A systematic review and meta-analysis of the effect of hyperglycemia on admission for acute myocardial infarction in diabetic and non-diabetic patients

INTRODUCTION

Regarding a potential relationship between diabetes and the prognostic significance of hyperglycemia in patients presenting with acute myocardial infarction (AMI), there is still debate. Therefore, we aimed in this study to demonstrate the effect of hyperglycemia on different outcomes in AMI patients, whether they are diabetic or not.

METHODS

We searched PubMed, Web of Science, and Scopus using the following search strategy: "Diabetes" or "Diabetic" AND "Acute myocardial infarction" OR "AMI" AND "hyperglycemia" OR "glucose level" to find eligible articles that needed to go through the screening process for inclusion in our study. We conducted a meta-analysis of 19 included studies from Japan, Germany, China, the United Kingdom, and others using Review Manager version 5.4 software, pooling the mean difference in continuous variables, the number and total of dichotomous variables to measure the odds ratio (OR), and the generic inverse variance of OR or hazard ratio (HR) as reported in the included studies.

RESULTS

The mean age of the participants ranged from 56.3 to 72.3 years old. The difference in blood glucose levels between diabetes and non-diabetes patients was found to be statistically significant, with an SMD of 1.39 (95%CI: 1.12, 1.66, p < 0.00001). In diabetic patients, hyperglycemia was statistically significantly associated with mortality, with a HR of 1.92 (95% CI: 1.45, 2.55, p < 0.00001) and an OR of 1.76 (95% CI: 1.15, 2.7, p = 0.01). In non-diabetic patients admitted with AMI, hyperglycemia was statistically significantly associated with mortality, with a HR of 1.56 (95% CI: 1.31, 1.86, p < 0.00001) and an OR of 2.89 (95% CI: 2.47, 3.39, p < 0.00001). AMI patients who were diabetic were statistically more likely to have a major adverse cardiovascular event (MACE) (HR = 1.9; 95% CI: 1.19-3.03; p = 0.007). AMI patients who were not diabetic were also statistically more likely to have a MACE (HR = 1.6; 95% CI: 1.15-2.23, p = 0.006).

CONCLUSION

Hyperglycemia in AMI patients is a predictor of worse outcomes, including MACE and mortality, regardless of whether these patients are diabetic or not. In these patients, some factors act as predictors of mortality, including older age, higher glucose levels on admission, and a high Killip class.

Chronic hyperglycemia and intracranial meningiomas

Meningiomas are among the most common primary tumors of the central nervous system. Previous research into the meningioma histological appearance, genetic markers, transcriptome and epigenetic landscape has revealed that benign meningiomas significantly differ in their glucose metabolism compared to aggressive lesions. However, a correlation between the systemic glucose metabolism and the metabolism of the tumor hasn't yet been found. We hypothesized that chronic levels of glycaemia (approximated with glycated hemoglobin (HbA1c)) are different in patients with aggressive and benign meningiomas. The study encompassed 71 patients with de novo intracranial meningiomas, operated on in three European hospitals, two in Croatia and one in Spain. Our results show that patients with WHO grade 2 meningiomas had significantly higher HbA1c values compared to patients with grade 1 lesions (P = 0.0290). We also found a significant number of patients (19/71; 26.7%) being hyperglycemic, harboring all the risks that such a condition entails. Finally, we found a significant correlation between our patients' age and their preoperative HbA1c levels (P = 0.0008, ρ(rho) = 0.388), suggesting that older meningioma patients are at a higher risk of having their glycaemia severely dysregulated. These findings are especially important considering the current routine and wide-spread use of corticosteroids as anti-edematous treatment. Further research in this area could lead to better understanding of meningiomas and have immediate clinical impact.

Global burden of MDR-TB and XDR-TB attributable to high fasting plasma glucose from 1990 to 2019: a retrospective analysis based on the global burden of disease study 2019

PURPOSE

High fasting plasma glucose (HFPG) has been identified as a risk factor for drug-resistant tuberculosis incidence and mortality. However, the epidemic characteristics of HFPG-attributable multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) remain unclear. We aimed to analyze the global spatial patterns and temporal trends of HFPG-attributable MDR-TB and XDR-TB from 1990 to 2019.

METHODS

Utilizing data from the Global Burden of Disease 2019 project, annual deaths and disability-adjusted life years (DALYs) of HFPG-attributable MDR-TB and XDR-TB were conducted from 1990 to 2019. Joinpoint regression was employed to quantify trends over time.

RESULTS

From 1990 to 2019, the deaths and DALYs due to HFPG-attributable MDR-TB and XDR-TB globally showed an overall increasing trend, with a significant increase until 2003 to 2004, followed by a gradual decline or stability thereafter. The low sociodemographic index (SDI) region experienced the most significant increase over the past 30 years. Regionally, Sub-Saharan Africa, Central Asia and Oceania remained the highest burden. Furthermore, there was a sex and age disparity in the burden of HFPG-attributable MDR-TB and XDR-TB, with young males in the 25-34 age group experiencing higher mortality, DALYs burden and a faster increasing trend than females. Interestingly, an increasing trend followed by a stable or decreasing pattern was observed in the ASMR and ASDR of HFPG-attributable MDR-TB and XDR-TB with SDI increasing.

CONCLUSION

The burden of HFPG-attributable MDR-TB and XDR-TB rose worldwide from 1990 to 2019. These findings emphasize the importance of routine bi-directional screening and integrated management for drug-resistant TB and diabetes.

Temporal trends in mortality of tuberculosis attributable to high fasting plasma glucose in China from 1990 to 2019: a joinpoint regression and age-period-cohort analysis

Background

Nowadays, high fasting plasma glucose (HFPG) has been identified as the important risk factor contributing to the increased burden of diseases. But there remains a lack of research on tuberculosis (TB) mortality specifically attributable to HFPG. Thus, this study aims to explore the long-term trends in HFPG-related TB mortality in China from 1990 to 2019.

Methods

Data on HFPG-related TB mortality were obtained from the Global Burden of Disease (GBD) Study 2019. Analyzing the data using joinpoint regression and age-period-cohort methods adjusting for age, period, and cohort allowed us to assess the trends in TB mortality due to HFPG.

Results

The age-standardized mortality rates (ASMRs) of TB attributable to HFPG exhibited a downward trend in China from 1990 to 2019, with an average annual percentage change (AAPC) of -7.0 (95% CI, -7.5 to -6.6). Similar trends were found for male (AAPC of -6.5 [95% CI, -7.0 to -6.0]) and female (AAPC of -8.2 [95% CI, -8.5 to -7.9]), respectively. Local drifts curve with a U-shaped pattern reflected the AAPC of TB mortality due to HFPG across age groups. The greatest decline was observed in the age group of 60-64 years. The mortality rates related to HFPG first increased and then decreased with increasing age, peaking in the 55-59 age group. Our analysis of the period and cohort effects found that the rate ratios of TB mortality due to HFPG have decreased over the past three decades, more prominently in women. It is noteworthy that while both genders have seen a decline in HFPG-attributable TB mortality and risk, men have a higher risk and slightly less significant decline than women.

Conclusion

The present study shows that HFPG-related ASMRs and risk of TB in China decreased over the last 30 years, with similar trends observed in both men and women. In order to attain the recommended level set by the WHO, the effective strategies for glycemic control and management still needed to be implemented strictly to further decrease the burden of TB.

