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Factors Affecting the Risk of Infection

Factors Affecting the Risk of Infection

The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...

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Related Experiment Video

Updated: Mar 17, 2026

Assessment of Acute Wound Healing using the Dorsal Subcutaneous Polyvinyl Alcohol Sponge Implantation and Excisional Tail Skin Wound Models.

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Development and Validation of a Predictive Model for Surgical Site Infection in Open Hand Injuries.

Xingguo Nie¹, Guodong Wang², Yiwen Ba¹

¹Department of Orthopedics, The First Affiliated Hospital of Henan Medical University, Weihui, Henan, People's Republic of China.

Infection and Drug Resistance

|March 16, 2026

Summary

This summary is machine-generated.

Machine learning models can predict surgical site infections (SSI) in open hand injuries. A random forest model showed strong performance, identifying key risk factors like age and diabetes for better patient outcomes.

Keywords:

clinical decision support machine learning open hand injuries postoperative incision infection random forest

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Published on: May 2, 2025

Area of Science:

Orthopedic Surgery
Infectious Disease Epidemiology
Computational Biology

Background:

Surgical site infection (SSI) is a significant complication in open hand injuries.
Current risk assessment methods for SSI lack precision and generalizability.
Objective assessment is needed to improve patient outcomes.

Purpose of the Study:

Develop and validate machine learning (ML) models to predict SSI in open hand injuries.
Compare the performance of various ML algorithms for SSI prediction.
Identify key clinical indicators for SSI risk stratification.

Main Methods:

Retrospective analysis of 800 patients with open hand injuries.
Development and comparison of eight ML algorithms, including random forest.
Internal and external validation using independent cohorts and performance metrics like AUC.

Main Results:

The random forest model achieved the highest predictive performance (AUC 0.849-0.903).
Key predictors identified include age, smoking, diabetes, time to surgery, wound contamination, and negative pressure drainage.
SHapley Additive exPlanations (SHAP) provided interpretability for identified risk factors.

Conclusions:

The random forest model demonstrates robust predictive capability and generalizability for SSI in open hand injuries.
This ML model can serve as a clinical decision-support tool for early risk stratification.
Personalized interventions based on predicted risk can potentially reduce morbidity and improve outcomes.