Postoperative gastrointestinal dysfunction and gastrointestinal tumors: A review and a point of view

Abstract

Gastrointestinal (GI) dysfunction following surgery for GI tumors is a common complication that significantly impacts patient recovery, prolongs hospital stays, and increases healthcare burdens. This review of recent research systematically examines the risk factors, predictive model development, and clinical prognostic value associated with this postoperative condition. We delve into key risk determinants including patient age, tumor stage, operation duration, and nutritional indices. We focus on the clinical significance of translating complex regression models into simplified, practical risk indices for bedside application. The review also addresses current limitations in the field, particularly concerning study follow-up duration and the need for robust external validation of existing models. Finally, we provide a forward-looking perspective on future research directions, emphasizing the necessity for model refinement, deeper exploration of underlying pathophysiological mechanisms, and the integration of predictive tools into comprehensive prevention and management strategies.

Key Words: Postoperative gastrointestinal dysfunction; Gastrointestinal cancer; Risk index; Predictive model; Prognostic value; Risk factors

Core Tip: Wang et al’s study described their construction of a risk prediction model that includes a systemic risk index prognostic value for postoperative gastrointestinal (GI) dysfunction in patients whose GI cancer will be treated surgically. Moreover, emphasis is placed on the clinical significance of transforming complex regression models into streamlined, pragmatic risk indices for bedside utilization. A forward-looking perspective on future research directions is provided, with emphasis placed on the necessity for model refinement, a deeper exploration of underlying pathophysiological mechanisms, and the integration of predictive tools into comprehensive prevention and management strategies.