Accuracy of Continuous Glucose Monitoring in an Insulin-Treated Population Requiring Haemodialysis

BACKGROUND

Continuous glucose monitoring (CGM) is revolutionizing diabetes care by giving both patients and the healthcare professionals unprecedented insights into glucose variability and patterns. It is established in National Institute for Health and Care Excellence (NICE) guidance as a standard of care for type 1 diabetes and diabetes in pregnancy under certain conditions. Diabetes mellitus (DM) is recognized as an important risk factor for chronic kidney disease (CKD). Around a third of patients receiving in-center haemodialysis as renal replacement therapy (RRT) have diabetes, either as a direct cause of renal failure or as an additional co-morbidity. Evidence of poor compliance with the current standard of care (self-monitoring of blood glucose [SMBG]) and overall greater morbidity and mortality, suggests this patient population as an ideal target group for CGM. However, there exists no strong published evidence showing the validity of CGM devices in insulin-treated diabetes patients requiring haemodialysis.

METHODS

We applied a Freestyle Libre Pro sensor to 69 insulin-treated diabetes haemodialysis (HD) patients on a dialysis day. Interstitial glucose levels were obtained, and time matched within 7 minutes to capillary blood glucose testing and any plasma blood glucose levels sent. Data cleansing techniques were applied to account for rapidly correcting hypoglycaemia and poor SMBG technique.

RESULTS

Clarke-error grid analysis showed 97.9% of glucose values in an acceptable range of agreement (97.3% on dialysis days and 99.1% on non-dialysis days).

CONCLUSIONS

We conclude that the Freestyle Libre sensor is accurate in measuring glucose levels when compared to glucose as measured by capillary SMBG testing and laboratory obtained serum glucose in patients on HD.

Five-year morbidity and mortality rates in a U.S. population with obesity with and without prediabetes

AIMS

This research examines the prevalence of morbidity and mortality among people with obesity with or without prediabetes.

METHODS

This observational study uses Optum® Market Clarity deidentified data from 2007 to 2020. Individuals with obesity without prediabetes (obesity only) were matched 1:1 to adults with prediabetes plus obesity based upon age, sex, race, ethnicity, and region. Age and sex adjusted prevalence rates and 95 % CIs were calculated for morbidity and mortality for each 365-day period post index date and over the entire 5-year post-period.

RESULTS

After 5-years, the adjusted mortality rate was 10.1 % for adults with obesity plus prediabetes and 6.9 % for adults with obesity only (p < 0.05). Five years post index date, the prevalence of type 2 diabetes was 25.3 % for people with obesity plus prediabetes and 9.2 % for people with obesity only (p < 0.05). Prevalence rates after 5 years for atherosclerotic cardiovascular disease (13.1 % v 8.1 %), composite cardiovascular outcome (7.0 % v 4.4 %) and composite cardio-renal outcome (8.9 % v 5.0 %) were significantly higher for adults with obesity plus prediabetes compared to adults with obesity only (all p < 0.05).

CONCLUSIONS

Results of this study indicate that the presence of prediabetes contributes to the development of additional morbidity and mortality in adults with obesity.

Does diabetes mellitus comorbidity affect in-hospital mortality and length of stay? Analysis of administrative data in an Italian Academic Hospital

AIMS

Hospitalized patients with comorbid diabetes mellitus may have worse outcomes than the others. We conducted a study to assess whether comorbid diabetes affects in-hospital mortality and length of stay.

METHODS

For this population-based study, we analyzed the administrative databases of the Regional Health Information System of the Region Friuli Venezia Giulia, where the Hospital of Udine is located. Hospital discharge data were linked at the individual patient level with the regional Diabetes Mellitus Registry to identify diabetic patients. For each 3-digit ICD-9-CM discharge diagnosis code, we assessed the difference in length of stay and in-hospital mortality between diabetic and non-diabetic patients. We conducted both univariate and multivariate analyses, adjusted for age, sex, Charlson's comorbidity score, and urgency of hospitalization, through linear and logistic regression models.

