Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates
Yuksel Akkaya1, Irfan Aydin2, Handan Tanyildizi-Kokkulunk3
1Department of Medical Microbiology, Hamidiye Faculty of Medicine, University of Health Sciences, Istanbul 34668, Türkiye.
Abstract:
Background/Objectives:Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.
Insights
Machine learning models rapidly predict Klebsiella pneumoniae antibiotic resistance, outperforming traditional methods. This accelerates crucial treatment decisions for better patient outcomes and antimicrobial stewardship.
Area of Science:
- Clinical microbiology
- Computational biology
- Infectious disease epidemiology
Background:
- Klebsiella pneumoniae exhibits increasing resistance to carbapenems and broad-spectrum antibiotics.
- Timely antibiotic selection is critical in severe infections like septic shock, but conventional resistance testing is slow.
- Rapid antimicrobial resistance prediction is needed to guide effective empirical therapy.
Purpose of the Study:
- To evaluate machine learning (ML) algorithms for predicting antimicrobial resistance in Klebsiella pneumoniae.
- To compare the performance of ML models against conventional methods for resistance testing.
- To identify the most effective ML algorithm for rapid resistance prediction.
Main Methods:
- Retrospective analysis of 607 Klebsiella pneumoniae isolates (2017-2024) with antibiogram data.
- Integration of patient demographic and clinical information with isolate data.
- Application and evaluation of Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms.
Main Results:
- The Random Forest (RF) model demonstrated superior performance, achieving an average AUC of 0.96.
- RF accurately predicted resistance to 22 antibiotics, including critical ones like Ertapenem (100%), Imipenem (93%), and Meropenem (95%).
- Feature importance metrics highlighted key predictors for resistance.
Conclusions:
- Machine learning models, particularly RF, offer a powerful tool for rapid antibiotic resistance prediction.
- These models can significantly aid in making accurate empirical treatment decisions.
- ML-based prediction supports enhanced antimicrobial stewardship programs.
