Background: Prolonged postoperative ileus (PPOI) remains a common complication after colorectal surgery, leading to delayed gastrointestinal recovery, prolonged hospitalization, and increased healthcare costs. Despite minimally invasive surgery and enhanced recovery protocols, postoperative gastrointestinal dysfunction remains frequent. Interpretation of current evidence is limited by substantial heterogeneity in PPOI definitions and outcome reporting. Methods: This systematic review was conducted according to PRISMA 2020 and Cochrane Handbook recommendations, following a protocol prospectively registered for PROSPERO (CRD420261307668). PubMed/MEDLINE, Embase, and CENTRAL were searched for randomized controlled trials published since January 2000 involving adult colorectal surgery patients. Studies evaluating all perioperative factors associated with PPOI were included. Screening, data extraction, and risk-of-bias assessment were independently performed by multiple reviewers. Findings were synthesized narratively according to perioperative phase and intervention type. Results: Of the 36 included RCTs, 7 (19.4%) reported an operational definition of POI/PPOI, of which 5 (13.9% of all included trials) applied a predefined time-based definition corresponding specifically to PPOI. The remaining studies reported POI/PPOI without sufficiently detailed diagnostic criteria or primarily assessed surrogate measures of gastrointestinal recovery. Across the included trials, effects on postoperative gastrointestinal recovery were heterogeneous, although selected analgesia strategies, early oral feeding, gum chewing, and selected pharmacological interventions showed potentially favorable effects on gastrointestinal recovery outcomes. Non-opioid and multimodal analgesic strategies were associated with earlier bowel recovery, with time to first flatus within 1.5–2 days and first bowel movement within 2–3 days. Although leukocyte count and CRP remained elevated postoperatively in the limited number of trials reporting these markers, no significant between-group differences were observed; therefore, these data do not establish an independent association between postoperative inflammation and PPOI. Conclusions: Because clinically defined PPOI was assessed in only a small proportion of included trials, the available evidence relates predominantly to postoperative gastrointestinal recovery rather than to prevention of clinically defined PPOI. PPOI remains a multifactorial complication arising within the complex interplay of surgical, pharmacological, inflammatory, and perioperative factors; however, the independent effects of opioid exposure, postoperative inflammation, and background recovery protocols cannot be established from the included trials unless these factors are directly randomized. Potential signals of benefit were observed with selected analgesic strategies, early feeding, gum chewing, and some pharmacological interventions; however, heterogeneity in PPOI definitions, interventions, and outcome reporting precludes definitive conclusions regarding their efficacy for PPOI prevention. Persistent heterogeneity in PPOI definitions limits direct comparison between studies. The randomized intervention–control comparisons identified potential signals of benefit for selected strategies, although their efficacy for preventing clinically defined PPOI remains uncertain.

Background: Early identification of patients at increased risk of severe acute pancreatitis (AP) remains challenging because clinically relevant disease heterogeneity may not be fully apparent during the initial evaluation. Premature activation of pancreatic digestive zymogens within acinar cells is a central event in the pathophysiology of AP, and biomarkers directly related to this proteolytic process may provide information that is biologically distinct from nonspecific inflammatory markers. This study investigated the prognostic relevance of early serum and urinary trypsinogen-2 and trypsin activation peptide (TAP) in patients with AP, using complementary statistical, Bayesian, and machine-learning approaches. Methods: This retrospective observational cohort included 73 adults with AP who underwent serum and urinary proteolytic biomarker assessment within the first 24 h after admission. Imaging severity was evaluated using the modified Computed Tomography Severity Index (mCTSI) and was available for 66 patients. Associations between biomarkers and clinical or imaging variables were assessed using Spearman correlation and Bayesian inference. Receiver operating characteristic (ROC) analysis evaluated urinary trypsinogen-2 for mCTSI ≥ 4 and mCTSI ≥ 8. A multiple mediation model assessed indirect pathways linking C-reactive protein (CRP) with mCTSI. Unsupervised K-means clustering was used to identify proteolytic profiles, while Elastic Net and Random Forest models were developed to predict higher imaging severity (mCTSI ≥ 6) and log-transformed CRP. No imputation was performed; each analysis used the available valid observations. Results: Urinary trypsinogen-2 concentrations increased significantly across mild, moderate, and severe imaging categories (medians 5149.8, 11,952.8, and 27,008.6 pg/mL, respectively; Kruskal–Wallis p = 0.0073). For mCTSI ≥ 8, urinary trypsinogen-2 yielded an AUC of 0.791 (95% CI, 0.638–0.944), with 77.8% sensitivity and 75.4% specificity at a threshold > 14,707 pg/mL, and a negative predictive value of 95.6%. For mCTSI ≥ 4, the AUC was 0.683 (95% CI, 0.550–0.817). Urinary trypsinogen-2 correlated positively with mCTSI (rho = 0.412, p < 0.001), serum trypsinogen-2 (rho = 0.512, p < 0.001), and urinary TAP (rho = 0.447, p < 0.001), but not with CRP, amylase, or lipase. Bayesian analysis provided strong evidence for the association between urinary trypsinogen-2 and mCTSI (BF₁₀ = 42.37). In the mediation model, the indirect pathway through urinary trypsinogen-2 was statistically significant (β = 0.14, p = 0.009). K-means clustering identified three proteolytic profiles that differed significantly in age, CRP, and BISAP score (p = 0.003, p < 0.001, and p < 0.001, respectively). For mCTSI ≥ 6, Random Forest achieved an AUC of 0.86 and Elastic Net an AUC of 0.81, with urinary trypsinogen-2 the most important predictor in both models. In CRP regression, R² was 0.53 for Random Forest and 0.41 for Elastic Net. Conclusions: Early urinary trypsinogen-2 was consistently associated with imaging severity and represented the strongest predictor across multiple analytical frameworks, including frequentist correlation, Bayesian inference, mediation analysis, clustering, and machine learning. These findings support its potential role as a component of multimodal early risk stratification in AP, providing information complementary to conventional clinical and biochemical assessment. However, the identified ROC thresholds and machine-learning models remain exploratory and require prospective, multicenter external validation before clinical implementation.