ArticleJournal of diabetes science and technology2008
Model-based insulin sensitivity as a sepsis diagnostic in critical care.
Article in Journal of diabetes science and technology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 36 citations in OpenAlex.
- Data entry errors and design for model-based tight glycemic control in critical care.Journal of diabetes science and technology · 2012Trial
- Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance.Journal of diabetes science and technology · 2022Article
- Estimating Increased EGP During Stress Response in Critically Ill Patients.Journal of diabetes science and technology · 2021Article
- Levels and Diagnostic Value of Model-based Insulin Sensitivity in Sepsis: A Preliminary Study.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2018Article
- Increased FGF21 plasma levels in humans with sepsis and SIRS.Endocrine connections · 2013Article
- Interface design and human factors considerations for model-based tight glycemic control in critical care.Journal of diabetes science and technology · 2012Article
- Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?Annals of intensive care · 2011Article
- Validation of a model-based virtual trials method for tight glycemic control in intensive care.Biomedical engineering online · 2010Article
- DISTq: An Iterative Analysis of Glucose Data for Low-Cost, Real-Time and Accurate Estimation of Insulin Sensitivity.The open medical informatics journal · 2009Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTimely diagnosis and treatment of sepsis in critical care require significant clinical effort, experience, and resources. Insulin sensitivity is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to identify insulin sensitivity in real time.
methodsReceiver operating characteristic curves and cutoff insulin sensitivity values for diagnosing sepsis were calculated for model-based insulin sensitivity (S(I)) and a simpler metric (SS(I)) that was estimated from glycemic control data of 30 patients with sepsis and can be calculated in real time without use of a computer. Results were compared to the insulin sensitivity profiles of a general intensive care unit population of 113 patients without sepsis and 30 patients with sepsis, comprising a total of 26,453 patient hours. Patients with sepsis were identified as having sepsis based on a sepsis score (ss) of 3 or higher (ss = 0 - 4 for increasing severity). Patients with type I or type II diabetes were excluded. Ethics approval for this study was granted by the South Island Regional Ethics Committee.
resultsReceiver operating characteristic cutoff values of S(I) = 8 x 10-5 liter mU(-1) min(-1) and SS(I) = 2.8 x 10-4 liter mU(-1) min(-1) were determined for ss > or = 3. The model-based S(I) fell below this value in 15% of all patient hours. The S(I) test had a negative predictive value of 99.8%. The test sensitivity was 78% and specificity was 82%. However, the positive predictor value was 2.8%. Slightly lower sensitivity (68.8%) and specificity (81.7%), but equally good negative prediction (99.7%), were obtained for the estimated SS(I).
conclusionsInsulin sensitivity provides a negative predictive diagnostic for sepsis. High insulin sensitivity rules out sepsis for the majority of patient hours and may be determined noninvasively in real time from glycemic control protocol data. Low insulin sensitivity is not an effective diagnostic, as it can equally mark the presence of sepsis or other conditions.
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