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Decision Trees for Managing Impaired Physical Mobility in Multiple Trauma Patients
Raisa Camilo Ferreira1,2, Karen Dunn-Lopez2, Sue Moorhead2
1School of Nursing, State University of Campinas, Campinas, Brazil.
Journal of Advanced Nursing
|May 7, 2025
Summary
Decision trees effectively predict trauma patient mortality and mobility decline using conditional probabilities. Early identification of high-risk patients enables timely interventions and personalized rehabilitation, improving recovery outcomes.
Area of Science:
- Nursing
- Critical Care
- Data Science
Background:
- Trauma patient outcomes are influenced by mobility and mortality risks.
- Predictive models are crucial for optimizing clinical decision-making in trauma care.
Purpose of the Study:
- To develop and validate decision trees for identifying predictors of mortality and morbidity deterioration in trauma patients.
- To leverage conditional probabilities for enhanced clinical decision support.
Main Methods:
- A quasi-experimental longitudinal study involving 201 trauma patient records.
- Utilized the Chi-squared Automatic Interaction Detection (CHAID) algorithm for decision tree construction.
- Validated models using K-fold cross-validation for reliability and predictive accuracy.
Main Results:
- Decision trees identified key predictors for survival and mobility deterioration.
- Patients not requiring Cardiopulmonary Status (NOC 0414) but needing Transfer Performance (NOC 0210) had a 97.4% survival rate.
- Requiring Cardiopulmonary Status (NOC 0414) correlated with a 25% risk of mobility worsening, versus 9% for others.
Conclusions:
- Decision trees offer a robust, data-driven framework for predicting mobility outcomes and mortality risk in trauma patients.
- Findings emphasize the importance of early mobilization and tailored rehabilitation for improved patient recovery.
- This novel application of decision trees in trauma nursing enhances evidence-based practice and patient safety.
