Literature DB >> 24641919

Association of glycemic control parameters with clinical outcomes in chronic critical illness.

Rifka C Schulman1, Erin L Moshier2, Lisa Rho3, Martin F Casey2, James H Godbold2, Jeffrey I Mechanick4.   

Abstract

OBJECTIVE: Chronic critical illness (CCI) is a term used to designate patients requiring prolonged mechanical ventilation and tracheostomy with associated poor outcomes. The present study assessed the impact of glycemic parameters on outcomes in a CCI population.
METHODS: A retrospective case series was performed including 148 patients in The Mount Sinai Hospital Respiratory Care Unit (2009-2010). Utilizing a semi-parametric mixture model, trajectories for the daily mean blood glucose (BG), BG range, and hypoglycemia rate over time identified low- (n = 87) and high-risk (n = 61) hyperglycemia groups and low- (n = 90) and high-risk (n = 58) hypoglycemia groups. The cohort was also classified into diabetes (DM, n = 48), stress hyperglycemia (SH, n = 85), and normal glucose (n = 15) groups.
RESULTS: Hospital- (28% vs. 13%, P = .0199) and 1-year mortality (66% vs. 46%, P = .0185) rates were significantly greater in the high- versus low-risk hyperglycemia groups, respectively. The hypoglycemia rate (<70 mg/dL) was lower among ventilator-liberated patients compared to those who failed to liberate (0.092 vs. 0.130, P<.0001). In the SH group, both hospital mortality (high-risk hyperglycemia 48% and low-risk hyperglycemia 15%, P = .0013) and 1-year mortality (high-risk 74% and low-risk 50%, P = .0482) remained significantly different, while no significant difference in the diabetes group was observed. There were lower hypoglycemia rates with SH compared to diabetes (<70 mg/dL: 0.086 vs. 0.182, P<.0001; <40 mg/dL: 0.012 vs. 0.022, P = .0118, respectively).
CONCLUSION: Tighter glycemic control was associated with improved outcomes in CCI patients with SH but not in CCI patients with diabetes. Confirmation of these findings may lead to stratified glycemic control protocols in CCI patients based on the presence or absence of diabetes.

Entities:  

Year:  2014        PMID: 24641919     DOI: 10.4158/EP13324.OR

Source DB:  PubMed          Journal:  Endocr Pract        ISSN: 1530-891X            Impact factor:   3.443


  3 in total

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Authors:  Omid Veiseh; Benjamin C Tang; Kathryn A Whitehead; Daniel G Anderson; Robert Langer
Journal:  Nat Rev Drug Discov       Date:  2014-11-28       Impact factor: 84.694

Review 2.  Nanomedicine-Based Strategies for Diabetes: Diagnostics, Monitoring, and Treatment.

Authors:  Luke R Lemmerman; Devleena Das; Natalia Higuita-Castro; Raghavendra G Mirmira; Daniel Gallego-Perez
Journal:  Trends Endocrinol Metab       Date:  2020-03-04       Impact factor: 12.015

3.  Stimuli-Responsive Delivery of Therapeutics for Diabetes Treatment.

Authors:  Jicheng Yu; Yuqi Zhang; Hunter Bomba; Zhen Gu
Journal:  Bioeng Transl Med       Date:  2016-10-03
  3 in total

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