Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 33  |  Issue : 3  |  Page : 794-800

Study of effect of glycemic gap on adverse outcomes in critically ill patients with diabetes


1 Department of Internal Medicine, Faculty of Medicine, Menoufia University, Menoufia, Egypt
2 Department of Internal Medicine, Banha Educational Hospital, Banha, Al Qalyubiya, Egypt

Date of Submission20-Jan-2020
Date of Decision12-Apr-2020
Date of Acceptance04-May-2020
Date of Web Publication30-Sep-2020

Correspondence Address:
Adel A. S. EL Eslam
Department of Internal Medicine, Faculty of Medicine, Yassin Abdel Ghafar street from Gamal Abdel Naser Street, Shebin El Kom, Menoufia
Egypt
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mmj.mmj_16_20

Rights and Permissions
  Abstract 


Objective
To study if higher levels of glycemic gap can be used as a tool to predict adverse outcomes in patients with diabetes mellitus admitted with critical illness.
Background
The glycemic gap is calculated as a difference between the A1C-derived average glucose and the admission glucose and may be a better reflector of outcomes.
Patients and methods
This study was conducted on 150 patients with type 2 diabetes mellitus who were admitted to the ICUs of Menoufia University Hospitals and Benha Teaching Hospital. Full detailed history, Simplified Acute Physiologic Score II, Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation II score, glycated hemoglobin, and glycemic gap were assessed.
Results
The best cutoff value for prediction of adverse outcomes in critically ill patients with diabetes for random blood sugar on admission was 329, with sensitivity of 98.8% and specificity of 89.5%; glycated hemoglobin was 11.88, with sensitivity of 99% and specificity of 98.3%; A1C-derived average glucose was 295.12, with sensitivity of 98.8% and specificity of 98.3%; glycemic gap was 64.25, with sensitivity of 62.5% and specificity of 60%; Acute Physiology and Chronic Health Evaluation II score was 37.0, with sensitivity of 92.2% and specificity of 90.5%; Simplified Acute Physiologic Score II score was 30, with sensitivity of 90% and specificity of 88.8%; and Sequential Organ Failure Assessment score was 6.0, with sensitivity of 83.3% and specificity of 78.6%.
Conclusion
Higher glycemic gap levels were significantly associated with an increased risk of multiorgan dysfunction syndrome, acute respiratory distress syndrome, shock, upper gastrointestinal bleed, acute kidney injury, acute respiratory failure, as well as ICU mortality. The glycemic gap is a tool that may be used to assess the severity and prognosis of patients with type 2 diabetes admitted with critical illness.

Keywords: adverse outcomes, critical illness, diabetes mellitus, glycemic gap


How to cite this article:
Gazareen SS, EL Eslam AA, Zewain SK. Study of effect of glycemic gap on adverse outcomes in critically ill patients with diabetes. Menoufia Med J 2020;33:794-800

How to cite this URL:
Gazareen SS, EL Eslam AA, Zewain SK. Study of effect of glycemic gap on adverse outcomes in critically ill patients with diabetes. Menoufia Med J [serial online] 2020 [cited 2020 Nov 26];33:794-800. Available from: http://www.mmj.eg.net/text.asp?2020/33/3/794/296651




  Introduction Top


Diabetes mellitus (DM) is a chronic metabolic disorder characterized by persistent hyperglycemia. It may be due to impaired insulin secretion, resistance to peripheral actions of insulin, or both. Chronic hyperglycemia in association with the other metabolic changes in diabetic patients can cause damage to various organ systems, leading to the development of disabling and life-threatening health complications, most prominent of which are microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular complications [1]. Blood glucose levels at the time of admission to hospital are associated with adverse outcomes [2].

Studies have shown that the spikes in blood glucose over and above the previous existing values have more bearing on the outcomes. The following adverse outcomes were recorded: multiorgan dysfunction syndrome (dysfunction of more than one organ), acute respiratory distress syndrome [PaO2 (mmHg)/FiO2 <200 mmHg], shock (defined as persistent hypotension despite adequate fluid resuscitation), upper gastrointestinal bleed, acute kidney injury (defined as serum creatinine elevated >0.3 mg/dl or 50% from baseline), and acute respiratory failure (defined as the need for ventilatory support) [3].

