Related Experiment Videos
Development and Prospective Validation of a Machine Learning Tool for Predicting Multidrug-resistant Organism
Nadheem M Shajeef1,2, Teresa M Sobi1,2, Chithra Jayaprakash3
1Department of Pharmacy Practice, Nirmala College of Pharmacy, Muvattupuzha, Kerala, India.
Background And Aims:
Multidrug-resistant organisms (MDROs) are a growing threat in critical care settings, causing prolonged hospitalization, increased costs, and mortality. Irrational antibiotic use, invasive procedures, and limited diagnostic times contribute to the prevalence of MDROs. Machine learning (ML) offers a promising approach for early risk identification. This study aimed to develop and pilot validate an ML-based tool to predict MDRO infection risk in Critical Care Unit (CCU) patients using baseline clinical data.
Patients And Methods:
Retrospective data from 323 patients admitted to the CCU of a tertiary care hospital in Kerala, India, were used to develop and evaluate five ML models using Python (version 3.11). Predictor variables included demographics, comorbidities, and initial medical device usage. A pilot prospective validation was subsequently conducted on 49 new CCU admissions to assess real-world predictive performance within 48 hours of admission.
Results:
Among 323 patients, 76 developed MDRO infections (23.5%). Major risk factors identified included prolonged hospitalization and invasive device use. The random forest model demonstrated a training accuracy of 96.0% and a test-set accuracy of 75.3%. In the pilot prospective validation, the tool yielded a sensitivity of 81.8%, a specificity of 100%, and an overall accuracy of 95.9%.
Conclusions:
This study demonstrates that the ML-based tool can effectively stratify MDRO risk in a CCU setting. While the performance discrepancy between training and testing indicates overfitting, the high specificity and sensitivity observed in the prospective pilot phase support its potential as a bedside aid for early antimicrobial stewardship (AMS).
Clinical Significance:
The ML tool enables early risk stratification, allowing for timely infection control and efficient resource utilization in constrained clinical settings.
Insights
Machine learning effectively predicts multidrug-resistant organism (MDRO) infection risk in critical care units (CCUs). This tool aids early intervention and antimicrobial stewardship, improving patient outcomes and resource management.
Area of Science:
- Critical care medicine
- Infectious diseases
- Health informatics
Background:
- Multidrug-resistant organisms (MDROs) pose a significant threat in critical care settings, leading to increased morbidity, mortality, and healthcare costs.
- Irrational antibiotic use, invasive procedures, and diagnostic delays contribute to MDRO prevalence.
- Early identification of patients at risk for MDRO infections is crucial for effective management.
Purpose of the Study:
- To develop and pilot validate a machine learning (ML)-based tool for predicting MDRO infection risk in Critical Care Unit (CCU) patients.
- To utilize baseline clinical data for early risk stratification.
- To assess the tool's real-world predictive performance.
Main Methods:
- Retrospective analysis of 323 CCU patients' data, including demographics, comorbidities, and medical device usage.
- Development and evaluation of five ML models using Python.
- Pilot prospective validation on 49 new CCU admissions within 48 hours of admission.
Main Results:
- 76 out of 323 patients (23.5%) developed MDRO infections.
- Random forest model showed 96.0% training accuracy and 75.3% test accuracy.
- Prospective validation achieved 81.8% sensitivity, 100% specificity, and 95.9% overall accuracy.
Conclusions:
- The ML tool effectively stratifies MDRO risk in CCU patients.
- Despite potential overfitting, high prospective sensitivity and specificity support its use as a bedside aid for antimicrobial stewardship (AMS).
- Early risk stratification facilitates timely infection control and optimized resource allocation.