RESULTS

After adjusting for potential confounders, diabetes significantly increased the risk of in-hospital death among patients hospitalized for bacterial pneumonia (OR = 1.94) and intestinal obstruction (OR = 4.23) and length of stay among those admitted for several diagnoses, including acute myocardial infarction and acute renal failure. Admission glucose blood level was associated with in-hospital death in patients with pneumonia and intestinal obstruction, and increased length of stay for several conditions.

CONCLUSIONS

Patients with diabetes mellitus who are hospitalized for other health problems may have increased risk of in-hospital death and longer hospital stay. For this reason, diabetes should be promptly recognized upon admission and properly managed.

Stroke mortality and its predictors in a Nigerian teaching hospital

BACKGROUND

Stroke is the third leading cause of death worldwide. Stroke mortality has been noted to be higher in blacks in biracial studies. There have been few studies on stroke mortality and its predictors in Nigeria. This study examines mortality of stroke and its predictors in a Northern Nigerian teaching hospital.

METHODS

This was a prospective study that was carried out at Jos University Teaching Hospital in Nigeria. One hundred and twenty stroke patients admitted into the medical wards within one year were assessed. Demographic data was recorded. Patients were examined and ancillary investigations were carried out. The deaths and predictors were recorded. Multivariate logistic regression was used to identify independent predictors of mortality.

RESULTS

There were one hundred and twenty participants. Forty two (35%), patients died. Most (76.2%) deaths occurred within the first week. Predictors of mortality on univariate analysis were age ≥ 60 years, male sex, loss of consciousness, high NIHSS score (≥16), the presence of co-morbid conditions and presence of complications. On multivariate analysis, the predictors of mortality were the presence of co-morbid conditions, GCS<10 and high NIHSS score.

CONCLUSION

Stroke mortality was quite high in this study. Predictors of mortality were the indices of severity and the presence of co-morbid conditions.

Genome-wide association study identifies common loci influencing circulating glycated hemoglobin (HbA1c) levels in non-diabetic subjects: the Long Life Family Study (LLFS)

OBJECTIVE

Glycated hemoglobin (HbA1c) is a stable index of chronic glycemic status and hyperglycemia associated with progressive development of insulin resistance and frank diabetes. It is also associated with premature aging and increased mortality. To uncover novel loci for HbA1c that are associated with healthy aging, we conducted a genome-wide association study (GWAS) using non-diabetic participants in the Long Life Family Study (LLFS), a study with familial clustering of exceptional longevity in the US and Denmark.

METHODS

A total of 4088 non-diabetic subjects from the LLFS were used for GWAS discoveries, and a total of 8231 non-diabetic subjects from the Atherosclerosis Risk in Communities Study (ARIC, in the MAGIC Consortium) and the Health, Aging, and Body Composition Study (HABC) were used for GWAS replications. HbA1c was adjusted for age, sex, centers, 20 principal components, without and with BMI. A linear mixed effects model was used for association testing.

RESULTS

Two known loci at GCK rs730497 (or rs2908282) and HK1 rs17476364 were confirmed (p<5e-8). Of 25 suggestive (5e-8

CONCLUSIONS

The analysis reconfirmed two known GWAS loci (GCK, HK1) and identified 25 suggestive loci including one reconfirmed variant in G6PC2 and one replicated variant near OR10R3P/SPTA1. Future focused survey of sequence elements containing mainly functional and regulatory variants may yield additional findings.

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Key Words

• Genome-wide association study
• Glucose
• Insulin resistance and diabetes
• Non-enzymatic glycation
• Premature aging processes

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MESH Headings

• Aged
• Cohort Studies
• Denmark
• Diabetes Mellitus/blood
• Female
• Genome-Wide Association Study
• Genotype
• Glycated Hemoglobin/metabolism
• Humans
• Longevity
• Male
• Middle Aged
• Phenotype
• United States

