00 (Reference) | |||
| Yes | 0.95 (0.62 ~ 1.46) | 0.826 | ||
| Catheter adjustment | ||||
| No | 1.00 (Reference) | |||
| Yes | 0.61 (0.08 ~ 4.59) | 0.630 | ||
Abbreviations: BMI, body mass index, HER2 human epidermal growth factor receptor 2
Analysis of Thrombosis-Free Survival
Kaplan–Meier survival analysis was performed to compare thrombosis-free survival between the two BMI groups (Fig. 2). Thrombosis-free survival decreased progressively over time in both groups. Patients with BMI > 23 kg/m² consistently demonstrated a lower probability of thrombosis-free survival compared with those with BMI ≤ 23 kg/m² (HR = 0.570, 95% CI: 0.373–0.871, P = 0.008). The divergence between the curves was evident in the early phase of follow-up and increased over time, indicating a persistent difference in thrombotic risk.
Fig. 2.
Kaplan–Meier curves for thrombosis-free survival according to BMI categories (≤ 23 vs. > 23 kg/m²)
Discussion
This study was designed to investigate the impact of BMI on the risk of port-related thrombosis in patients with breast cancer, with a particular focus on the dose–response relationship and clinically relevant thresholds. Our findings demonstrate a significant non-linear association between BMI and thrombosis risk, as identified through continuous RCS analysis. Although patients with BMI > 23 kg/m² exhibited an increased risk of thrombosis after adjustment for confounders, this threshold should be interpreted as exploratory and primarily intended to facilitate clinical interpretation rather than define a definitive risk cutoff. Restricted cubic spline analysis further revealed a clear non-linear relationship, and survival analysis confirmed that patients with higher BMI exhibited persistently lower thrombosis-free survival over time, indicating a sustained and cumulative effect. These results underscore the clinical relevance of BMI as a simple and actionable risk stratifier for thrombotic complications in oncologic vascular access, particularly in populations with relatively lower BMI distributions.
Our observation that elevated BMI (> 23 kg/m²) increases the risk of catheter-related thrombosis aligns with existing literature. A systematic review of cancer patients with peripherally inserted central catheters (PICCs) reported that overweight and obese individuals (BMI ≥ 25 kg/m²) had approximately a twofold higher risk of thrombosis compared with normal or underweight patients (RR ≈ 2.06) [3]. Prospective PICC cohorts have identified obesity and reduced physical activity as significant predictors of thrombosis, supporting the prothrombotic milieu associated with higher BMI and related metabolic dysregulation, such as hypercoagulability reflected in elevated D-dimer levels and altered lipid profiles [7]. Conversely, a recent prospective study highlighted the interplay between BMI and vascular morphology, where hemodynamic parameters, including vessel diameter and blood flow velocity, modulated thrombosis risk in a non-linear manner, potentially accounting for discrepancies across catheter types and populations [8]. Variations in study design, catheter type (PICC versus implantable ports), BMI cutoffs, surveillance methods, and additional risk modifiers such as infection or prior thrombosis underscore the complexity of thrombotic risk beyond BMI alone.
Recent evidence has further emphasized the multifaceted impact of obesity on breast cancer biology and clinical outcomes, supporting the broader relevance of obesity-related systemic alterations in this patient population [9]. Building on this context, the observed association between elevated BMI and port-related thrombosis may be partly explained by several interconnected biological mechanisms linking obesity, systemic inflammation, and hypercoagulability. First, obesity induces a chronic low-grade inflammatory state driven by adipose-derived cytokines such as interleukin-6 and tumor necrosis factor-α, which promote endothelial activation and upregulate adhesion molecules and tissue factor, facilitating thrombus formation [10, 11]. Second, excess adiposity is linked to a hypercoagulable state, evidenced by increased circulating procoagulant factors, including fibrinogen, factor VII, and plasminogen activator inhibitor-1, alongside impaired fibrinolytic activity [12, 13]. Third, obesity-related endothelial dysfunction further enhances prothrombotic potential by reducing nitric oxide bioavailability and promoting platelet activation and aggregation [14, 15]. In the context of cancer, these effects may be amplified by tumor-associated prothrombotic pathways, including release of cancer-derived microparticles and coagulation cascade activation, while an indwelling catheter provides a local trigger through vascular injury and blood flow disruption [16, 17]. Collectively, these mechanisms provide biological plausibility for the observed non-linear increase in thrombosis risk beyond a critical BMI threshold, indicating that the prothrombotic impact of adiposity becomes more pronounced once metabolic dysregulation reaches a certain level.
