Early detection of Vascular Catheter-Associated Infections employing supervised machine learning - A case study in Lleida Region

Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasive infection is crucial for improving hospital care and reducing antibiotic use. This study retrospectively developed reliable machine learning models to clasify VCAIs from patient medical records, excluding fever and antibiotic prescription indicators. The dataset, collected from the group of public hospitals of the Lleida health region in Catalonia (Spain) between 2011 and 2019, consisted of 24,239 episodes with 150 features related to vascular catheter use. After validation, processing and feature engineering, the dataset showed an imbalance, with 94.46% (10,090) non-catheter episodes and 5.53% (591) catheter infection cases. The study’s results underscore that classifiers have demonstrated respectable performance postpreprocessing and feature engineering despite the initial imbalance within the dataset, with balanced accuracies ranging between 70% and 80%. Notably, the Decision Tree (DT) classifier emerged as the frontrunner, achieving 82%, thereby validating its effectiveness. While various oversampling strategies, such as SMOTE, were explored, these did not significantly strengthen the performance beyond what was achieved using DT alone. This study highlights that strategic feature engineering with the DT classifier is sufficient to obtain robust VCAI detection before the apparition of a probable sepsis.