Abstract
Background: Mechanically ventilated patients are susceptible to nosocomial infections such as ventilator-associated pneumonia. To treat ventilated patients with suspected infection, clinicians select appropriate antibiotics. However, decision-making regarding the use of antibiotics for methicillin-resistant Staphylococcus aureus (MRSA) is challenging, because of the lack of evidence-supported criteria. This study aims to derive a machine learning model to predict MRSA as a possible pathogen responsible for infection in mechanically ventilated patients.Methods: Data were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database (an openly available database of patients treated at the Beth Israel Deaconess Medical Center in the period 2008–2019). Of 26,409 mechanically ventilated patients, 809 were screened for MRSA during the mechanical ventilation period and included in the study. The outcome was positivity to MRSA on screening, which was highly imbalanced in the dataset, with 93.9% positive outcomes. Therefore, after dividing the dataset into a training set (n = 566) and a test set (n = 243) for validation by stratified random sampling with a 7:3 allocation ratio, synthetic datasets with 50% positive outcomes were created by synthetic minority over-sampling for both sets individually (synthetic training set: n = 1,064; synthetic test set: n = 456). Using these synthetic datasets, we trained and validated an XGBoost machine learning model using 28 predictor variables for outcome prediction. Model performance was evaluated by area under the receiver operating characteristic (AUROC), sensitivity, specificity, and other statistical measurements. Feature importance was computed by the Gini method.Results: In validation, the XGBoost model demonstrated reliable outcome prediction with an AUROC value of 0.89 [95% confidence interval (CI): 0.83–0.95]. The model showed a high sensitivity of 0.98 [CI: 0.95–0.99], but a low specificity of 0.47 [CI: 0.41–0.54] and a positive predictive value of 0.65 [CI: 0.62–0.68]. Important predictor variables included admission from the emergency department, insertion of arterial lines, prior quinolone use, hemodialysis, and admission to a surgical intensive care unit.Conclusions: We were able to develop an effective machine learning model to predict positive MRSA screening during mechanical ventilation using synthetic datasets, thus encouraging further research to develop a clinically relevant machine learning model for antibiotics stewardship.
Highlights
Selection of antibiotics for critically-ill patients undergoing mechanical ventilation in the intensive care unit (ICU) is challenging [1, 2], as these patients are susceptible to nosocomial infections such as ventilator-associated pneumonia (VAP), catheter-related blood site infection, and catheter-associated urinary tract infection [3,4,5]
This publicly available relational database is provided by the Laboratory for Computational Physiology at the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), and includes information on critical care patients who were admitted to the ICU at the Beth Israel Deaconess Medical Center (BIDMC, Boston, MA, USA) during the period 2008–2019
A smaller fraction of patients admitted from emergency department (ED) or hospitalized in the coronary care unit (CCU) was present in the synthetic training data compared with the synthetic validation data (41.3% vs. 54.4% and 5.6% vs. 13.8%, respectively)
Summary
Selection of antibiotics for critically-ill patients undergoing mechanical ventilation in the intensive care unit (ICU) is challenging [1, 2], as these patients are susceptible to nosocomial infections such as ventilator-associated pneumonia (VAP), catheter-related blood site infection, and catheter-associated urinary tract infection [3,4,5]. The inappropriate use of broad-spectrum antibiotics could lead to the emergence of resistant bacteria [6, 7]. The decision-making regarding the use of antibiotics for methicillin-resistant staphylococcus aureus (MRSA) is a source of distress for clinicians, due to their harmful complications such as hypersensitivity reactions, neutropenia, thrombocytopenia, and acute kidney injury [9,10,11]. Ventilated patients are susceptible to nosocomial infections such as ventilator-associated pneumonia. To treat ventilated patients with suspected infection, clinicians select appropriate antibiotics. Decision-making regarding the use of antibiotics for methicillin-resistant Staphylococcus aureus (MRSA) is challenging, because of the lack of evidence-supported criteria. This study aims to derive a machine learning model to predict MRSA as a possible pathogen responsible for infection in mechanically ventilated patients
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