The glycemic gap is calculated as a difference between the A1C-derived average glucose (ADAG) and the admission glucose and may be a better reflector of outcomes. The average glucose for the past 8–12 weeks will be derived from the formula for ADAG, calculated as ADAG = [28.7 × glycated hemoglobin (HbA1c)−46.7] [4]. Measurement of admission glucose in patients with diabetes may not correlate with stress levels as many have previously high glucose levels in the blood. To counter this fallacy, glycemic gap takes into account the HbA1c. HbA1c is not affected by stress or infection but can be affected by anemia, hemoglobinopathies, and other diseases [5].

Therefore, the aim of the current study was to study if higher levels of glycemic gap can be used as a tool to predict adverse outcomes in patients with DM admitted with critical illness.


  Patients and Methods Top


This study was conducted on 150 patients with type 2 diabetes mellitus (T2DM) who were admitted to the ICUs of Menoufia University Hospitals and Benha Teaching Hospital.

Ethical consideration

The study was approved by the ethical committee of Menoufia Faculty of Medicine hospital. An informed consent was obtained from all patient's guardian before the study was commenced.

Selection criteria for the patients

The patients included in this study were selected according to the inclusion and exclusion criteria.

Inclusion criteria

Patients with T2DM admitted to the ICUs were included.

Exclusion criteria

Age less than 18 years, hypoglycemia at admission, admission diagnosis of diabetic ketoacidosis/hyperosmolar hyperglycemic syndrome, treatment with corticosteroids, death within 24 h of admission, renal failure, acute and chronic blood loss, hemolytic anemia, known hemoglobin variants, pregnancy, patients with incomplete data, and hospital stay more than 180 days were the exclusion criteria.

Methods

All patients were subjected to the following procedures:

Complete medical history of risk factors: it was obtained from the patient, families, caregivers, or prior medical records including the following: background characteristics, including age and sex, and DM (use of insulin or oral hypoglycemic agents).

Full general and clinical examinations.

Routine laboratory tests: these included complete blood count.

Blood glucose level: it was measured calorimetrically by the glucose oxidase method.

Liver function tests [serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT)]: the used tests to check liver function in our study were the ALT and AST. It was measured indirectly by using a spectrophotometer.

Kidney function tests (serum urea and creatinine): urea and creatinine were measured spectrophotometrically.

Acute Physiology and Chronic Health Evaluation (APACHE) II scoring system: the APACHE prognostic scoring system was developed in 1981 at the George Washington University Medical Centre. It employs basic physiologic principles to stratify acutely ill adult patients by severity of illness. The APACHE II scoring system is a simplified version of the original APACHE system and consists of three sections: 12 acute physiologic variables, age, and chronic health status. The APACHE II score is determined by totaling points from these three sections, resulting in a total score between 0 and 71 points. Patients are assigned points based on the most deranged physiologic variables obtained on these assigned parameters during the patient's initial 24 h in an ICU setting. Chronological age and severity of preexisting chronic disease are also scored as they are thought to affect physiologic reserve and probability of survival during a period of acute illness [6].

Simplified Acute Physiologic Score II (SAPS II): SAPS II was designed to measure the severity of disease for patients admitted to ICU aged 15 or more. Following 24 h after admission to the ICU, the measurement has been completed and resulted in an integer point score between 0 and 163 and a predicted mortality between 0 and 100%. No new score can be calculated during the stay. If a patient is discharged from the ICU and readmitted, a new SAPS II score can be calculated. This scoring system is mostly used for the following:

To describe the morbidity of a patient when comparing the outcome with other patients.

To describe the morbidity of a group of patients when comparing the outcome with another group of patients [7].

Sequential Organ Failure Assessment (SOFA) score: SOFA score, previously known as the sepsis-related organ failure assessment score, is used to track a person's status during the stay in an ICU to determine the extent of a person's organ function or rate of failure. The score is based on six different scores, one each for the respiratory, cardiovascular, hepatic, coagulation, renal, and neurological systems [8].

Measurement of HbA1c and glycemic gap: on admission, the blood glucose level was measured. A blood analyzer (Primus CLC 385; Primus Corporation, Kansas City, Missouri, USA) was used to measure HbA1c. To convert HbA1c levels to the estimated long-term average glucose levels for the previous 3 months, the equation AG = 28.7 × HbA1c − 46.7 was used. From the glucose level at ED admission minus the estimated long-term average glucose levels, the glycemic gap was calculated. Patients were followed up until hospital discharge or death [4].

