Ultradian variation of blood glucose in intensive care unit patients receiving insulin infusions

Pulmonary and Critical Care Medicine, Biomedical Research Building (UHN-67), 3181 SW Sam Jackson Park Rd., Oregon Health and Science University, Portland, OR 97239-3098, USA.
Diabetes care (Impact Factor: 8.57). 11/2007; 30(10):2503-5. DOI: 10.2337/dc07-0865
Source: PubMed
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    ABSTRACT: OBJECTIVE:: Circadian rhythms are intrinsic timekeeping mechanisms that allow for adaptation to cyclic environmental changes. Increasing evidence suggests that circadian rhythms may influence progression of a variety of diseases as well as effectiveness and toxicity of drugs commonly used in the intensive care unit. In this perspective, we provide a brief review of the molecular mechanisms of circadian rhythms and its relevance to critical care. DATA SOURCES, STUDY SELECTION, DATA EXTRACTION, AND DATA SYNTHESIS:: Articles related to circadian rhythms and organ systems in normal and disease conditions were searched through the PubMed library with the goal of providing a concise review. CONCLUSIONS:: Critically ill patients may be highly vulnerable to disruption of circadian rhythms as a result of the severity of their underlying diseases as well as the intensive care unit environment where noise and frequent therapeutic/diagnostic interventions take place. Further basic and clinical research addressing the importance of circadian rhythms in the context of critical care is warranted to develop a better understanding of the complex pathophysiology of critically ill patients as well as to identify novel therapeutic approaches for these patients.
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    ABSTRACT: 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. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.
    PLoS ONE 07/2013; 8(7):e69475. DOI:10.1371/journal.pone.0069475 · 3.53 Impact Factor