INTRODUCTION

Postoperative gastrointestinal dysfunction (PGID) represents a frequent and clinically significant complication following surgery for gastrointestinal (GI) cancer, profoundly impacting patient recovery trajectories and healthcare resource utilization[1]. Characterized by symptoms such as delayed gastric emptying, ileus, and impaired colonic motility, PGID prolongs hospital stay and increases the risk of secondary complications, including malnutrition, dehydration, and infection, elevating overall morbidity and healthcare costs[2,3]. The clinical importance of PGID extends beyond immediate postoperative care, as it can delay the initiation of adjuvant therapies, such as chemotherapy, potentially compromising long-term oncological outcomes[4]. Despite advances in surgical techniques and perioperative management, the incidence of PGID remains substantial, underscoring the need for a deeper understanding of its epidemiology and underlying mechanisms[5,6]. Epidemiological studies indicate that PGID affects many patients undergoing GI cancer surgery, with rates varying widely depending on the type of resection, patient demographics, and diagnostic criteria[7]. For instance, gastrectomy and colorectal resection are particularly associated with high rates of postoperative ileus and gastroparesis. The variability in incidence highlights the multifactorial nature of PGID, influenced by a complex interplay of patient-specific, surgical, and tumor-related factors[8]. Recognition of the clinical and economic burden of PGID has resulted in a growing emphasis on developing strategies for its prevention and early management, which necessitates reliable tools for risk stratification. However, current clinical practice often relies on generalized risk assessment based on broad categories such as age or tumor stage, which lack the precision required for individualized patient care[9]. This gap in predictive accuracy underscores the urgency for more sophisticated models that integrate a comprehensive array of risk factors to guide targeted interventions. The pathophysiology of PGID is multifaceted, involving a convergence of neural, hormonal, inflammatory, and mechanical disruptions precipitated by surgical trauma and the underlying malignancy[10]. Surgical intervention inherently disrupts the autonomic nervous system, particularly the vagus nerve, which plays a critical role in regulating GI motility. Additionally, the release of pro-inflammatory cytokines and stress hormones in response to surgery can further inhibit GI peristalsis, contributing to the development of ileus. Tumor-related factors, such as local invasion or obstruction, may exacerbate these effects by causing pre-existing neural or vascular compromise[11]. Beyond these immediate mechanisms, patient-specific variables, including age, nutritional status, and comorbidities such as diabetes or neurological disorders, modulate individual susceptibility to PGID[7,12]. For example, elderly patients often exhibit diminished autonomic resilience and baseline motility, making them more vulnerable to postoperative dysmotility. Similarly, advanced tumor stages may be associated with more extensive surgical resection or neoadjuvant treatment, both of which can amplify the risk of dysfunction. The intricate web of these risk factors complicates the prediction of PGID, as linear associations are often inadequate to capture the dynamic interactions at play. This complexity necessitates a shift from isolated factor analysis to integrated modeling approaches that can account for synergistic effects and non-linear relationships[13]. Understanding the pathophysiology is crucial for identifying potential therapeutic targets and informing the selection of variables in predictive models, ensuring they reflect the biological realities of PGID development[14]. In response to the challenges of predicting PGID, researchers have increasingly focused on developing and validating risk prediction models that enhance clinical decision-making[15]. Traditional models relied on demographic and clinical variables, such as age, body mass index (BMI), and surgical duration, but predictive performance was limited by oversimplification and lack of external validation[16]. More recent efforts have used advanced statistical techniques, including machine learning algorithms, to create more robust tools. A notable contribution in this domain is the work by Wang et al[7], who constructed a predictive model incorporating a systemic risk index derived from a combination of inflammatory markers, nutritional parameters, and surgical complexity scores. This model represents a significant advancement by integrating multidimensional data to generate a composite risk score, offering a more nuanced assessment than single-factor approaches. The development of such models typically involves several key steps: Identification of candidate predictors through literature review and clinical expertise; model construction using regression analyses or machine learning; internal validation via bootstrapping or cross-validation; and external validation in independent cohorts to ensure generalizability. Wang et al’s model[7] underwent rigorous validation, demonstrating good discriminative ability and calibration in diverse patient populations. However, the journey from model development to clinical implementation is fraught with challenges, including the need for standardized data collection, model interpretability for clinicians, and integration into existing electronic health record systems. Despite these hurdles, such models have great potential to facilitate early identification of high-risk patients and prompt preventive measures, such as enhanced recovery after surgery (ERAS) or pharmacological prophylaxis. This aligns with the broader trend towards personalized medicine, where tailored interventions are guided by individualized risk profiles. The clinical translation of risk indices, such as the systemic risk index proposed by Wang et al[7], holds profound implications for improving postoperative outcomes in GI cancer patients. By enabling precise risk stratification, these tools empower clinicians to implement proactive, personalized management strategies that can mitigate the onset or severity of PGID. For instance, patients identified as high risk could be prioritized for multimodal analgesia to reduce opioid use-a known contributor to ileus-or for early ambulation and enteral nutrition (EN) protocols. Moreover, risk indices can inform shared decision-making processes, allowing patients and healthcare providers to discuss potential complications and preventive measures preoperatively, thereby enhancing patient engagement and satisfaction. Beyond direct clinical applications, these models also offer value in resource allocation and healthcare planning, as they help identify populations that may require more intensive monitoring or extended hospital stays, optimizing bed utilization, and reducing unnecessary costs. However, the successful integration of risk indices into routine practice demands careful consideration of practical barriers, such as the availability of required biomarkers or computational resources in diverse clinical settings. Additionally, ongoing validation and refinement are essential to maintain model accuracy across different surgical techniques and evolving treatment paradigms, such as minimally invasive surgery or immunotherapy[17]. The dynamic nature of cancer care necessitates that predictive models remain adaptable, incorporating new predictors as our understanding of PGID pathophysiology deepens[18-20]. Ultimately, the goal is to move from reactive management of complications to proactive prevention, fostering a paradigm shift towards precision perioperative care that enhances both short-term recovery and long-term survival for GI cancer patients[21-23]. Despite the progress exemplified by models like that of Wang et al[7], current research on PGID prediction faces several limitations that warrant attention. Many existing studies are retrospective in design, introducing potential biases related to data completeness and selection, which may affect the generalizability of findings[24,25]. Most models have been developed and validated in single-center cohorts, limiting their applicability across diverse healthcare systems with varying patient demographics and surgical practices. The reliance on readily available clinical variables, while pragmatic, may overlook novel biomarkers or genetic factors that could enhance predictive accuracy[26]. For example, emerging evidence suggests that gut microbiome composition or specific genetic polymorphisms may influence susceptibility to postoperative dysmotility, yet these are rarely incorporated into current models. Another critical gap is the lack of focus on patient-reported outcomes and quality of life measures, which are essential for capturing the full impact of PGID beyond clinical endpoints. Future research directions should prioritize prospective, multicenter studies to validate and refine existing models in real-world settings, ensuring robustness and scalability[27-29]. Integration of multi-omics data, such as genomics, proteomics, and metabolomics, could unlock new dimensions of risk stratification, paving the way for more comprehensive predictive tools. Collaboration between clinicians, data scientists, and bioinformaticians is key to harnessing big data and AI for model development. Exploration of the interplay between PGID and other postoperative complications, such as anastomotic leaks or infections, could lead to holistic risk assessment frameworks. Ultimately, advancing this field requires a concerted effort to bridge translational gaps, fostering innovation that predicts PGID and informs targeted therapeutic interventions, thereby improving the overall care continuum for GI cancer patients[30-32].