Statistical analysis

Results were tabulated and statistically analyzed by using a personal computer using Microsoft Excel 2016 and SPSS, version 20 Data were then imported into Statistical Package for the Social Sciences (SPSS Inc., Chicago, Illinois, USA). Statistical analysis was done using the following: descriptive, for example, percentage (%), mean and SD, and analytical, which included independent t-test, χ2, and receiver operating characteristic. A value of P value less than 0.05 was considered statistically significant and less than 0.001 for high significant result.


  Results Top


In the current study, age of nonsurvivor cases was significantly higher than survivor cases; it was distributed as 61.5 ± 6.11 and 49.0 ± 9.31 years, respectively. Although females were significantly higher in nonsurvivor cases, they were significantly lower in survivor cases. However, DM duration, ICU stay, and mechanical ventilation (MV) days were significantly higher among nonsurvivor cases than survivor cases [Table 1]. Moreover, patients who had insulin therapy were significantly lower than those who had oral antidiabetics among nonsurvivor cases. However, diabetic patients admitted to ICU with cardiovascular problems were significantly higher among nonsurvivor cases than survival cases [Table 2]. Our results indicated that random blood sugar admission, urea, creatinine, AST, ALT, HbA1c, ADAG, glycemic gap, APACHE II score, SAPS II score, and SOFA were significantly higher among nonsurvivor cases than survivor cases [Table 3]. In the current study, the best cutoff value for prediction of adverse outcomes in critically ill patients with diabetes for random blood sugar on admission was 329, with sensitivity of 98.8% and specificity of 89.5%; HbA1c was 11.88, with sensitivity of 99% and specificity of 98.3%; ADAG was 295.12, with sensitivity of 98.8% and specificity of 98.3%; glycemic gap was 64.25 with sensitivity of 62.5% and specificity of 60%; APACHE II score was 37.0, with sensitivity of 92.2% and specificity of 90.5%; SAPS II score was 30, with sensitivity of 90% and specificity of 88.8%; and SOFA score was 6.0, with sensitivity of 83.3% and specificity of 78.6% [Figure 1] and [Table 4].
Table 1: Comparison between the studied groups regarding age, sex, duration of diabetes mellitus, ICU stay, and mechanical ventilation

Click here to view
Table 2: Comparison between the studied groups regarding diabetes mellitus medication and cause of admission

Click here to view
Table 3: Comparison between the studied groups regarding laboratory parameters

Click here to view
Figure 1: ROC curve for detection of cutoffs for mortality. ROC, receiver operating characteristic.

Click here to view
Table 4: Cutoff values for prediction of adverse outcomes in critically ill patients with diabetes

Click here to view



  Discussion Top


In the present study, there was a significant difference regarding age among patients. Age was significantly higher in nonsurvivor cases than survivor cases, as it was distributed as 61.5 ± 6.11 and 49.0 ± 9.31, respectively. These results agree with Chentli et al. [9] who reported that chronic hyperglycemia leads to damage and failure of various organs, especially the heart, blood vessels, eyes, kidneys, and nerves. Those macroangiopathies and microangiopathies that can be observed even in newly diagnosed patients are due to glucose metabolism disorder over a long-term duration. They demonstrated that DM prevalence and its comorbidities and mortality are higher in elderly than in young people.

In our study, women were significantly higher in nonsurvivor cases, and they were significantly lower in survivor cases. They represent 55.6% of the nonsurvivor cases and 28.1% of the survivor cases. These results agree with Peters et al. [10], who highlighted that women and men experience the disease differently. They reported that actually women lose their relative protection from DM complications in postmenopausal period, presenting a stronger increase of cardiometabolic risk than diabetic males, but the reasons are not entirely clear. The explanations are likely to be multifactorial, with contributions from differences in inherent physiological factors and in the management and treatment of diabetes, to the detriment of women. Moreover, the data collected by the Italian Society of Diabetologists show that sex disparities in control of DM still exist despite equity of same care and pharmacological treatment of both sexes.

In our study, DM duration is significantly higher in nonsurvivor cases than survivor cases. These results agree with Herrington et al. [11] who demonstrated that diabetes is strongly associated with deaths from vascular disease, renal disease, and infection, with risks increasing substantially both with longer diabetes duration and worse glycemic control compared with those without diabetes. Poorer glycemic control was associated with even higher death rates at any given duration of diabetes; an HbA1c of 9% or more was associated with about a doubling in the death rate compared with an HbA1c of less than 9%.

