***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: People with diabetes are more likely to be hospitalized and to have longer duration s of hospital stay than those without diabetes . A recent survey estimated that 22 % of all hospital inpatient days were incurred by people with diabetes and that hospital inpatient care accounted for half of the 174 billion USD total U.S. medical expenditures for this disease ( 1 ) . These findings are due , in part , to the continued expansion of the worldwide epidemic of type 2 diabetes . In the U.S. alone , there are ∼1.6 million new cases of diabetes each year , with an over all prevalence of 23.6 million people ( 7.8 % of the population , with one-fourth of the cases remaining undiagnosed ) . An additional 57 million American adults are at high risk for type 2 diabetes ( 2 ) . Although the costs of illness-related stress hyperglycemia are not known , they are likely to be considerable in light of the poor prognosis of such patients ( 3–6 ) . There is substantial observational evidence linking hyperglycemia in hospitalized patients ( with or without diabetes ) to poor outcomes . Cohort studies as well as a few early r and omized controlled trials ( RCTs ) have suggested that intensive treatment of hyperglycemia improved hospital outcomes ( 5–8 ) . In 2004 , this evidence led the American College of Endocrinology ( ACE ) and the American Association of Clinical Endocrinologists ( AACE ) , in collaboration with the American Diabetes Association ( ADA ) and other medical organizations , to develop recommendations for treatment of inpatient hyperglycemia  ( 9 ) . In 2005 , the ADA added recommendations for treatment  of hyperglycemia in the hospitalto itsannual St and ards of Medical Care ( 10 ) . Recommendations from the ACE and the ADA generally endorsed tight glycemic control in critical care units . For patients in general medical and surgical units , where RCT evidence regarding treatment targets was lacking , glycemic goals similar to those advised for out patients were advocated ( 9 , Type 2 diabetes is associated with altered immune and hemostatic responses . We investigated the selective effects of hyperglycemia and hyperinsulinemia on innate immune , coagulation , and fibrinolytic responses during systemic inflammation . Twenty-four healthy humans were studied for 8 hours during clamp experiments in which either plasma glucose , insulin , both , or none was increased , depending on r and omization . Target plasma concentrations were 5 versus 12 mM for glucose , and 100 versus 400 pmol/L for insulin . After 3 hours , 4 ng/kg Escherichia coli endotoxin was injected intravenously to induce a systemic inflammatory and procoagulant response . Endotoxin administration induced cytokine release , activation of neutrophils , endothelium and coagulation , and inhibition of fibrinolysis . Hyperglycemia reduced neutrophil degranulation ( plasma elastase levels , P < .001 ) and exaggerated coagulation ( plasma concentrations of thrombin-antithrombin complexes and soluble tissue factor , both P < .001 ) . Hyperinsulinemia attenuated fibrinolytic activity due to elevated plasminogen activator-inhibitor-1 levels ( P < .001 ) . Endothelial cell activation markers and cytokine concentrations did not differ between clamps . We conclude that in humans with systemic inflammation induced by intravenous endotoxin administration hyperglycemia impairs neutrophil degranulation and potentiates coagulation , whereas hyperinsulinemia inhibits fibrinolysis . These data suggest that type 2 diabetes patients may be especially vulnerable to prothrombotic events during inflammatory states BACKGROUND Glycemic control is important to patients ' outcomes . However , the process of maintaining glycemic control is risk laden and labor intensive for nurses . OBJECTIVES To examine the effects of using a computerized insulin dose calculator to facilitate management