DEFINITION, DIAGNOSTIC CRITERIA AND INCIDENCE OF PGID

PGID is a broad term encompassing the delayed recovery of GI motility, secretion, absorption, or barrier function following abdominal surgery, manifesting as symptoms like such as ileus, nausea, vomiting, abdominal distension, and intolerance to oral intake[10]. This condition is a common and significant complication, particularly after GI cancer surgery, where extensive surgical fields and significant neurovascular injury to the bowel contribute to its high incidence[33]. Diagnosis of PGID primarily relies on clinical assessment, with key indicators including the time to first flatus or defecation, recovery of bowel sounds, and the ability to tolerate a diet[34]. However, a universally accepted “gold standard” diagnostic criterion is lacking, leading to variability in reporting and management. To address this, scoring systems like the I-FEED classification have been developed to provide a more objective and consistent framework for describing the clinical manifestations of postoperative GI impairment[35]. The I-FEED score, which evaluates intake, nausea, emesis, physical examination findings, and symptom duration, categorizes patients into normal, postoperative GI intolerance, or PGID groups, demonstrating construct validity in linking higher scores to worse clinical outcomes such as longer hospital stay[35]. Epidemiological data underscore the clinical burden of PGID. A large retrospective study in American hospitals found an overall incidence of PGID of 5.8% among > 638000 inpatient hospitalizations for GI procedures, with major bowel procedures, peritoneal adhesiolysis, and appendectomy being significant predictors[8]. The incidence is notably higher in cancer surgery; for instance, postoperative ileus is reported to occur in approximately 10% of abdominal tumor surgery[36]. In the specific context of GI cancer, PGID remains prevalent and severely impedes patient recovery[7]. The risk is influenced by multiple factors, including surgical approach, with open surgery associated with higher gut trauma and worse outcomes compared to minimally invasive techniques[33]. Robotic surgery may offer additional benefits within enhanced recovery protocols but does not entirely eliminate the problem[33]. Patient-specific factors such as age, preoperative nutritional status (e.g., hemoglobin and albumin levels), and tumor stage have been identified as independent risk factors for PGID in GI tumor patients, enabling the construction of predictive models[7]. The lack of a unified diagnostic standard and high incidence, especially in complex cancer surgery, highlight PGID as a critical area requiring improved definition, early identification, and targeted preventive strategies.

MULTI-DIMENTIONAL IMPACT OF PGID ON PATIENT PROGNOSIS

PGID exerts a profound and multidimensional negative impact on patient prognosis, affecting clinical, nutritional, and psychosocial outcomes. Clinically, PGID is a primary driver of prolonged postoperative hospitalization. Conditions such as postoperative ileus and PGID are directly associated with extended hospital stays, which in turn escalate healthcare costs significantly[10,33]. The prolonged immobility and hospital exposure increase the risk of secondary complications, including hospital-acquired infections, venous thromboembolism, and pulmonary issues, thereby compounding patient morbidity and healthcare resource utilization[37]. For example, in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy, GI dysfunction was a leading cause of prolonged hospitalization[37]. The financial burden is substantial, with costs running into billions of dollars annually in healthcare systems[10]. From a nutritional and metabolic perspective, PGID critically impairs oral intake and nutrient absorption. This dysfunction can lead to or exacerbate postoperative malnutrition, which weakens the immune system and delays wound healing[38]. In patients with GI tumors, who are already at risk of nutritional deficits, this can be particularly detrimental, potentially impacting long-term survival outcomes[38]. The dysfunction also has significant psychological and social repercussions. The persistent symptoms of pain, abdominal discomfort, nausea, and the inability to eat normally contribute to increased patient anxiety and depression[38]. This psychological distress can reduce treatment adherence and overall engagement in rehabilitation programs, creating a vicious cycle that delays recovery. Perioperative anxiety and depression can affect GI physiology via the brain-gut axis; conversely, PGID symptoms may exacerbate mental status[39]. Furthermore, PGID can lead to diminished quality of life, as measured by tools like the GI Quality of Life Index, affecting daily functioning and well-being beyond the acute postoperative phase[40]. In severe cases, particularly when PGID progresses to higher grades of acute GI injury, it is associated with markedly increased morbidity and mortality, as demonstrated in cardiac surgery patients where acute GI injury grade ≥ 2 was associated with a 9.1% 30-day mortality rate[41]. Therefore, PGID is not merely a transient inconvenience but a serious complication that extends hospital stay, increases costs, jeopardizes nutritional status and immune function, impairs quality of life, and elevates the risk of severe complications and death, underscoring the imperative for effective prevention and management strategies.