Moreover, in the current study, MV days were significantly higher in nonsurvivors case than survivor cases. These results agree with Crisafulli et al. [12] who reported that DM could be an autonomous risk, harmfully disturbing the structure of the lung and its function. DM and reduced glycemic control are associated with alteration in lung volumes and reduction in flow rates along with a reduction in gas transfer which may be owing to diabetes-associated microangiopathy. DM especially if is not controlled is accompanied by a higher prevalence of infections, mainly respiratory infection, and an increasing mortality rates and length of MV stay even after correcting other bad prognostic issues.

In the present study, insulin therapy was significantly lower than oral antidiabetics in nonsurvivor cases. These results agree with Singh et al. [13] who showed that intensive insulin therapy aiming at blood glucose levels of 80–110 mg/dl could reduce the morbidity and mortality of critically ill patients.

Our results showed that diabetic patients admitted to ICU with cardiovascular problems were significantly higher in nonsurvivor cases than survivor cases. These results agree with Kosiborod et al. [14], who demonstrated that type 2 diabetes is associated with disabling and potentially life-threatening microvascular and macrovascular complications. Moreover, 80% of patients with type 2 diabetes develop cardiovascular complications, which account for ∼65% of deaths in this group. The contribution of microvascular complications to type 2 diabetes morbidity is also substantial.

Regarding the association between serum urea and creatinine levels in critically ill diabetic patients and hospital mortality, our results showed a significant association between elevated serum urea and creatinine levels and inpatient mortality. These results agree with Hsu et al. [15] who demonstrated that DM is the major cause of renal morbidity and mortality. The most common lesions involving glomeruli were associated clinically with three glomerular syndromes, including non-nephritic proteinuria, nephrotic syndrome, and chronic renal failure. After many years of diabetes, the delicate filtering system in the kidney becomes destroyed, initially becoming leaky to larger blood proteins such as albumin which are then lost in urine. This is more likely to occur if the blood sugar is poorly controlled. Both serum urea and creatinine are widely used to assess the function of kidney. Hyperglycemia has been shown to alter redox equilibrium, which can then induce oxidative stress and cause kidney damage. These suggest that oxidative stress mediates glucose-induced renal injury and increased in hospital mortality.

Concerning the association between serum AST and ALT levels in critically ill diabetic patients and hospital mortality, our results showed a significantly association between elevated serum ALT and AST levels and inpatient mortality. These results agree with Mandal et al. [16], who demonstrated that elevated liver enzymes are commonly associated with T2DM.

Regarding the association between HbA1c level in critically ill diabetic patients and ICU mortality, our results showed a significantly association between elevated HbA1c levels and ICU mortality. These results agree with Palta et al. [17], who demonstrated that ICU mortality increased substantially with increasing HbA1c variability (overall and for both sexes). The variations in the HbA1c levels suggest poor glycemic control in the T2DM cases with hyperglycemia.

Regarding the association between ADAG level in critically ill diabetic patients and ICU mortality, our study showed a significantly association between elevated ADAG levels and ICU mortality. These results agree with Donagaon and Dharmalingam [4], who demonstrated that elevated ADAG was associated with an increased risk of mortality in type 2 diabetic patients admitted to ICU. It is calculated by subtracting ADAG=[(28.7 × HbA1c)−46.7] from plasma glucose at admission.

Regarding the association between APACHE II score and ICU mortality, our results indicated that APACHE II score was significantly higher among nonsurvivor cases than survivor cases. It was significantly positively correlated with ICU mortality. These results agree with Nematifard et al. [18] who found increased in the APACHE II score led to increased risk of death by 20%. Results added that APACHE II score appeared to increase the accuracy of mortality predictions in ICU.

Regarding the association between SAPS II score and ICU mortality, our results showed that SAPS II score was significantly higher among nonsurvivor cases than survivor cases. It was significantly positive correlated with ICU mortality. These results agree with Haq et al. [19], who noted that in-hospital mortality was substantially greater in patients with higher SAPS II score and that a score greater than 80 was associated with a 70% mortality rate whereas a score less than 40 was associated with a less than 3% mortality. The authors concluded that the SAPS II predict mortality in elderly patients.

Concerning the association between SOFA score and ICU mortality, our study showed that SOFA score was significantly higher among nonsurvivor cases than survivor cases. It was significantly positive correlated with ICU mortality. These results agree with Jentzer et al. [20], who demonstrated that SOFA score had good discrimination for short-term mortality, and discriminative ability was further improved by using the maximum and mean SOFA scores over the first 2–3 ICU days.

