Peritoneal dialysis (PD) has become a widely adopted renal replacement therapy for patients with end-stage renal disease (ESRD), valued for its ability to preserve residual kidney function, offer flexible treatment modalities, and support home-based care. However, as PD duration and global prevalence rise, PD-related complications have emerged as critical factors affecting patient outcomes. Among these, abdominal wall hernia stands out as a frequent and clinically significant mechanical complication, with reported incidence rates ranging from 10% to 25% and up to 30% in long-term PD populations (1–3). Epidemiological studies indicate that patients who develop abdominal wall hernia face a twofold increase in hospitalization risk and a 1.5-fold higher mortality rate compared to those without herniation. The presence of hernia not only causes physical discomfort and functional decline but also disrupts dialysis efficiency, potentially leading to life-threatening complications such as bowel incarceration, intestinal obstruction, and necrosis-often necessitating surgical intervention. Despite growing awareness, current risk stratification tools remain insufficiently precise for individualized prediction. Therefore, there is a pressing clinical need for a validated, accurate predictive model to identify high-risk patients early and guide preventive strategies. This study addresses that gap by developing and externally validating a novel nomogram specifically tailored to predict abdominal wall hernia risk in PD patients, with the potential to improve clinical decision-making and patient outcomes.

Numerous studies have identified potential risk factors associated with the occurrence of abdominal wall hernia in PD patients, including prolonged duration of PD, advanced age, hypoalbuminemia, obesity, and specific dialysis modalities such as continuous ambulatory peritoneal dialysis (CAPD) (4–6). Despite these findings, the current understanding remains fragmented, and existing models for predicting the risk of abdominal wall hernia in this population are limited by small sample sizes, single-center designs, and a lack of external validation. Moreover, most of these models lack clinical applicability due to insufficient discrimination and calibration performance, which hampers their utility in guiding individualized risk assessment and preventive strategies.

In this context, the development of a robust, externally validated, and clinically applicable predictive tool is urgently needed to facilitate early identification of high-risk patients and to support targeted preventive interventions. Nomograms, as graphical predictive models that integrate multiple prognostic factors, have demonstrated considerable value in various clinical settings by enabling individualized risk stratification and improving clinical decision-making. The present study aims to develop and externally validate a nomogram for predicting the risk of abdominal wall hernia in PD patients based on multicenter retrospective data. By identifying independent predictors and rigorously validating the model’s performance, this tool holds promise for enhancing risk stratification, guiding timely interventions, and ultimately improving clinical outcomes in this vulnerable patient population.

Peritoneal dialysis is a vital therapeutic option for patients with end-stage renal disease. However, it is often complicated by the development of abdominal wall hernias, which not only increase hospitalization rates and surgical risks but can also compromise dialysis efficacy and long-term patient outcomes. Epidemiological studies indicate that the incidence of abdominal wall hernias among peritoneal dialysis patients ranges from 10% to 30%, frequently accompanied by high recurrence rates and significant economic burdens on healthcare systems. Despite the clinical significance of this complication, there remains a notable lack of robust, individualized risk prediction tools. The absence of such tools hinders the early identification of high-risk patients and the timely implementation of preventive measures, thereby limiting effective management.

To address this unmet clinical need, we developed the first clinically derived nomogram to predict the risk of abdominal wall hernia in patients undergoing peritoneal dialysis. The model demonstrated good discriminative performance, with AUCs of 0.823 and 0.747 in the training and external validation cohorts, respectively. However, the calibration curve (Figure 4) indicated a narrow range of predicted probabilities, suggesting limited variability in risk scores. This may stem from the relatively homogeneous distribution of key variables–such as age, dialysis modality, and serum albumin–within the study population, thereby constraining the model’s predictive spectrum.

The nomogram integrates multiple objective clinical variables, offering improved risk stratification over conventional single-variable approaches. Its consistent performance across independent validation cohorts supports its generalizability and potential as a clinical decision-support tool. These findings highlight the feasibility of individualized hernia risk prediction in peritoneal dialysis and lay the groundwork for future prospective validation.

Regarding the model’s development, we acknowledge that certain high-risk groups–such as those with prior abdominal surgery, connective tissue disorders, or major comorbidities–were excluded based on predefined inclusion and exclusion criteria. These criteria were established to minimize confounding effects from surgical history and to ensure a more homogeneous baseline population for model derivation. Nevertheless, the exclusion of these subgroups may limit the model’s applicability in more diverse or complex clinical scenarios. Future external validation in broader patient populations will be critical to assess the model’s robustness and generalizability across heterogeneous clinical settings.

An independent selection of risk factors was conducted using the logistic proportional hazards model, and the model’s performance was rigorously assessed through comprehensive metrics, including AUC, calibration curves, and decision curve analysis (DCA), thereby ensuring robust statistical validity. Moreover, external validation incorporating multicenter data further enhanced the model’s clinical applicability and generalizability. This study design represents a substantial improvement over previous investigations, which were often limited by small sample sizes and single-center data, thus providing a more solid evidence base to inform future clinical practice.

Existing studies have indicated that advancing age and hypoalbuminemia may represent potential risk factors for abdominal wall hernia (1, 2). Building upon this foundation, the present study further validates the weight of these factors within predictive models and incorporates PDV, establishing its role as an independent risk factor. From a physiological perspective, with increasing age, there is a decline in muscle mass and collagen synthesis capacity, leading to diminished tissue repair ability, compromised integrity of the abdominal wall structure, and consequently, an elevated risk of hernia development (8). Concurrently, an exacerbated chronic inflammatory state may impair tissue healing responses and promote the formation of weakened areas in the abdominal wall (9). On the other hand, hypoalbuminemia is frequently utilized as a biomarker for malnutrition and systemic inflammation; its reduction can suppress collagen synthesis, weaken the mechanical strength of connective tissues, and impair the wound healing process, thereby increasing the likelihood of abdominal wall hernia occurrence (10–12). Furthermore, chronic inflammation is prevalent among patients with end-stage renal disease, and through inducing muscle atrophy and protein catabolism, it exacerbates malnutrition and further compromises tissue repair capacity (13, 14). Maintaining optimal nutritional status, particularly by correcting hypoalbuminemia, may represent one important strategy in the prevention of abdominal wall hernias.