Patient-related non-modifiable factors: Demographics and tumor characteristics

Advanced age is a consistently identified independent risk factor for PGID in patients undergoing GI cancer surgery. A study constructing a risk prediction model for PGID in GI tumor patients identified age as one of seven independent risk factors[7]. This association is particularly pronounced in elderly patients, where the risk escalates further. A retrospective cohort study focusing on elderly patients (≥ 65 years) undergoing GI cancer surgery found that age ≥ 75 years was an independent predictor of postoperative swallowing dysfunction, with an odds ratio of 2.56[42]. The physiological underpinnings of this vulnerability are multifaceted. Aging is associated with a natural decline in organ functional reserve and autonomic nervous system regulation. The integrity of the enteric nervous system, essential for coordinated motility, can be compromised. Research indicates that surgery and anesthesia can induce cognitive impairment potentially linked to abnormal complement signaling and synaptic disruption; processes that may be exacerbated in the aging brain and could parallel degenerative changes in the enteric nervous system[43]. This neurological susceptibility is compounded by age-related sarcopenia, which itself was identified as an independent predictor of swallowing dysfunction in elderly surgical patients, likely reflecting broader systemic frailty and diminished physiological resilience[42]. Regarding tumor stage, advanced disease presents a compounded risk. Late-stage tumors often necessitate more extensive surgical resection and lymph node dissection, which increase trauma to the celiac plexus and can compromise intestinal blood supply. A narrative review on recovery after abdominal tumor surgery confirms that the technical difficulty of the operation is a key risk factor for postoperative ileus[36]. Patients with advanced cancer frequently present with preoperative comorbidity such as cancer-related intestinal obstruction or cachexia, indicating that their baseline GI function is already impaired prior to surgery, setting the stage for a protracted and complicated postoperative recovery[36]. The influence of gender on PGID risk, while noted in some models as a factor[7], requires more nuanced investigation. The pathophysiological hypothesis suggests that variations in hormonal levels, particularly estrogen, may influence outcomes. A review has observed that estrogens may offer protective effects on the GI tract[44]. Age, age ≥ 75 years, Surgery and anesthesia, the technical difficulty of the operation, patients with advanced cancer, estrogens are identified risk factor for PGID in patients undergoing GI cancer surgery (Table 1)[7,36,42-44]. However, direct evidence linking gender-specific hormonal profiles to PGID risk in elective GI cancer surgery remains sparse and warrants further targeted research to elucidate any distinct mechanisms or risk profiles between males and females.

Table 1 Prospective studies on the patient-related non-modifiable factors.

Ref.	Comments
Wang et al[7]	Age as one of seven independent risk factors
Yoshiyama et al[42]	Age ≥ 75 years was an independent predictor of postoperative swallowing dysfunction, with an odds ratio of 2.56
Ji et al[43]	Surgery and anesthesia can induce cognitive impairment potentially linked to abnormal complement signaling and synaptic disruption
Xie et al[36]	The technical difficulty of the operation is a key risk factor for postoperative ileus. Patients with advanced cancer frequently present with preoperative comorbidity, indicating that their baseline GI function is already impaired prior to surgery
Lang et al[44]	Estrogens may offer protective effects on the GI tract

GI: Gastrointestinal.

Perioperative modifiable factors: Surgery, anesthesia, and nutritional status

The duration and scope of surgery are critical modifiable determinants of PGID risk. Prolonged operating time is consistently associated with increased risk, as it entails extended exposure to anesthetic agents, greater tissue trauma, and a more pronounced systemic inflammatory response; all of which potently inhibit GI motility[36]. A risk prediction model study confirmed operation duration as an independent risk factor for PGID[7]. The anatomical extent of resection is paramount. Extensive procedures such as total gastrectomy or ultra-low anterior resection fundamentally disrupt the normal continuity and neural architecture of the GI tract, leading to more severe and prolonged dysfunction. This is reflected in clinical practice where the complexity of surgery is a recognized risk factor[36]. Preoperative nutritional status, quantified by markers like hemoglobin and albumin, is another pivotal modifiable factor. Preoperative anemia (low hemoglobin) and hypoalbuminemia are both established as independent risk factors in predictive models[7]. Anemia compromises tissue oxygen delivery, impairing wound and anastomotic healing, while hypoalbuminemia contributes to intestinal wall edema, reduces colloid osmotic pressure, and weakens immune competence, thereby exacerbating postoperative inflammation and paralytic ileus[7]. The detrimental impact of anemia extends beyond physical recovery. It is also associated with a higher incidence of postoperative cognitive dysfunction (POCD) and elevated inflammatory markers in elderly GI cancer patients, indicating a systemic burden[45]. Anesthetic and analgesic management constitute a major modifiable domain. The use of opioid analgesics is a well-documented and significant risk factor for postoperative ileus, as opioids act directly on intestinal μ-opioid receptors to inhibit peristalsis and secretion[36]. Strategies to mitigate this risk are therefore a cornerstone of enhanced recovery protocols. The use of regional anesthesia, such as thoracic epidural analgesia, is advocated as part of a multimodal analgesic approach to reduce systemic opioid consumption. A randomized trial comparing patient-controlled intravenous analgesia with epidural analgesia after pancreatic surgery found no significant difference in a composite GI complication endpoint. However, it noted that epidural analgesia was associated with increased intraoperative vasopressor use and postoperative weight gain, indicating fluid shifts[46]. Other regional techniques like transversus abdominis plane or rectus sheath blocks are also used to minimize opioid needs. Conversely, some anesthetic adjuvants may have beneficial effects. For instance, intraoperative infusion of dexmedetomidine has been shown in a randomized controlled trial to significantly accelerate the return of GI function (time to first flatus and defecation) and reduce the incidence of ileus-related symptoms in patients undergoing cesarean delivery[47]. This highlights that anesthetic selection is not merely about risk mitigation but can be actively tailored to facilitate GI recovery.

