Predicting open wound mortality in the ICU using machine learning.
Ronald K Akiki1,2, Rajsavi S Anand1,2, Mimi Borrelli3
1Alpert Medical School, Brown University, Providence, RI, USA.
Journal of Emergency and Critical Care Medicine (Hong Kong, China)
|November 12, 2021
Summary
Machine learning accurately predicts mortality risk in intensive care unit (ICU) patients with open wounds. This model offers improved patient care and management strategies for this vulnerable population.
Area of Science:
- Critical Care Medicine
- Data Science in Healthcare
- Wound Management
Background:
- Open wounds significantly impact patient health, leading to pain, functional loss, and increased mortality.
- Open wounds are a prevalent comorbidity, affecting a large US population and increasing ICU stay risk.
- Limited research exists on mortality prediction for ICU patients with open wounds.
Purpose of the Study:
- To develop a predictive model for mortality risk in intensive care unit (ICU) patients with open wounds.
- To assess the efficacy of machine learning models in predicting outcomes for this patient group.
Main Methods:
- Utilized the Medical Information Mart for Intensive Care III (MIMIC-III) database for de-identified patient data.
- Developed and compared random forest and binomial logistic regression models.
- Included variables such as wound location, demographics, admission type, platelet count, and hyperphosphatemia.
Main Results:
- The study included 3,937 patients; 15% died during their ICU stay.
- The random forest model demonstrated high predictive accuracy with an Area Under the Curve (AUC) of 0.924.
- Traditional comorbidity indices showed lower predictive power (AUCs of 0.528 and 0.565).
Conclusions:
- Machine learning models show promise for predicting mortality in ICU patients with open wounds.
- These models can potentially enhance clinical decision-making and patient management.
- Further research may refine these predictive tools for improved wound care outcomes.
