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"A machine learning model can be used to predict 25% of patients with impending CLABSI with only 1.1/1,000 of these predictions being incorrect" Bonello et al (2022).

Predicting CLABSI

Abstract:

Background: While modeling of central line-associated blood stream infection (CLABSI) risk factors is common, models that predict an impending CLABSI in real time are lacking.

Aim: To build a prediction model which identifies patients who will develop a CLABSI in the ensuing 24 hours.

Methods: We collected variables potentially related to infection identification in all patients admitted to the cardiac ICU or cardiac ward at Boston Children’s Hospital in whom a central venous catheter (CVC) was in place between January 2010 and August 2020, excluding those with a diagnosis of bacterial endocarditis. We created models predicting whether a patient would develop CLABSI in the ensuing 24 hours. We assessed model performance based on area under the curve (AUC), sensitivity, and false positive rate (FPR) of models run on an independent testing set (40%).

Findings: 104,035 patient-days and 139,662 line-days corresponding to 7,468 unique patients were included in the analysis. There were 399 positive blood cultures (0.38%), most commonly with Staphylococcus aureus (23% of infections). Major predictors included a prior history of infection, elevated maximum heart rate, elevated maximum temperature, elevated C-reactive protein, exposure to parenteral nutrition, and use of alteplase for CVC clearance. The model identified 25% of positive cultures with an FPR of 0.11% (AUC = 0.82).

Conclusions: A machine learning model can be used to predict 25% of patients with impending CLABSI with only 1.1/1,000 of these predictions being incorrect. Once prospectively validated, this tool may allow for early treatment or prevention.


Reference:

Bonello K, Emani S, Sorensen A, Shaw L, Godsay M, Delgado M, Sperotto F, Santillana M, Kheir JN. Prediction of Impending Central Line Associated Bloodstream Infections in Hospitalized Cardiac Patients: Development and Testing of a Machine-Learning Model. J Hosp Infect. 2022 Jun 20:S0195-6701(22)00190-6. doi: 10.1016/j.jhin.2022.06.003. Epub ahead of print. PMID: 35738317.