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Grants

• R01 AG032319 NIA NIH HHS
• U01 AG023744 NIA NIH HHS
• R01 HL059367 NHLBI NIH HHS
• P30 AG034424 NIA NIH HHS
• HHSN268201100006C NHLBI NIH HHS
• U01AG023712 NIA NIH HHS
• HHSN268200782096C NHLBI NIH HHS
• K01DK076595 NIDDK NIH HHS
• R21 DK080294 NIDDK NIH HHS
• U19AG023122 NIA NIH HHS
• R01HL086694 NHLBI NIH HHS
• HHSN268201100012C NHLBI NIH HHS
• UL1RR025005 NCRR NIH HHS
• U01 AG023712 NIA NIH HHS
• HHSN268201100009I NHLBI NIH HHS
• P30 AG024827 NIA NIH HHS
• R01HL59367 NHLBI NIH HHS
• HHSN268201100010C NHLBI NIH HHS
• UL1 RR025005 NCRR NIH HHS
• N01 AG062101 NIA NIH HHS
• HHSN268201100008C NHLBI NIH HHS
• UL1 TR001079 NCATS NIH HHS
• HHSN268201100005G NHLBI NIH HHS
• U19 AG023122 NIA NIH HHS
• HHSN268201100008I NHLBI NIH HHS
• HHSN268201100007C NHLBI NIH HHS
• P50 AG008702 NIA NIH HHS
• K01067207 PHS HHS
• HHSN268201100011I NHLBI NIH HHS
• P30 DK079637 NIDDK NIH HHS
• HHSN268201100011C NHLBI NIH HHS
• R01 HL086694 NHLBI NIH HHS
• 1R01AG032098-01A1 NIA NIH HHS
• U01AG023746 NIA NIH HHS
• K24 AG025727 NIA NIH HHS
• N01 AG062106 NIA NIH HHS
• HHSN268200625226C PHS HHS
• U01 HG004402 NHGRI NIH HHS
• U01HG004402 NHGRI NIH HHS
• R01 AG032098 NIA NIH HHS
• U01AG023744 NIA NIH HHS
• K01 DK076595 NIDDK NIH HHS
• R01HL087641 NHLBI NIH HHS
• U01 AG023749 NIA NIH HHS
• R21 HL114237 NHLBI NIH HHS
• HHSN268201100005I NHLBI NIH HHS
• N01 AG062103 NIA NIH HHS
• U01AG023755 NIA NIH HHS
• R21HL114237 NHLBI NIH HHS
• U01 AG023755 NIA NIH HHS
• U01AG023749 NIA NIH HHS
• K24AG025727 NIA NIH HHS
• Intramural NIH HHS
• U01 AG023746 NIA NIH HHS
• HHSN268201100009C NHLBI NIH HHS
• HHSN268201100005C NHLBI NIH HHS
• HHSN268201100007I NHLBI NIH HHS
• R21DK080294 NIDDK NIH HHS
• R01AG032319 NIA NIH HHS
• R01 HL087641 NHLBI NIH HHS

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Affiliation(s)

Ping An Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA.
Iva Miljkovic University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Center for Aging and Population Health, Pittsburgh, PA, USA
Bharat Thyagarajan Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Aldi T Kraja Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
E Warwick Daw Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
James S Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
Elizabeth Selvin Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
W H Linda Kao Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
Nisa M Maruthur Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
Micahel A Nalls Laboratory of Neurogenetics, NIA/NIH, Bethesda, MD, USA
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, USA
Tamara B Harris Laboratory of Epidemiology, Demography and Biometry, NIA/NIH, Bethesda, MD, USA
Joseph H Lee Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
Ingrid B Borecki Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
Kaare Christensen Danish Aging Research Center, Epidemiology, University of Southern Denmark, Odense, Denmark; Department of Clinical Biochemistry and Pharmacology and Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
John H Eckfeldt Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Richard Mayeux Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
Thomas T Perls Division of Geriatrics, Department of Medicine, Boston University Medical Center, Boston, MA, USA
Anne B Newman University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Center for Aging and Population Health, Pittsburgh, PA, USA
Michael A Province Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA

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ACUTE MYOCARDIAL INFARCTION SPECIFICS IN PATIENTS WITH CHRONIC TONSILLITIS

Aim. To investigate the clinical specifics of acute myocardial infarction

(AMI) in patients with chronic tonsillitis.