MODEL CONSTRUCTION METHODOLOGY: FROM REGRESSION ANALYSIS TO RISK SCORING

The construction of risk prediction models for PGID in patients with GI tumors predominantly relies on multivariate logistic regression analysis to identify independent predictive variables. A foundational study by Wang et al[7] exemplified this approach, where data from 176 patients undergoing GI tumor surgery were analyzed. Through univariate and multivariate logistic regression, seven independent risk factors were confirmed: Age, sex, BMI, tumor stage, operation duration, and preoperative hemoglobin and albumin levels[7]. This methodological step of variable screening is crucial, as it isolates factors with significant predictive value from a broader set of clinical parameters. Similar regression-based methodologies are used for related postoperative complications. For instance, studies on POCD in elderly GI tumor patients have used logistic regression to identify independent risk factors such as age, education level, and anemia[45]. Research on severe traumatic brain injury patients identified insulin-like growth factor 1, postoperative intracranial pressure, and fecal calprotectin as independent predictors for GI dysfunction[48]. The subsequent transformation of these regression coefficients into a practical clinical tool involves converting them into an integer-based risk score. This is achieved by applying a specific formula, often based on the β coefficients from the logistic regression model. Each identified risk factor is assigned a point value proportional to its β coefficient, and the sum of these points yields a total risk score, which corresponds to a specific probability of developing PGID. This process effectively translates a complex statistical “equation” into an intuitive “score” that can be easily calculated and interpreted at the bedside. The performance of such internally developed models is then rigorously evaluated. Discrimination, or ability of the model to distinguish between patients who will or will not develop the outcome, is typically assessed using the area under the curve (AUC). In the Wang et al’s study[7], the constructed model demonstrated excellent discrimination with an AUC of 0.895. Calibration, which measures the agreement between predicted probabilities and observed outcomes, is often tested using the Hosmer-Lemeshow goodness-of-fit test. A well-calibrated model shows no significant difference between predicted and observed event rates, as indicated by a non-significant P value (e.g., P = 0.274 in a POCD nomogram study)[49]. This combination of regression analysis, risk score derivation, and internal validation forms the core methodological framework for building initial predictive tools for PGID. Figure 1 showcases construction of the integration of predictive tools, comprehensive prevention and management strategies for PGID.

Figure 1 Construction of the integration of predictive tools, comprehensive prevention and management strategies. BMI: Body mass index.

External validation and challenges in clinical applicability

A significant limitation of many initial risk prediction models, including that developed by Wang et al[7], is the frequent lack of long-term follow-up data and, more critically, external validation using independent datasets. The generalizability or transportability of a model is not guaranteed by strong internal validation metrics alone. The performance of a model in the specific, often homogeneous population from which it was derived may not hold in heterogenous populations from different geographic regions, hospital settings, or surgical practices. For instance, a model developed in a single-center retrospective study on GI perforation patients showed excellent internal discrimination (AUC 0.921) but requires validation in external cohorts to confirm its broad applicability[50]. The clinical utility of a predictive model demands accuracy as well as simplicity and speed for integration into busy clinical workflows. To address this, the development of user-friendly tools is essential. This can involve creating physical risk index cards or, more commonly, digital calculators and nomograms that allow for rapid bedside assessment. Nomograms, which provide a graphical representation of the model, have been widely adopted for various postoperative outcomes. Examples include nomograms for predicting acute respiratory distress syndrome after GI perforation[50], POCD after GI tumor resection[49], and low anterior resection syndrome after robotic rectal cancer surgery[51]. These tools transform the risk score into a visual and quantitative probability, facilitating immediate clinical decision-making. A major methodological challenge underpinning many existing models is their basis on retrospective data, which is inherently susceptible to selection bias and unmeasured confounding variables. To overcome this limitation and to robustly validate and optimize predictive models, prospective, multi-center studies are considered the necessary next step. Such studies, by design, involve predefined protocols, consecutive patient enrollment, and data collection from multiple institutions, thereby enhancing the representativeness of the study population and the reliability of the findings. This approach is crucial for translating promising models from research settings into routine clinical practice, ensuring their accuracy and applicability across diverse healthcare settings.

RISK STRATIFICATON FOR INDIVIDAULIZED CLINICAL MANAGEMENT

The clinical application of risk indices for postoperative PGID fundamentally transforms perioperative care by enabling proactive, individualized management. The primary utility lies in the early identification of high-risk patients, allowing for intensified monitoring and targeted interventions. A robust risk-prediction model for PGID, constructed with an AUC of 0.895, effectively stratifies patients based on factors such as age, sex, BMI, tumor stage, operation duration, and preoperative hemoglobin and albumin levels[7]. The efficacy of this model was further validated by a large meta-analysis, which identified several independent risk factors including male sex, age ≥ 60 years, history of smoking or chronic obstructive pulmonary disease, prolonged operation time, and postoperative opioid use[52]. By quantifying these risks, clinicians can pre-emptively identify patients who are most vulnerable to PGID, shifting the paradigm from reactive treatment to proactive prevention. This risk stratification directly informs preoperative optimization strategies. For patients identified as high- risk, preoperative interventions can be initiated to mitigate modifiable factors. Correction of preoperative anemia and hypoalbuminemia, as highlighted in the risk models, becomes a critical target for nutritional support and medical optimization[7]. This aligns with broader evidence that preoperative nutritional optimization, such as with exclusive EN in specific populations, is associated with reduced postoperative infectious complications[53]. Preoperative prehabilitation programs encompassing physical conditioning and nutritional counseling can be prioritized for these high-risk individuals to enhance physiological reserve. The risk index also provides a scientific basis for optimizing the perioperative care pathway, guiding the formulation of individualized anesthesia and analgesia strategies, with an emphasis on opioid-sparing, multimodal analgesic regimens. Given that postoperative opioid history is a significant risk factor for PGID[52], reducing intraoperative and postoperative opioid exposure is paramount. This can be achieved by integrating regional anesthetic techniques, such as fascial plane blocks, and non-opioid adjuvants such as acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), and the N-methyl-D-aspartate receptor antagonists into a comprehensive ERAS protocol[54]. The choice of surgical approach can also be influenced; for instance, minimally invasive techniques should be considered for high-risk patients when oncologically feasible, as they are associated with less tissue trauma and faster recovery[55]. Finally, the entire postoperative ERAS pathway, including early enteral feeding and mobilization, can be tailored and more rigorously enforced for patients with elevated risk scores, ensuring that every component of care is aligned with mitigating their specific risk profile[56]. Thus, the integration of a validated risk index into clinical workflow facilitates a holistic, patient-centered approach that spans from preoperative assessment through to postoperative recovery, ultimately aiming to reduce the incidence and severity of PGID.

