Adult Sepsis Guidelines Children's Sepsis Guidelines
Adult ICU Liberation Guidelines PANDEM Guidelines for Children and Infants
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This podcast discusses a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. The discussion centers on the article “Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU,” published in the July 2022 issue of Pediatric Critical Care Medicine.
The PANDEM guidelines evaluate current practices and provide recommendations for management of pain, agitation, iatrogenic withdrawal, neuromuscular blockade, delirium, ICU environment, and early mobility in critically ill infants and children. Host Margaret M. Parker, MD, MCCM, is joined by Heidi A. B. Smith, MD, MSCI, FAAP, to discuss the guidelines.
In the ICU, medical staff do all they can to assist patients and get them back to health as quickly as possible. In the process of saving lives, bedside manner and communication may suffer. Lauren Rissman, MD, discusses the eye-opening experience she had when she was admitted to the ICU from the labor and delivery unit and the importance of having a patient advocate.
Host Margaret M. Parker, MD, MCCM is joined by Alon Geva, MD, MPH, to discuss how the implementation of eSIMPLER provided clinical decision support prompts with display of relevant data automatically pulled from the electronic health record and improved certain care processes.
Host Elizabeth Mack, MD, MS, FCCM, is joined by Michael Fundora, MD, FAAP, to discuss if the hypothesized frontline clinician workload, measured by bed occupancy and staffing, is associated with poor outcomes and unnecessary testing.
As a proof of concept, a recurrent neural network (RNN) model was developed using electronic medical record (EMR) data capable of continuously assessing a child’s risk of mortality throughout an ICU stay as a proxy measure of illness severity. Host Margaret M. Parker, MD, MCCM, is joined by Melissa D. Aczon, PhD, to discuss how the RNN model can process hundreds of input variables contained in a patient’s EMR and integrate them dynamically as measurements become available.