In the present study, HER2-positive status was associated with a lower risk of port-related thrombosis; however, this finding should be interpreted cautiously. Although breast cancer is generally associated with a prothrombotic state, the influence of molecular subtype on thrombosis risk remains incompletely understood. Given the retrospective and observational nature of this study, the observed association should not be interpreted as evidence of a direct biological protective effect of HER2 positivity per se. One plausible explanation is that differences in systemic therapy exposure across molecular subtypes may partially contribute to the observed finding. HER2-positive patients routinely receive HER2-targeted therapies, such as trastuzumab, which substantially improve treatment response and reduce tumor burden. These treatment-related effects may indirectly influence thrombotic risk by attenuating cancer-associated hypercoagulability or systemic inflammation. In contrast, therapies commonly used in other molecular subtypes, such as CDK4/6 inhibitors in HR-positive/HER2-negative disease, have been associated with an increased risk of venous thromboembolism [18], suggesting that treatment-related factors may partly shape thrombotic outcomes across subtypes. In addition, residual confounding from unmeasured clinical and treatment-related variables cannot be excluded. Although prior studies suggest that tumor biology and host inflammatory responses may differ among molecular subtypes and potentially influence thrombotic risk, direct evidence linking HER2 positivity itself to reduced thrombosis risk remains limited [19]. Therefore, further prospective studies incorporating detailed treatment exposure and biological data are warranted to validate this association and clarify the underlying mechanisms.
Despite its strengths, several limitations should be acknowledged. First, this was a single-center retrospective study with a relatively limited sample size, which may restrict the generalizability of the findings. Baseline characteristics differed across BMI groups, raising the possibility of selection bias and confounding by indication. Although multivariable regression models were applied to adjust for key covariates, including age, BMI, port characteristics, and endocrine therapy, residual confounding cannot be fully excluded, particularly given the absence of detailed information on several clinically relevant variables, such as chemotherapy regimens, anti-HER2 therapy, surgical duration, thromboprophylaxis strategies, catheter dwell time, and prior catheter-related complications. Second, heterogeneity in systemic treatment across molecular subtypes was not fully captured, which may have introduced additional unmeasured confounding, particularly in analyses examining the association between HER2 status and thrombosis risk. Finally, although advanced multivariable adjustment strategies were used to mitigate confounding and explore potential non-linear effects, the retrospective design inherently limits causal inference. Therefore, the observed associations, particularly those related to BMI and thrombosis risk, should be interpreted cautiously and require validation in well-designed prospective studies incorporating more granular treatment and procedural data.
Conclusion
Elevated BMI is independently associated with increased port-related thrombosis risk in breast cancer patients, with a non-linear dose–response relationship and an exploratory threshold at approximately 23 kg/m² that may aid clinical interpretation. BMI may thus serve as a practical marker for risk stratification in patients undergoing TIVAP implantation, and patients with higher BMI could benefit from closer monitoring and potentially tailored thromboprophylaxis. Future prospective, multicenter studies with standardized thrombosis surveillance and inclusion of clinical and laboratory biomarkers, such as D-dimer, coagulation factors, and adipocytokines, are warranted to validate these findings and elucidate underlying mechanisms. Integration of refined anthropometric measures, vascular characteristics, and advanced predictive modeling could further inform individualized thromboprophylaxis and evidence-based vascular access management in oncology practice.
Abbreviations
- BMI
Body mass index
- TIVAP
Totally implantable venous access port
- RCS
Restricted cubic spline
- HER2
Human epidermal growth factor receptor 2
- PICC
Peripherally inserted central catheter
- OR
Odds ratio
- CI
Confidence interval
Authors' contributions
WL, XD and YC contributed to the study conception and design, YC, TL and XL conducted data collection, analysis of data was contributed by YC, DW and LL, YC and DW prepared all the figures and tables, YC drafted the manuscript, All authors discussed and edited the paper. All authors read and approved the manuscript.
Funding
This study was supported by the Joint Funds for the innovation of science and Technology, Fujian province (Grant No. 2025Y9500).
Data availability
The data supporting all tables and figures in this published article are not publicly available to protect patient privacy but can be accessed from the corresponding author（Weibin Lian）on request.
Declarations
Ethics approval and consent to participate
This retrospective study was approved by the Ethics Committee of of Quanzhou First Hospital (Approval No.: 2026K167) and was conducted in accordance with the principles of the Declaration of Helsinki. Due to the retrospective design and the use of anonymized clinical data, the requirement for informed consent was waived by the Ethics Committee.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yanzhen Chen, Duanduan Wang and Liangqiang Li contributed equally to this work.
Contributor Information
Xianqiang Du, Email: dxq20140528@163.com.
Weibin Lian, Email: dr_weibinlian@fjmu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting all tables and figures in this published article are not publicly available to protect patient privacy but can be accessed from the corresponding author（Weibin Lian）on request.