Nadheem M Shajeef, Teresa M Sobi, Chithra Jayaprakash, Suja Abraham
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).
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.