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

Summary

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 regimen, enhanced nutritional support, closer monitoring).

Low-Risk Group (model-predicted infection probability \<50%): Will receive the current standard, high-quality postoperative care.

Duration: The study is planned to run from February 2026 to October 2029. Each participating patient will be followed for 3 months (90 days) to observe whether infection occurs.

Sample Size: It is planned to recruit approximately 500 eligible patients.
4. Primary Evaluation Indicators Primary Indicator: The incidence of surgical site infection within 90 days after surgery. The diagnosis of infection will be made by experts who are unaware of the patient's group assignment, strictly following international standards.

Secondary Indicators: Include the accuracy of the intelligent prediction model in practical use, patient hospital stay length, other infection-related complications, medical costs, and the adoption rate of the model's recommendations by physicians.
5. Eligibility Criteria (Inclusion Criteria Summary)

You may be eligible to participate in this study if you meet the following conditions:

Diagnosed with a brain or spinal tumor and scheduled for elective surgery. Aged 18 years or older. Expected survival exceeds 3 months and able to cooperate with postoperative follow-up.

Voluntary participation and signing of a written informed consent form. (Note: This study has detailed exclusion criteria. Final confirmation of whether all criteria are met will be determined by your study doctor.)
6. Patients' Rights and Safety Voluntary Participation: Participation in this study is entirely voluntary. You have the right to withdraw from the study at any time, which will not affect the quality of any normal medical services you are entitled to at our hospital or your relationship with it.

Informed Consent: Before the study begins, your study doctor will explain all study procedures, potential risks, and benefits in detail, and you will sign an "Informed Consent Form." Privacy Protection: All your personally identifiable information will be kept strictly confidential. Codes will replace your name and other identifiable information in research analyses and reports.

Safety Assurance: This study has been reviewed and approved by the hospital's Ethics Committee. We have established an independent Data Safety Monitoring Board to monitor the study's safety throughout. Clinical trial liability insurance has been purchased for all participants to cover damages related to the trial.
7. Contact Information

If you would like to learn more about this study, please contact us via:

Principal Investigator: Dr. Yang Ming National Cancer Center/National Clinical Research Center for Cancer/Cancer Research Unit: Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College

Conditions

• Surgical Site Infection (SSI)
• Central Nervous System Tumor

Interventions

OTHER

ML-Based Enhanced Intervention Package for SSI Prevention

This is a comprehensive, individualized intervention package administered to patients identified as high-risk for surgical site infection (SSI) by a machine learning (LightGBM) model. The package is activated postoperatively and may include, but is not limited to: 1) \*\*Escalated/optimized antibiotic prophylaxis regimen\*\*; 2) \*\*Intensified nutritional support protocols\*\*; 3) \*\*Enhanced monitoring\*\* of vital signs and wound status. The specific components are tailored based on the patient's risk profile and clinical context. The intervention aims to validate the effectiveness of a model-driven strategy in reducing SSI incidence.

OTHER

Standard Postoperative Care

This intervention represents the current standard of care provided to patients after elective central nervous system tumor surgery at this institution. It includes routine antibiotic prophylaxis, standard wound care, basic vital signs monitoring, and follow-up according to established clinical pathways. Patients in this group do not receive the enhanced, study-specific intervention package and serve as the control population for comparison.

Sponsors & Collaborators

• Ming Yang

  lead OTHER

Study Design

Allocation

NON_RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-06-01

Primary Completion

2029-10-30

Completion

2029-10-30

Countries

• China

Study Locations