Notably, this study identifies continuous ambulatory peritoneal dialysis (CAPD) as a potential risk factor for the development of abdominal wall hernias when compared to automated peritoneal dialysis (APD). The observed association appears to stem from the intrinsic operational features of CAPD, particularly its characteristic prolonged dwell times and sustained intra-abdominal pressure (IAP) elevation. From a biomechanical standpoint, the persistent mechanical stress imposed during CAPD may induce chronic tissue strain that exceeds the adaptive capacity of the abdominal wall’s viscoelastic properties. This sustained pressure, compounded by repetitive manual exchanges that introduce fluctuating shear forces, may progressively compromise myofascial integrity and accelerate collagen matrix degradation (15, 16). Additionally, the absence of nocturnal decompression periods in CAPD may restrict opportunities for tissue recovery, thereby contributing to cumulative structural fatigue over time. These findings suggest that while CAPD may be associated with increased hernia risk, it likely acts in conjunction with other clinical and mechanical factors rather than as an independent predictor.

The repetitive pressurization of the peritoneal cavity inherent to CAPD may further impair connective tissue remodeling and hinder myofascial repair mechanisms (17). Increased dwell frequency, larger dialysate volumes, and continuous abdominal wall tension may interact synergistically to erode the mechanical stability of the anterior abdominal wall over the course of long-term dialysis. Although this study did not specifically assess peritoneal transport characteristics, previous evidence suggests that high transporter status-often managed with intensified exchange protocols-may amplify these biomechanical stresses, potentially potentiating tissue injury through a combination of chronic mechanical strain and inflammation-mediated extracellular matrix disruption (18). These complex interactions highlight the multifactorial nature of hernia development in peritoneal dialysis patients and caution against the interpretation of CAPD as an isolated causative factor.

Compared with previous studies, this study demonstrates notable advancements in both the integrity and clinical applicability of the model construction. Xu et al. (1) conducted a systematic review and meta-analysis, identifying hypoalbuminemia, advanced age, diabetes, and obesity as key risk factors for abdominal wall hernia in patients undergoing peritoneal dialysis; Chen et al. (2) further corroborated hypoalbuminemia and advanced age as independent predictors. Importantly, this study introduces peritoneal dialysis modality as a dynamic variable and develops a visually accessible nomogram for individualized risk assessment, thereby enhancing the feasibility and precision of clinical decision-making. Furthermore, this model allows for the incorporation of potential interventions, such as nutritional support aimed at correcting hypoalbuminemia-a modifiable risk factor that may significantly influence hernia development. Targeted dietary interventions or supplementation strategies could potentially mitigate the risk of herniation by improving serum albumin levels and enhancing abdominal wall integrity.

This work not only addresses a gap in the existing literature but also offers a practical tool to guide preventive and therapeutic strategies in clinical practice. In terms of clinical application, the nomogram developed in this study can be used for early risk screening in outpatient or inpatient settings, helping to identify high-risk patients and formulate targeted intervention strategies, such as enhancing nutritional support, optimizing dialysis regimens, avoiding excessive intra-abdominal pressure, and implementing prophylactic surgical reinforcement when necessary. This proactive management strategy holds promise for reducing the incidence of abdominal wall hernias and associated complications, thereby improving patients’ quality of life and prognosis (19–21).

Certainly, this study has several limitations that warrant discussion. First, the retrospective observational design inherently limits the ability to establish causal relationships, a common challenge in studies of this nature (22). Second, although our cohort included patients from multiple centers, the sample size remains relatively modest compared to large-scale epidemiological studies, and the inclusion criteria may introduce potential selection bias (23). Furthermore, several potentially important confounding factors, such as physical activity level, smoking history, and genetic predisposition, were not systematically collected or analyzed, which could influence hernia development and thus affect the generalizability of the model. In addition, key clinical parameters related to peritoneal function, such as peritoneal transport type, were not integrated into the current framework, limiting deeper mechanistic insights into the impact of dialysis modality on abdominal wall integrity.

To address these gaps, future research should incorporate larger, prospective multicenter cohorts with standardized data collection protocols, dynamic clinical monitoring, advanced imaging modalities, and novel biomarkers reflective of connective tissue metabolism and systemic inflammation. Such enhancements would not only improve model robustness but also support broader external validation efforts. Ultimately, the integration of radiomic features, machine learning techniques, and multi-omics data may offer a more comprehensive risk stratification tool tailored for clinical decision-making.

Looking ahead, it is recommended to explore an intelligent prediction system integrating radiomics, machine learning algorithms, and multi-omics information to enhance the accuracy and generalization ability of the model. Moreover, other research teams are encouraged to conduct external validation in diverse populations and design randomized controlled trials (RCTs) based on the risk stratification of the present model to evaluate its practical effectiveness in preventive interventions. This will provide a solid foundation for achieving precision medicine and individualized intervention.

In conclusion, the nomogram model established in this study offers a new tool for early warning of abdominal wall hernia in patients undergoing peritoneal dialysis, demonstrating promising clinical application potential and warranting broader implementation and further validation.