of glycemic control for critically ill cardiac patients . METHODS A prospect i ve r and omized controlled trial was conducted with a sample of 300 intensive care patients , 141 r and omized to the calculator group and 159 in the control ( paper protocol ) group . A convenience sample of 44 intensive care nurses responded to a nurse satisfaction survey . RESULTS A significantly higher percentage of glucose measurements were in the target range in the calculator group than in the control group ( 70.4 % [ SD , 15.2 % ] vs 61.6 % [ SD , 17.9 % ] , Z = -4.423 , P < .001 ) , and glucose variance was significantly less in the calculator group ( 35.5 [ SD , 18.3 ] mg/dL vs 42.3 [ SD , 21.2 ] mg/dL , Z = -3.845 , P < .001 ) . Fewer hypoglycemic events occurred in the calculator group ( 7 vs 18 ) , although this difference was not statistically significant . Nurse satisfaction was higher for the calculator group than for the control group ( 8.4 [ SD , 1.4 ] vs 4.8 [ SD , 2.4 ] , Z = -5.055 , P < .001 ) . Nurses ' deviation from the protocol was also less in the calculator group than in the control group . CONCLUSIONS Management of glycemic control and nurse satisfaction were improved with use of the dose calculator . Improving nurses ' processes of care may improve nurses ' use of time and patient care overall . Studies with larger sample sizes over time are needed to determine these relationships BACKGROUND Intensive glycemic control has been associated with reduced morbidity and mortality in critically ill patients . Web-based , patient-specific insulin nomograms may facilitate improved glucose control . OBJECTIVE To compare 2 algorithms for individualizing insulin infusion therapy ( a web-based system [ Glucomm and er method ] and a st and ard paper-based nomogram ) in a cardiovascular surgery intensive care unit ( ICU ) . METHODS In this prospect i ve , before-after cohort study , measures of glycemic control for 50 patients receiving insulin according to the Glucomm and er system  were compared with a control group ( n = 50 ) who received insulin according to the st and ard paper-based nomogram used in the cardiovascular surgery ICU . RESULTS There was no significant difference between the 2 groups with respect to time to target blood glucose ( 5.1 - 8.0 mmol/L ) , percentage of time within the target range , or mean amplitude of glucose excursion . Patients in the intervention group spent less time above the target range ( p = 0.007 ) and more time below the target range ( p < 0.001 ) , and the mean glucose was lower in this group compared with the control group ( 7.9 versus 8.6 mmol/L , p = 0.002 ) . The percentage of blood glucose measurements below 4 mmol/L was higher in the intervention group than in the control group ( 3.7 % versus 1.4 % , p = 0.003 ) . Satisfaction surveys revealed that the program was well accepted by the nursing staff in the cardiovascular surgery ICU . CONCLUSIONS A web-based insulin nomogram was an easy-to-use instrument for achieving tighter glucose control for patients in the cardiovascular surgery ICU . Use of the Glucomm and er system led to lower mean blood glucose but an increase in episodes of hypoglycemia BACKGROUND : In the setting of protocol ized glycemic control , the relationship between postoperative glycemic variability on major adverse events ( MAEs ) after cardiac surgery is unknown for patients with increased preoperative hemoglobin A1C ( HbA1C > 6.5 % ) . In this study , we sought to establish ( a ) whether postoperative glycemic variability is associated with MAEs after CABG surgery and ( b ) whether preoperative HbA1C could identify patients at increased risk of postoperative glycemic variability . METHODS : Patients undergoing coronary artery bypass grafting with or without valvular surgery from January 2008 to May 2011 were enrolled in this prospect i ve , single-center , observational cohort study . Demographic , intraoperative , and postoperative outcome data were obtained from institutional data collected for the Society of Thoracic Surgery ( STS ) data base . The primary outcome , MAE was a composite of in-hospital death , myocardial infa rct ion ( MI ) , reoperations , sternal infection , cardiac tamponade , pneumonia , stroke , or renal failure . Glycemic variability in the postoperative period was assessed by the coefficient of variation ( CV ) . CV was used as quartiles for the multivariate logistic regression . Variable selection in multivariable modeling was based on clinical and statistical significance and was performed in a hierarchical fashion . RESULTS : Of the 1461 patients enrolled , 9.8 % had an MAE . Based on the established target of HbA1C < 6.5 % for the diagnosis of diabetes mellitus , we considered HbA1C as a binary variable ( < 6.5 % and ≥6.5 % ) in our primary analysis . Multivariate logistic regression analyses for the preoperative variables only revealed that preoperative HbA1C ( odds ratio [ OR ] , 1.6 ; 95 % confidence interval [ CI ] , 1.1–2.3 ; P = 0.02 ) , history of MI ( OR , 1.9 ; 95 % CI , 1.3–2.8 ; P = 0.001 ) , and STS risk score per quartile ( OR , 1.7 ; 95 % CI , 1.4–2.1 ; P < 0.001 ) were associated with MAEs . When postoperative variables were included in the analyses , postoperative glycemic variability ( CV per quartile ) in the intensive care unit ( OR , 1.3 ; 95 % CI , 1.1–1.5 ; P = 0.03 ) , mean glucose levels averaged over the first 4 postoperative hours ( OR , 1.2 ; 95 % CI , 1.0–1.4 ; P = 0.03 ) , history of MI ( OR , 1.8 ; 95 % CI , 1.2–2.6 ; P = 0.004 ) , and STS risk score per quartile ( OR , 1.6 ; 95 % CI , 1.3–2.0 ; P < 0.001 ) were associated with MAEs . Glycemic variability as assessed by CV was increased postoperatively in patients with preoperative HbA1C ≥6.5 % ( 0.20 ± 0.09 vs 0.16 ± 0.07 , P < 0.001 ) . CONCLUSIONS : Postoperative glycemic variability is associated with MAEs after cardiac surgery . Glycemic variability is only measured when the patient leaves the intensive care unit , and there is no opportunity to intervene earlier . Preoperative HbA1C identifies risk for postoperative glycemic variability and may provide a more rational guide for targeting measures to reduce variability OBJECTIVE We performed a r and omized trial to compare three insulin-titration protocol s for tight glycemic control ( TGC ) in a surgical intensive care unit : an absolute glucose ( Matias ) protocol , a relative glucose change ( Bath ) protocol , and an enhanced model predictive control ( eMPC ) algorithm . RESEARCH DESIGN AND METHODS A total of 120 consecutive patients after cardiac surgery were r and omly assigned to the three protocol s with a target glycemia range from 4.4 to 6.1 mmol/l . Intravenous insulin was administered continuously or in combination with insulin boluses ( Matias protocol ) . Blood glucose was measured in 1- to 4-h intervals as requested by the protocol s. RESULTS The eMPC algorithm gave the best performance as assessed by time to target ( 8.8 ± 2.2 vs. 10.9 ± 1.0 vs. 12.3 ± 1.9 h ; eMPC vs. Matias vs. Bath , respectively ; P < 0.05 ) , average blood glucose after reaching the target ( 5.2 ± 0.1 vs. 6.2 ± 0.1 vs. 5.8 ± 0.1 mmol/l ; P < 0.01 ) , time in target ( 62.8 ± 4.4 vs. 48.4 ± 3.28 vs. 55.5 ± 3.2 % ; P < 0.05 ) , time in hyperglycemia > 8.3 mmol/l ( 1.3 ± 1.2 vs. 12.8 ± 2.2 vs. 6.5 ± 2.0 % ; P < 0.05 ) , and sampling interval ( 2.3 ± 0.1 vs. 2.1 ± 0.1 vs. 1.8 ± 0.1 h ; P < 0.05 ) . However , time in hypoglycemia risk range ( 2.9–4.3 mmol/l ) in the eMPC group was the longest ( 22.2 ± 1.9 vs. 10.9 ± 1.5 vs. 13.1 ± 1.6 ; P < 0.05 ) . No severe hypoglycemic episode ( < 2.3 mmol/l ) occurred in the eMPC group compared with one in the Matias group and two in 
    Output:
    
There is some evidence to support the use of computerised insulin dosing calculators for insulin infusion management within critical care environments