Risk-based preventive and therapeutic measures

Implementing targeted preventive and therapeutic measures based on individual risk stratification is essential for mitigating PGID. Preventive measures are initiated proactively, particularly for high-risk patients, and span the intraoperative and immediate postoperative periods. Intraoperatively, meticulous surgical technique is paramount, emphasizing minimal tissue handling, precise dissection to preserve neural and vascular supply, and careful control of parameters when adjunctive therapies such as hyperthermic intraperitoneal chemotherapy are used. Postoperatively, a bundle of evidence-supported interventions should be deployed early. This includes the judicious use of prokinetic agents (e.g., metoclopramide or erythromycin) to stimulate GI motility, alongside nonpharmacological strategies such as chewing gum (which utilizes the cephalic-vagal reflex) and enforced early ambulation[1]. Critically, these measures are most effective when embedded within a structured, multimodal analgesic plan that minimizes opioid consumption; a key modifiable risk factor for PGID[52]. The cornerstone of such a plan is the adoption of opioid-sparing, multimodal analgesia, which synergistically targets different pain pathways to provide effective pain relief while reducing opioid-related side effects such as ileus. Multimodal analgesic regimens typically combine scheduled acetaminophen, NSAIDs, regional anesthesia techniques (e.g., epidural analgesia or fascial plane blocks), and adjuncts such as ketamine, intravenous lidocaine[54]. For patients who have already developed PGID, therapeutic management must be both supportive and targeted. Foundational supportive care includes nil-by-mouth status, nasogastric decompression for symptomatic relief, and appropriate intravenous fluid resuscitation to maintain electrolyte balance. Beyond this, the risk model can guide targeted therapeutic interventions by hinting at underlying contributing causes. For instance, if the model highlights preoperative hypoalbuminemia or nutritional deficits as risk factors, aggressive postoperative nutritional support becomes a therapeutic priority. This may involve initiating early EN, which has been shown in critically ill populations to improve outcomes, including in patients with abdominal infection[57]. The choice between EN and parenteral nutrition should be individualized; while EN is preferred to maintain gut integrity, combined or total parenteral nutrition may be necessary in cases of prolonged intolerance, although its use requires careful monitoring for complications such as refeeding hypophosphatemia or hepatic dysfunction[58]. If the risk profile or clinical course suggests an infectious component (e.g., intraabdominal sepsis), prompt diagnosis and source control are vital, coupled with appropriate antimicrobial therapy. Nutritional support in this context also serves a therapeutic role, as adequate protein and energy intake are associated with reduced nosocomial infection risk and may help preserve lean muscle mass[59,60]. Therefore, a risk-informed approach to PGID management ensures that therapeutic efforts are not merely symptomatic but address the specific physiological derangements-whether nutritional, inflammatory, or infectious -that predispose the patient to or perpetuate the dysfunction, thereby facilitating a more efficient and comprehensive recovery.

DEFICIENCIES AND ROOM FOR IMPROVEMENT OF EXISTING RESEARCH

Current research on PGID in GI oncology is subject to several limitations that constrain the development of truly effective, mechanism-informed management strategies. A primary shortcoming is the short follow-up duration in most studies, which predominantly focus on short-term complications within the first 30 days post-surgery[61]. This narrow focus overlooks the long-term impact of PGID on critical patient-centered outcomes, including sustained nutritional status, quality of life, and even oncological survival. For instance, while studies have shown that interventions such as home EN can improve short-term nutritional parameters, including albumin and hemoglobin levels[62], their long-term efficacy in preventing chronic malnutrition and sarcopenia, and their downstream effects on cancer recurrence or overall survival remain largely unexplored. This represents a critical gap in understanding the full disease burden of PGID. Secondly, there is a notable absence of specific biomarkers in existing risk prediction models. Current models rely heavily on routine clinical and laboratory parameters, lacking biomarkers that directly reflect the underlying pathophysiology of PGID, such as intestinal barrier integrity, systemic inflammatory state, or neuroendocrine dysregulation[63]. The integration of such biomarkers -for example, specific inflammatory cytokines or microbial metabolites-could transform predictive models from statistical associations to biologically grounded tools. Finally, a significant disconnect exists between epidemiological risk factors and mechanistic research. Most predictive models identify associations (e.g., low preoperative albumin) without elucidating the causal molecular and neuroimmune pathways through which these factors contribute to PGID[63]. The roles of gut microbiota dysbiosis, a sustained inflammatory cytokine storm, or altered macrophage polarization in the genesis of postoperative ileus or anastomotic dysfunction are recognized but not yet tightly integrated into clinical risk stratification or therapeutic targeting[63]. This mechanistic disjunction hinders the development of novel, targeted pharmacological interventions. Addressing these deficiencies requires a shift towards longitudinal studies assessing nutritional and quality of life outcomes over months to years; concerted efforts to discover and validate pathophysiological biomarkers; and translational research that bridges clinical risk factors with investigations into specific cellular and molecular pathways, such as those involving the gut-brain axis and local intestinal immunity.

