Related Experiment Video
Updated: Jan 15, 2026
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Machine learning prediction of multidrug-resistant urinary tract infections in brain and spinal cord injury patients:
Su Ji Lee1, Hangyul Yoon2, Ji Cheol Shin1
1Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, the Republic of Korea.
Background And Objective:
Multidrug-resistant urinary tract infections (MDR UTIs) are a growing concern in patients with brain and spinal cord injuries due to their high susceptibility to UTIs and repeated antibiotic exposure. This study aimed to develop and externally validate a machine learning model for predicting MDR UTIs in patients with brain and spinal cord injuries.
Methods:
We retrospectively analyzed 849 patients with brain or spinal cord injuries and culture-confirmed UTIs. Data from a single institution were used for model training, and the other served as an external test set. Five machine learning models and ensemble models were trained and evaluated. Model performance was evaluated using accuracy, sensitivity, specificity, positive/negative predictive value, and area under the receiver operating characteristic curve.
Results:
The finally selected ensemble model, combining Naive Bayes and Random Forest, achieved an area under the receiver operating characteristic curve of 0.8425, with an accuracy of 0.7688, sensitivity of 0.7647, specificity of 0.7697, positive predictive value of 0.4262, and negative predictive value of 0.9360. Important predictors included recent antibiotic use, total Functional Independence Measure score, neutrophil-to-platelet ratio, neutrophil-to-lymphocyte ratio, erythrocyte sedimentation rate, age, hematocrit, hemoglobin, serum protein, serum albumin, and urethral indwelling catheter.
Conclusion:
Externally validated, the model showed consistent performance across institutions. It may support early MDR risk stratification in patients with brain and spinal cord injuries. Early identification of at-risk individuals may guide antibiotic stewardship in neurorehabilitation settings.
Insights
A new machine learning model can predict multidrug-resistant urinary tract infections (MDR UTIs) in patients with brain and spinal cord injuries. This tool aids in early risk stratification and antibiotic stewardship in neurorehabilitation.
Area of Science:
- Medical Informatics
- Infectious Diseases
- Neurology
Background:
- Multidrug-resistant urinary tract infections (MDR UTIs) pose a significant threat to patients with brain and spinal cord injuries.
- These patients exhibit increased susceptibility to UTIs and frequent antibiotic exposure, exacerbating the MDR UTI problem.
Purpose of the Study:
- To develop and externally validate a machine learning model for predicting MDR UTIs in individuals with brain and spinal cord injuries.
- To identify key predictors for MDR UTIs in this vulnerable patient population.
Main Methods:
- Retrospective analysis of 849 patients with brain or spinal cord injuries and culture-confirmed UTIs.
- Development and evaluation of five machine learning and ensemble models using single-institution training data and external test sets.
- Performance assessment via accuracy, sensitivity, specificity, predictive values, and AUC.
Main Results:
- An ensemble model combining Naive Bayes and Random Forest achieved an AUC of 0.8425.
- The model demonstrated good predictive performance with an accuracy of 0.7688 and a negative predictive value of 0.9360.
- Key predictors included recent antibiotic use, Functional Independence Measure score, neutrophil-to-platelet ratio, neutrophil-to-lymphocyte ratio, and indwelling urethral catheter.
Conclusions:
- The externally validated machine learning model shows consistent performance across different institutions.
- This model can aid in the early risk stratification of MDR UTIs in patients with neurological injuries.
- Early identification of high-risk patients can optimize antibiotic stewardship in neurorehabilitation settings.
