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Factors Affecting the Risk of Infection01:26

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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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Post-Operative Infection Prediction and Risk Factor Analysis in Colorectal Surgery Using Data Mining Techniques: A

Kamran Azimi1, Michael D Honaker2,3, Sreenath Chalil Madathil4

  • 1Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, New York, USA.

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Developing accurate prediction models can help identify patients at high risk for post-operative infections, enabling targeted interventions to reduce complications and improve patient outcomes.

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Area of Science:

  • Medical Informatics
  • Surgical Outcomes Research
  • Predictive Analytics in Healthcare

Background:

  • Post-operative infections significantly impact patient recovery, increase readmission rates, and impose financial burdens on healthcare systems.
  • Identifying patients at high risk for surgical site infections is crucial for implementing preventive strategies.
  • Current methods for predicting post-operative infections require refinement to improve accuracy and clinical utility.

Purpose of the Study:

  • To develop and validate a predictive model for identifying patients at high risk of post-operative infections following colorectal surgery.
  • To compare the performance of various machine learning algorithms in predicting post-operative infections.
  • To identify key pre-operative and clinical factors associated with post-operative infection risk.

Main Methods:

  • Retrospective analysis of 208 patients undergoing colorectal resection between 2016 and Q1 2017.
  • Univariable analysis to identify significant factors associated with post-operative infection.
  • Development and comparison of prediction models using Decision Tree, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, Neural Network, and Random Forest algorithms.

Main Results:

  • Several factors were significantly associated with post-operative infections, including elective surgery, steroid use, and unplanned returns to the operating room.
  • Accurate prediction models were developed using seven key factors: age, serum sodium, blood urea nitrogen, hematocrit, platelet count, procedure time, and length of stay.
  • Logistic Regression and Support Vector Machine (SVM) demonstrated stable performance in predicting infections.

Conclusions:

  • Predictive models incorporating pre-operative and clinical data can effectively identify patients at risk for post-operative infections.
  • The identified significant factors provide opportunities for targeted interventions to mitigate infection risk.
  • The developed models offer a valuable tool for clinicians to proactively manage patients susceptible to post-operative complications.