Publications by authors named "Shmuel Teppler"
Article Synopsis
- Acinetobacter baumanni infections are common and serious in ICUs, making early detection crucial for better patient outcomes.
- This study developed a Machine Learning prediction tool using data from nearly 20,000 ICU patients to identify those at risk for these infections.
- The tool showed moderate predictive ability, with key risk factors being respiratory function, metabolic issues, and antibiotic use, suggesting areas for improving prediction accuracy in the future.*
Introduction: The decision to intubate and ventilate a patient is mainly clinical. Both delaying intubation (when needed) and unnecessarily invasively ventilating (when it can be avoided) are harmful. We recently developed an algorithm predicting respiratory failure and invasive mechanical ventilation in COVID-19 patients.
View Article and Find Full Text PDFIn hypoxemic patients at risk for developing respiratory failure, the decision to initiate invasive mechanical ventilation (IMV) may be extremely difficult, even more so among patients suffering from COVID-19. Delayed recognition of respiratory failure may translate into poor outcomes, emphasizing the need for stronger predictive models for IMV necessity. We developed a two-step model; the first step was to train a machine learning predictive model on a large dataset of non-COVID-19 critically ill hypoxemic patients from the United States (MIMIC-III).
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