Risk Prediction Model for Postoperative Infections in Patients with Gastrointestinal Diseases: A Scoping Review

Detailed Description of the Research Project 1. Research Title A Scoping Review of Risk Prediction Models for Postoperative Infections in Patients with Gastrointestinal Diseases 2. Research Background and Clinical Problem Gastrointestinal surgeries (such as colorectal resection, gastrectomy, hepatobiliary surgery, etc.) are primary treatment methods for various benign and malignant diseases. However, postoperative infections (including surgical site infections, intra-abdominal infections, pneumonia, urinary tract infections, and bacteremia) are among the most common and serious complications of these procedures. These infections not only significantly increase patient suffering, prolong hospital stays, and raise medical costs, but are also closely associated with higher rates of reoperation, long-term recurrence risk, and mortality. In clinical practice, accurately identifying patients at high risk of postoperative infection is crucial for implementing precise preventive strategies (such as targeted antibiotic prophylaxis, enhanced perioperative care, and optimized nutritional support). In recent years, prediction models (e.g., statistical models, machine learning models) based on clinical data have been widely developed to quantify individual patient infection risk. However, current research in this field is fragmented: Diverse Model Types: Ranging from traditional logistic regression to complex machine learning algorithms. Varied Predictors: The included variables span multiple dimensions such as patient characteristics, disease status, surgical factors, and laboratory indicators. Heterogeneous Study Populations: Models targeting different gastrointestinal sites (stomach, colorectum, hepatobiliary-pancreas) lack横向 comparison. Inconsistent Model Quality: There are significant disparities in methodological rigor, validation thoroughness, and clinical applicability. Therefore, a systematic scoping review is urgently needed to map the knowledge landscape of this field, synthesize existing evidence, clarify research gaps, and provide direction for the future development, validation, and clinical translation of high-quality prediction models. 3. Research Purpose This scoping review aims to systematically investigate, describe, and synthesize the existing research evidence on risk prediction models for postoperative infections in patients with gastrointestinal diseases. Specific objectives are as follows: Map the Knowledge Landscape: Identify and describe all relevant published or established prediction models. Compare and Analyze Model Characteristics: Analyze the similarities and differences among these models regarding target populations (e.g., gastric cancer, colorectal cancer, IBD patients), types of infections predicted, predictors (variables) used, modeling methodologies (statistical/machine learning), and model performance (discrimination, calibration). Assess Methodological Quality: Evaluate the design and reporting quality of existing studies using specialized tools (e.g., PROBAST – Prediction model Risk Of Bias Assessment Tool). Explore Clinical Translation Status: Investigate the external validation, clinical impact assessment, and practical clinical application of these models. Identify Key Evidence Gaps: Clearly pinpoint weaknesses in current research, unresolved clinical questions, and priority areas for future studies. 4. Research Methodology This study will strictly adhere to the scoping review methodological framework (Arksey & O'Malley, and the Joanna Briggs Institute [JBI] guidance for scoping reviews) to ensure transparency and reproducibility. Key steps include: Defining the Research Question: Using the PCC (Population, Concept, Context) framework. P (Population): Adult patients with gastrointestinal diseases undergoing elective or emergency surgery. C (Concept): Risk prediction models for postoperative infection. C (Context): Perioperative management in healthcare settings. Literature Search: Systematic searches of Chinese and English databases, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Data. The search strategy will combine subject headings and free-text terms围绕 core concepts such as "gastrointestinal surgery," "postoperative infection," and "risk prediction model." Literature Screening: Two researchers will independently conduct title/abstract screening and full-text screening. Disagreements will be resolved through discussion or consultation with a third party. Data Extraction: A standardized data extraction form will be designed. Content will include: basic study information (author, year, country), study design, patient characteristics, infection outcome definition, prediction model details (development/validation, variables, algorithm, performance metrics), methodological quality, etc. Evidence Synthesis: Descriptive analysis and thematic analysis will be employed. Tables and figures (e.g., flowcharts, bubble charts) will be used to visually present model distribution, common predictors, methodological features, etc. Findings on model performance and quality assessments will be summarized. 5. Expected Outcomes A Comprehensive Evidence Map: Clearly展示 currently developed prediction models and their core characteristics for quick reference by researchers and clinicians. Identification of Key Predictors:归纳 commonly recurring variables with strong predictive value across different models (e.g., albumin level, operative time, ASA classification), providing focus for clinical risk screening. A Methodological Quality Assessment Report: Objectively揭示 common strengths and weaknesses of current research regarding data sources, variable handling, model validation, and reporting standards, thereby enhancing the methodological rigor of future studies. Clarification of Clinical Translation Bottlenecks: Point out key barriers preventing most models from moving beyond the paper stage into clinical decision support systems (e.g., lack of external validation, poor user-friendliness, absence of clinical impact assessment). Proposal of a Future Research Roadmap: Based on identified evidence gaps, provide concrete recommendations, for example: Prioritize developing models for specific high-risk subgroups (e.g., elderly, malnourished IBD patients). Advocate for using standardized outcome definitions and reporting guidelines (e.g., TRIPOD statement). Emphasize the importance of conducting multicenter external validation and prospective impact studies. Explore the feasibility of integrating existing models into electronic health record systems for real-time, dynamic risk预警. 6. Research Significance and Value For Clinical Practice: Helps clinicians understand existing prediction tools, promoting evidence-based risk stratification and the development of individualized preventive measures, ultimately improving patient outcomes. For Researchers: Avoids redundant research and provides a solid theoretical foundation and methodological guidance for developing the next generation of more accurate and reliable prediction models. For Health Policy and Hospital Management: Provides an evidence base for decision-making regarding optimizing resource allocation and quality management for perioperative infection prevention and control.