In the current study, we found a significantly association between glycemic gap and adverse outcomes in critically ill patients with diabetes. As glycemic gap was significantly higher in nonsurvivor cases than survivor cases, it was significantly positively correlated with ICU mortality. Mean glycemic gap level was distributed, as 87.81 ± 42.5 mg/dl for nonsurvivors group and 57.59 ± 42.8 mg/dl for survivors group. Previously, studies have showed that glycemic gap is associated with adverse outcomes in patients having diabetes admitted with community-acquired pneumonia [21] and acute myocardial infarction [22]. In a study by Liao et al. [23] patients with a glycemic gap of more than or equal to 80 mg/dl had higher ICU mortality, whereas in our study, it was more than 61.7 mg/dl.

Our study has several limitations. First, it was retrospective and may have been subject to selection bias. Notably, different management approaches between physicians may have influenced the study outcomes. Second, the adequacy of glycemic control during hospitalization may be relevant. During this study, the trigger to start insulin therapy was a blood glucose level of 180 mg/dl, and we did not specifically addressed the effects of glycemic control during hospitalization. Nonetheless, recent studies suggest that attempts at tight glycemic control do not improve outcomes according to Marik and Bellomo [24]. Third, we did not specifically address the level of parenteral nutrition or the catecholamine dose.


  Conclusion Top


Our results demonstrated higher glycemic gap levels were significantly associated with an increased risk of multiorgan dysfunction syndrome, acute respiratory distress syndrome, shock, upper gastrointestinal bleed, acute kidney injury, acute respiratory failure, as well as ICU mortality. The glycemic gap is a tool that may be used to assess the severity and prognosis of patients with type 2 diabetes admitted with critical illness. Our results open a new line of investigations aiming to design therapeutic strategies that modulate glycemic gap levels for the treatment of critically ill patients with diabetes.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Punthakee Z, Goldenberg R, Katz P. Diabetes Canada Clinical Practice Guidelines Expert Committee. Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome. Can J Diabetes 2019; 42 (Suppl 1):10–15.  Back to cited text no. 1
    
2.
Zelihic E, Poneleit B, Siegmund T, Haller B, Sayk F, Dodt C, et al. Hyperglycemia in emergency patients – prevalence and consequences: results of the GLUCEMERGE analysis. Eur J Emerg Med 2015; 22:181–187.  Back to cited text no. 2
    
3.
Sharma J, Chittawar S, Maniram RS, Dubey TN, Singh A. Clinical and epidemiological study of stress hyperglycemia among medical intensive care unit patients in central India. Indian J Endocrinol Metab 2017; 21:137–141.  Back to cited text no. 3
    
4.
Donagaon S, Dharmalingam M. Association between glycemic gap and adverse outcomes in critically ill patients with diabetes. Indian J Endocrinol Metab 2018; 22:208–211.  Back to cited text no. 4
    
5.
English E, Idris I, Smith G, Dhatariya K, Kilpatrick ES, John WG, et al. The effect of anaemia and abnormalities of erythrocyte indices on HbA1c analysis: a systematic review. Diabetologia 2015; 58:1409–1421.  Back to cited text no. 5
    
6.
Haniffa R, Isaam I, De Silva AP, Dondorp AM, De Keizer NF. Performance of critical care prognostic scoring systems in low and middle-income countries: a systematic review. Crit Care 2018; 22:18.  Back to cited text no. 6
    
7.
Aminiahidashti H, Bozorgi F, Montazer SH, Baboli M, Firouzian A. Comparison of APACHE II and SAPS II scoring systems in prediction of critically ill patients' outcome. Emerg (Tehran) 2017; 5:4.  Back to cited text no. 7
    
8.
Liu Z, Meng Z, Li Y, Zhao J, Wu S, Gou S, Wu H. Prognostic accuracy of the serum lactate level, the SOFA score and the qSOFA score for mortality among adults with Sepsis. Scand J Trauma Resusc Emerg Med 2019; 27:51.  Back to cited text no. 8
    
9.
Chentli F, Azzoug S, Mahgoun S. Diabetes mellitus in elderly. Indian J Endocrinol Metab 2015; 19:744–752.  Back to cited text no. 9
    
10.
Peters SAE, Huxley RR, Sattar N, Woodward M. Sex differences in the excess risk of cardiovascular diseases associated with type 2 diabetes: potential explanations and clinical implications. Curr Cardiovasc Risk Rep 2015; 9:36.  Back to cited text no. 10
    
