Evidence map›Paper›PMID 26819233›Full record

ArticleTheoretical biology & medical modelling2016

Simulation and qualitative analysis of glucose variability, mean glucose, and hypoglycemia after subcutaneous insulin therapy for stress hyperglycemia.

Richard J Strilka, Mamie C Stull, Michael S Clemens, Stewart C McCaver, Scott B Armen

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Article in Theoretical biology & medical modelling, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Richard J StrilkaDepartment of Trauma and Critical Care Surgery, San Antonio Military Medical Center, 3551 Roger Brooke Drive, Fort Sam Houston, San Antonio, TX, USA. rich.strilka.md@gmail.com.
Mamie C StullDepartment of Trauma and Critical Care Surgery, San Antonio Military Medical Center, 3551 Roger Brooke Drive, Fort Sam Houston, San Antonio, TX, USA. mamiestull@gmail.com.
Michael S ClemensDepartment of Trauma and Critical Care Surgery, San Antonio Military Medical Center, 3551 Roger Brooke Drive, Fort Sam Houston, San Antonio, TX, USA. michael.s.clemens@gmail.com.
Stewart C McCaverWalter Reed National Military Medical Center, 8901 Rockville Pike, Bethesda, MD, USA. stewart.mccarver@gmail.com.
Scott B ArmenDivision of Trauma, Acute Care and Critical Care Surgery, Pennsylvania State College of Medicine, 500 University Drive, Hershey, PA, USA. sarmen@hmc.psu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe critically ill can have persistent dysglycemia during the "subacute" recovery phase of their illness because of altered gene expression; it is also not uncommon for these patients to receive continuous enteral nutrition during this time. The optimal short-acting subcutaneous insulin therapy that should be used in this clinical scenario, however, is unknown. Our aim was to conduct a qualitative numerical study of the glucose-insulin dynamics within this patient population to answer the above question. This analysis may help clinicians design a relevant clinical trial.

methodsEight virtual patients with stress hyperglycemia were simulated by means of a mathematical model. Each virtual patient had a different combination of insulin resistance and insulin deficiency that defined their unique stress hyperglycemia state; the rate of gluconeogenesis was also doubled. The patients received 25 injections of subcutaneous regular or Lispro insulin (0-6 U) with 3 rates of continuous nutrition. The main outcome measurements were the change in mean glucose concentration, the change in glucose variability, and hypoglycemic episodes. These end points were interpreted by how the ultradian oscillations of glucose concentration were affected by each insulin preparation.

resultsSubcutaneous regular insulin lowered both mean glucose concentrations and glucose variability in a linear fashion. No hypoglycemic episodes were noted. Although subcutaneous Lispro insulin lowered mean glucose concentrations, glucose variability increased in a nonlinear fashion. In patients with high insulin resistance and nutrition at goal, "rebound hyperglycemia" was noted after the insulin analog was rapidly metabolized. When the nutritional source was removed, hypoglycemia tended to occur at higher Lispro insulin doses. Finally, patients with severe insulin resistance seemed the most sensitive to insulin concentration changes.

conclusionsSubcutaneous regular insulin consistently lowered mean glucose concentrations and glucose variability; its linear dose-response curve rendered the preparation better suited for a sliding-scale protocol. The longer duration of action of subcutaneous regular insulin resulted in better glycemic-control metrics for patients who were continuously postprandial. Clinical trials are needed to examine whether these numerical results represent the glucose-insulin dynamics that occur in intensive care units; if present, their clinical effects should be evaluated.

Indexed as

Blood GlucoseCircadian RhythmComputer SimulationCritical IllnessGlucoseHumansHyperglycemiaHypoglycemiaInsulinInsulin LisproInsulin ResistanceModels, TheoreticalOscillometryTreatment OutcomeBlood GlucoseGlucoseInsulinInsulin Lispro

Identifiers

PMID26819233
PMCPMC4728764

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.