Gynecological cancers, including ovarian, cervical, and endometrial cancers, represent a major global health concern for women globally. Chemotherapy is a widely used approach for treating these cancers and often requires reliable long-term venous access. Totally implantable venous access ports (TIVAPs) have provided an effective and safe option for patients requiring prolonged chemotherapy [1, 2]. TIVAPs offer significant clinical advantages such as lower infection risk, convenient maintenance, improved quality of life, and long usage duration [3, 4].

In recent years, with advances in medical technology and increasing emphasis on quality of life, arm ports (APs) have been increasingly adopted as a novel implantation approach. Compared with traditional chest wall ports, APs are considered a safer alternative that exerts lower negative impact on quality of life in patients undergoing intravenous chemotherapy [5, 6]. However, postoperative complications associated with APs remain a significant clinical concern. Frequently reported complications include infection, catheter-related thrombosis, catheter malposition, and catheter occlusion [7, 8, 9, 10, 11]. These adverse events not only increase patient suffering and financial burden but can also disrupt antitumor therapy, potentially affecting disease prognosis.

Current studies have reported several risk factors for complications associated with venous access ports; research evidence specifically emphasizing gynecological oncology cohorts, particularly regarding risk prediction models for AP-related complications, remains limited [12, 13]. This highlights a critical gap: there are no population-specific, easy-to-use predictive models for AP complications in gynecological oncology. The advances in precision medicine necessitate more individualized and nuanced clinical decision-making. Establishing a dedicated risk prediction model for AP complications in gynecological oncology could support preoperative identification of high-risk individuals, guide targeted preventive interventions, optimize healthcare resource utilization, and ultimately improve quality of care. The nomograms, as intuitive risk prediction tools, convert complex regression equations into simpler graphical scoring systems, thereby assisting clinicians in rapid risk stratification and enabling efficient doctor-patient communication [14].

Therefore, this study retrospectively evaluated clinical data from gynecological oncology patients who received APs at a single center, with the objective of systematically investigating independent risk factors for AP-related complications. Based on the findings, a population-specific risk prediction model was established, and its predictive performance and clinical utility were assessed through internal validation. The ultimate goal is to provide evidence-based guidance to support individualized management approaches and enhance complication prevention in gynecological oncology patients using APs.

This retrospective analysis of 476 gynecological oncology patients with APs constructed a risk prediction model, specifically targeting postoperative complications following AP implantation in this patient cohort. Preoperative NLR, history of ipsilateral arm surgery, and planned chemotherapy cycles were identified as independent risk predictors of complications. These variables were incorporated into an intuitive nomogram, enabling clinicians to conduct rapid, individualized risk assessment. The model demonstrated superior predictive performance and clinical utility, providing a valuable tool to guide tailored approaches for complication prevention and management.

The NLR is a reliable marker of systemic inflammatory response and has been widely validated as a prognostic predictor for various clinical outcomes in cancer patients [15, 16, 17, 18]. This study is the first to indicate a specific association between NLR and AP-related complications, with a significant odds ratio (OR = 1.53), suggesting that managing preoperative inflammatory status may contribute to reducing complication risk. Mechanistically, NLR indicates more than a simple risk indicator and is closely associated with the “inflammation-coagulation-immunity” axis. Elevated NLR, indicative of an inflammatory state, promotes the release of neutrophil extracellular traps (NETs), upregulates the expression of tissue factors, and enhances platelet activation, collectively enhancing a potent prothrombotic environment that significantly increases the risk of catheter-related thrombosis [19, 20]. On the other hand, persistent inflammation, mediated by pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-$\alpha$) and interleukin-6 (IL-6), disrupts fibroblast activity and collagen deposition, delaying wound healing at the incision site and subcutaneous pocket. Concurrently, chronic inflammatory signaling leads to lymphocyte exhaustion and apoptosis, compromising cellular immune response and facilitating microbial colonization and subsequent infection [21, 22]. This implies that careful evaluation and modulation of preoperative inflammatory status may play a crucial role in reducing AP-related complications.

A history of ipsilateral surgery was identified as the strongest predictor of complications (OR = 5.02), and its impact is driven by multiple mechanisms. Disruption of the local lymphatic network during previous surgery can lead to chronic lymphatic stasis and interstitial fluid accumulation [23]. Additionally, surgical scarring compromises the local microvascular architecture and reduces tissue perfusion, which impairs oxygen and nutrient delivery, severely affects tissue repair capacity, and is directly linked to increased infection risk [24, 25, 26]. Furthermore, the chronically hypoxic microenvironment within scar tissue can further exacerbate local inflammation through upregulation of factors such as hypoxia-inducible factor 1-alpha (HIF-1$\alpha$) [27, 28]. Thus, a history of ipsilateral surgery creates a “vulnerable” environment with anatomical, physiological, and immune defense deficiency, significantly increasing susceptibility to AP-related complications. This finding highlights the significance of carefully evaluating previous history and considering contralateral arm implantation when appropriate.

