A Clinical Prediction Model for Surgical Site Infection After Central Nervous System Tumor Surgery

1. Study Background For patients undergoing brain or spinal tumor surgery, postoperative surgical wound infection (known as "surgical site infection," SSI) is a recognized risk. Once an infection occurs, it may complicate and prolong the recovery process. Currently, doctors primarily rely on clinical experience to judge which patients are at higher risk of infection. Our research team has previously developed an intelligent prediction model (a machine learning tool) that can very accurately identify which patients are at higher risk of postoperative infection. This study aims to validate whether implementing enhanced preventive measures for high-risk patients in advance, based on the model's predictions, can effectively reduce the occurrence of infection. 2. Study Purpose The primary purpose of this study is to validate whether an individualized intervention strategy based on an intelligent prediction model can effectively reduce the incidence of surgical site infection in patients after central nervous system tumor surgery. Simultaneously, we will also evaluate the safety of this strategy, its impact on patient hospital stay length and medical costs, as well as its feasibility in practical clinical application. 3. Study Design Type: This is a prospective, single-center clinical validation study. "Prospective" means the research plan is established first, followed by patient recruitment and data collection according to the plan; "single-center" indicates the study is conducted solely at the Chinese Academy of Medical Sciences Cancer Hospital. Process: For all patients who consent to participate in this study, within 72 hours after surgery, the research system will automatically calculate their infection risk based on 8 key clinical indicators (such as blood test results, medical history, etc.). High-Risk Group (model-predicted infection probability ≥50%): Will receive a set of enhanced, individualized infection prevention measures (e.g., adjusted antibiotic