Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software.

Conservative management of esophageal perforation after a fall.

Introduction: Esophageal perforation in the setting of blunt trauma is rare, and diagnosis can be difficult due to atypical signs and symptoms accompanied by distracting injury.

Presentation Of Case: We present a case of esophageal perforation resulting from a fall from height. Unexplained air in the soft tissues planes posterior to the esophagus as well as subcutaneous emphysema in the absence of a pneumothorax on CT aroused clinical suspicions of an injury to the aerodigestive tract.

Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

Background: Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose.