AI-driven tool for CLABSI risk prediction
Abstract:
Background: Central Line-Associated Bloodstream Infections (CLABSI) are major causes of morbidity and mortality in intensive care units (ICUs). This study aimed to develop an artificial intelligence (AI)-driven predictive model for CLABSI within two calendar days of central line insertion using routine biochemical parameters for early detection.
Methods: A retrospective analysis of adult ICU patients with central lines was conducted. Demographic and biochemical parameters were collected. Feature selection using Recursive Feature Elimination (RFE) identified key predictors. Four models-Extreme Gradient Boosting (XGBoost), logistic regression, support vector machine (SVM), and random forest-were trained and validated.
Results: Among 234 patients, 39 were CLABSI-positive. SVM demonstrated the highest predictive power (area under receiver operating characteristic curve = 0.91) and diagnostic odds ratio (DOR = 45.34). Seven key predictors were identified: prothrombin time days 1 and 2, international normalized ratio day 2, sodium day 1, potassium day 2, neutrophil-to-lymphocyte ratio day 1, and urea/creatinine ratio day 2. Decision curve analysis showed an estimated risk stratification at a 23% cutoff “https://clabpredicu.netlify.app/”.
Conclusion: The developed AI model shows strong potential for early CLABSI prediction using routine blood parameters. Future studies should focus on external validation and broader clinical application to enhance early infection prevention, particularly in resource-limited settings.
Reference:
Lahariya MR, Anand G, Sarfraz A, Tiewsoh JBA, Kumar A. CLABpredICU – AI-driven risk prediction for CLABSI in intensive care units based on clinical and biochemical parameters. Am J Infect Control. 2025 May 28:S0196-6553(25)00402-X. doi: 10.1016/j.ajic.2025.05.016. Epub ahead of print. PMID: 40447194.