11.
Herrington WG, Alegre-Díaz J, Wade R, Gnatiuc L, Ramirez-Reyes R, Hill M, Emberson JR. Effect of diabetes duration and glycaemic control on 14-year cause-specific mortality in Mexican adults: a blood-based prospective cohort study. Lancet Diabetes Endocrinol 2018; 6:455–463.  Back to cited text no. 11
    
12.
Crisafulli E, Barbeta E, Ielpo A, Torres A. Management of severe acute exacerbations of COPD: an updated narrative review. Multidiscip Respir Med 2018; 13:36.  Back to cited text no. 12
    
13.
Singh M, Upreti V, Singh Y, Kannapur A, Nakra M, Kotwal N. Effect of glycemic variability on mortality in ICU settings: a prospective observational study. Indian journal of endocrinology and metabolism; 22:632-35.  Back to cited text no. 13
    
14.
Kosiborod M, Gomes MB, Nicolucci A, Pocock S, Rathmann W, Shestakova MV. DISCOVER investigators. Vascular complications in patients with type 2 diabetes: prevalence and associated factors in 38 countries (the DISCOVER study program). Cardiovasc Diabetol 2018; 17:150.  Back to cited text no. 14
    
15.
Hsu WH, Hsiao PJ, Lin PC, Chen SC, Lee MY, Shin SJ. Effect of metformin on kidney function in patients with type 2 diabetes mellitus and moderate chronic kidney disease. Oncotarget 2017;9:5416–5423.  Back to cited text no. 15
    
16.
Mandal A, Bhattarai B, Kafle P, Khalid M, Jonnadula SK, Lamicchane J, Gayam V. Elevated liver enzymes in patients with type 2 diabetes mellitus and non-alcoholic fatty liver disease. Cureus 2018; 10:3626.  Back to cited text no. 16
    
17.
Palta P, Huang E, Kalyani R, Golden SH, Yeh H-C. Hemoglobin A1c and mortality in older adults with and without diabetes: results from the National Health and Nutrition Examination Surveys (1988–2011). Diabetes Care 2017; 40:453–460.  Back to cited text no. 17
    
18.
Nematifard E, Ardehali SH, Shahbazi S, Eini-Zinab H, Vahdat Shariatpanahi Z. Combination of APACHE scoring systems with adductor pollicis muscle thickness for the prediction of mortality in patients who spend more than one day in the intensive care unit. Crit Care Res Pract 2018; 2018:5490346.  Back to cited text no. 18
    
19.
Haq A, Patil S, Parcells AL, Chamberlain RS. The Simplified Acute Physiology Score III is superior to the Simplified Acute Physiology Score II and Acute Physiology and Chronic Health Evaluation II in predicting surgical and ICU mortality in the 'oldest old'. Curr Gerontol Geriatr Res 2014; 2014:934852.  Back to cited text no. 19
    
20.
Jentzer JC, Bennett C, Wiley BM, Murphree DH, Keegan MT, Gajic O, Barsness GW. Predictive value of the Sequential Organ Failure Assessment Score for mortality in a contemporary cardiac intensive care unit population. J Am Heart Assoc 2018; 7:e008169.  Back to cited text no. 20
    
21.
Chen PC, Liao WI, Wang YC, Chang WC, Hsu CW, Chen YH, et al. An elevated glycemic gap is associated with adverse outcomes in diabetic patients with community-acquired pneumonia. Medicine (Baltimore) 2015; 94:1456.  Back to cited text no. 21
    
22.
Liao WI, Lin CS, Lee CH, Wu YC, Chang WC, Hsu CW, et al. An elevated glycemic gap is associated with adverse outcomes in diabetic patients with acute myocardial infarction. Sci Rep 2016; 6:27770.  Back to cited text no. 22
    
23.
Liao WI, Wang JC, Chang WC, Hsu CW, Chu CM, Tsai SH. Usefulness of glycemic gap to predict ICU mortality in critically ill patients with diabetes. Medicine 2015; 94:1525.  Back to cited text no. 23
    
24.
Marik PE, Bellomo R. Stress hyperglycemia: an essential survival response! Crit Care 2013; 17:305.  Back to cited text no. 24
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Patients and Methods
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed135    
    Printed8    
    Emailed0    
    PDF Downloaded15    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]