Frontiers and transformational directions of future research

Future research must pivot towards integrative, mechanism-driven, and precision-oriented strategies to advance the prediction, prevention, and management of PGID. A foremost frontier lies in the optimization and intellectualization of predictive models. Moving beyond static clinical variables, future models should integrate multi-omics data-including genomics, metabolomics, and the gut microbiome-alongside dynamic, real-time monitoring indicators such as continuous vital signs and wearable device data[64]. The use of advanced AI and machine learning algorithms on these rich datasets can facilitate the development of dynamic, personalized risk prediction tools capable of identifying high-risk patients preoperatively and adjusting risk assessments in realtime during the postoperative period. Concurrently, a deeper exploration of underlying mechanisms is imperative. There is a pressing need for dedicated basic and clinical-translational research to elucidate the precise cellular and molecular pathways through which established risk factors, such as hypoalbuminemia or systemic inflammation, lead to PGID[63]. Investigation of pathways related to enteric glial cell function, macrophage polarization states, and integrity of the intestinal mucosal barrier will enhance our pathophysiological understanding and unveil novel therapeutic targets for pharmacological intervention. The ultimate translational goal is to develop evidence-based, integrated prevention strategies, which necessitates the design of robust, risk-stratified randomized controlled trials to evaluate the efficacy of targeted, multimodal prevention bundles. These bundles could combine optimized ERAS protocols with specific adjuncts like immunonutrition, probiotics to modulate gut microbiota[64], or novel pharmacological agents identified through mechanistic research. The validated strategies must then be systematically integrated into standardized, yet adaptable, ERAS pathways to create a seamless “predict-to-prevent” closed-loop management system[65]. This integration emphasizes dynamic, gap-free perioperative care that spans prehabilitation, surgery, and post-discharge follow-up, actively involving multidisciplinary teams and patients themselves[65]. Encouragingly, ongoing trials are already exploring such integrative approaches, including multimodal prehabilitation programs incorporating exercise, nutrition, and psychological support[66]. By converging on model optimization, mechanistic discovery, and evidence-based protocol integration, future research can transform PGID management from reactive complication handling to proactive, personalized recovery optimization, ultimately improving long-term surgical and oncological outcomes for patients with GI tumors.

CONCLUSION

The management of PGID following GI cancer surgery remains a significant clinical challenge, profoundly impacting patient recovery and long-term quality of life. This review has synthesized current evidence, highlighting that PGID is a multifactorial syndrome in which surgical trauma, neural disruption, inflammatory cascades, and pre-existing patient vulnerabilities converge. The development of risk prediction models, exemplified by the work of Wang et al[7], represents a pivotal advancement in transitioning from reactive management to proactive, personalized care. By integrating readily available clinical parameters -such as age, tumor stage, surgical complexity, and nutritional status-these models distill complex pathophysiology into practical risk indices. The translation of statistical regression into clinically actionable tools is a critical step forward, enabling early risk stratification and facilitating timely, individualized perioperative interventions.

From an expert perspective, the evolution of PGID research reflects a broader paradigm shift in surgical oncology towards precision medicine. The primary strength of current predictive models lies in their immediate clinical utility and capacity for rapid implementation. These models enable clinicians to stratify care pathways, optimize resource allocation, and initiate preventive strategies, including optimized analgesia, early EN, or prokinetic agents in a targeted manner. However, a balanced analysis necessitates acknowledgment of their current limitations. Most models require rigorous external validation across diverse populations and healthcare settings to ensure generalizability. Furthermore, their focus on short-term outcomes often overlooks the long-term trajectory of GI function and its correlation with overall survival and quality of life. Perhaps most importantly, these phenomenological models, while excellent for prediction, are not yet fully explanatory. They identify “who” is at risk but provide limited insight into the “why”-the deeper molecular and pathophysiological mechanisms driving PGID.

Therefore, the future trajectory of PGID management must focus on bridging this gap between prediction and mechanism. The next generation of research should be bifocal. Firstly, it must strengthen the clinical predictive framework through large-scale, multi-center prospective studies to validate and refine existing models. Incorporating dynamic data from ERAS protocols and exploring novel biomarkers-such as specific inflammatory cytokines, gut microbiome profiles, or markers of autonomic dysfunction -could significantly improve both predictive accuracy and biological plausibility. Secondly, and concurrently, there must be a dedicated push towards fundamental mechanistic research. It is essential to understand the intricate interplay between surgical stress, the gut-brain axis, immune response, and enteric nervous system plasticity. This knowledge will validate the variables in our predictive models and unlock the door for targeted preventive strategies and novel therapeutics, moving beyond symptom management to address root causes.

Ultimately, the goal is to construct an integrated “Predict-Prevent-Treat” management strategy, in which, a validated risk index acts as the initial trigger, placing a patient on a tailored surveillance and prevention pathway. This pathway would be informed by ongoing mechanistic insights, potentially involving neuromodulatory agents, specific anti-inflammatory therapies, or microbiome modulation. By seamlessly integrating prediction derived from clinical data with prevention guided by biological understanding, we can optimize perioperative care comprehensively. This approach holds the promise of significantly reducing the incidence and severity of PGID, thereby shortening hospital stays, reducing healthcare costs, and, most importantly, improving the functional recovery and overall prognosis of GI cancers survivors. The journey is from identifying risk to understanding vulnerability, and finally to delivering resilient, patient-centered care.