The positive association between the planned number of chemotherapy cycles and complication risk (OR = 1.52) primarily reflects the cumulative effect of treatment intensity and extended catheter exposure. Mechanistically, each chemotherapy infusion imposes chemical and mechanical injury on the vascular endothelium. Recurrent endothelial damage and repair over time promote a prothrombotic state and increase the risk of catheter-related thrombosis [29]. Simultaneously, frequent catheter access increases the cumulative probability of micro-damage to the catheter lumen and facilitates bacterial biofilm formation. Once established, biofilms can shield pathogens from host immune responses and antimicrobial agents, leading to persistent and difficult-to-treat infections [30, 31, 32].

The proposed prediction model offers a practical tool for individualized AP management in gynecological oncology. For example, a patient with an NLR of 4.5 (approximately 40 points), a history of ipsilateral arm surgery (approximately 50 points), and 6 planned chemotherapy cycles (approximately 60 points) would yield a total score of about 150 points, corresponding to a complication risk of about 50%. For such high-risk patients, intensified monitoring and consideration of prophylactic interventions, such as anticoagulation, may be needed. The model demonstrated robust predictive performance, consistent with contemporary prediction models for complications of totally implantable venous access ports in adult oncology cohorts, which typically report validation AUCs around 0.80. For instance, a recent nomogram for predicting port-related infections in adult tumor patients reported an AUC of 0.835 [33]. These findings support clinical application while leaving room for future refinement.

Incorporating this model into preoperative assessment can help identify high-risk patients and guide targeted preventive approaches, including enhanced blood glucose management, optimized surgical planning, and individualized anticoagulation strategies. For patients stratified as very high-risk, alternative venous access options or more intensified postoperative monitoring may be considered. The model can also guide patient counseling by elucidating individual risk profiles and emphasizing preventive behaviors, which include adherence and management. Furthermore, the model provides a standardized tool for risk stratification in future clinical investigations, facilitating patient selection, subgroup analysis, and comparison of outcomes across different interventions.

This study has several limitations. First, as a single-center retrospective study, it is subject to potential selection bias related to the characteristics of the specific patient population and the clinical practices at our center, which may limit the generalizability of these findings to other healthcare settings with different demographics or treatment protocols. Second, despite the overall sample size, the number of patients was still limited, and it remained insufficient for detailed subgroup analyses of less frequent complication types. Third, the model has not undergone external validation, and its performance with broader or more diverse populations remains to be established.

Another key limitation is the use of a composite endpoint that combines complications with potentially different pathophysiological mechanisms. While this approach improves statistical power, it may mask specific risk factors that are specific to individual complication subtypes. Future investigations should focus on multi-center prospective designs for external validation, assessment of whether incorporating novel biomarkers, such as inflammatory cytokines and coagulation parameters, can enhance predictive performance, and the development of dynamic, potentially artificial intelligence-based prediction tools to enable real-time risk assessment and early detection of complications.

Overall, this study developed a prediction model specifically designed to estimate the risk of postoperative complications following AP implantation in patients with gynecological oncology. The model demonstrates good predictive performance and clinical utility, supporting preoperative identification of high-risk individuals and guided targeted preventive approaches. This model has the potential to lower complication rate, improve patient outcomes, and contribute to more effective clinical management.

• This study developed a nomogram for the first time to predict arm port complications in gynecological oncology patients.

• Preoperative NLR, history of ipsilateral arm surgery, and planned chemotherapy cycles were identified as independent risk factors.

• The model demonstrates good discriminative performance and calibration in both the training and validation sets.

• Decision curve analysis confirmed the clinical utility of the model across a moderate range of threshold probabilities.

• This tool may aid in preoperative risk stratification and guide personalized preventive strategies for high-risk patients.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

YLS, JH, JZ and FZ designed the research study. QY, YYQ and NZ performed the research. YLS and JZ analyzed the data. JZ drafted the manuscript. All authors contributed to the important editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The study was approved by the Ethics Committee of The Fourth Hospital of Hebei Medical University (approval number: 2023KS164). Written informed consent was obtained from all participants, and all procedures were conducted in accordance with the principles outlined in the Declaration of Helsinki.

Not applicable.

This research was supported by Hebei Province Medical Science Research Project Plan, Grant No. 20230830.

The authors declare no conflicts of interest.