Material and methods. The study included 70 patients at early AMI stages (Days 1–2). The following parameters were analysed: anthropometry, blood biochemistry, myocardial necrosis markers, QT interval dispersion, echocardiography and Holter ECG monitoring parameters, and a confirmed medical diagnosis of chronic tonsillitis in medical history.

Results. In 31 patients with confirmed chronic tonsillitis, higher body mass index values, a significantly higher incidence of acute heart failure (19,4%; p<0,05) and anterior AMI (70%), higher glucose levels at admission (7,58±0,62 mmol/l), and a higher end-diastolic size of right ventriculum (3,04±0,19 cm vs. 2,66±0,05 cm) were observed. Mean daytime and peak nighttime heart rate values were higher than in other patients.

Conclusion. Patients with confirmed chronic tonsillitis were characterised by a more severe clinical course of AMI. There is some evidence that chronic tonsillitis is also associated with metabolic syndrome.

Hyperglycemia at admission and during hospital stay are independent risk factors for mortality in high risk cardiac patients admitted to an intensive cardiac care unit

AIMS

Hyperglycemia is associated with increased mortality in cardiac patients. However, the predictive value of admission- and average glucose levels in patients admitted to an intensive cardiac care unit (ICCU) has not been described.

METHODS

Observational study of patients admitted to the ICCU of a tertiary medical center in whom glucose levels were measured at and during admission. Over a 19-month period, 1713 patients were included. Mean age was 63±14 years, 1228 (72%) were male, 228 (17%) had known diabetes. Median (interquartile) glucose levels at admission were 7.9 (6.5-10.1) mmol/l; median glucose levels during ICCU admission (873 patients with three or more measurements) were 7.3 (6.7-8.3) mmol/l. Cox regression analysis was performed including the variables age, gender, admission diagnosis, length of stay, prior (cardio)vascular disease and diabetes.

RESULTS

A 1 mmol/l increase in admission glucose level (above 9 mmol/l) was associated with a 10% (95% confidence interval (CI): 7 -13%) increased risk for all-cause mortality. A 1 mmol/l higher average glucose level (above 8 mmol/l) was an additional independent predictor of mortality (HR 1.11, 95% CI: 1.03 - 1.20). At 30 days, 16.8% (97/579) of the patients with an admission glucose level in the highest tertile (>9.8 mmol/L) had died vs 5.2% (59/1134) of those with a lower admission glucose level.

CONCLUSION

In a high risk ICCU population, both high admission glucose levels as well as high average glucose levels during hospitalization were independently associated with increased mortality, even when accounting for other risk factors and parameters of disease severity.

Which is more useful in predicting hospital mortality--dichotomised blood test results or actual test values? A retrospective study in two hospitals

Background

Routine blood tests are an integral part of clinical medicine and in interpreting blood test results clinicians have two broad options. (1) Dichotomise the blood tests into normal/abnormal or (2) use the actual values and overlook the reference values. We refer to these as the “binary” and the “non-binary” strategy respectively. We investigate which strategy is better at predicting the risk of death in hospital based on seven routinely undertaken blood tests (albumin, creatinine, haemoglobin, potassium, sodium, urea, and white blood cell count) using tree models to implement the two strategies.

Methodology

A retrospective database study of emergency admissions to an acute hospital during April 2009 to March 2010, involving 10,050 emergency admissions with routine blood tests undertaken within 24 hours of admission. We compared the area under the Receiver Operating Characteristics (ROC) curve for predicting in-hospital mortality using the binary and non-binary strategy.

Results

The mortality rate was 6.98% (701/10050). The mean predicted risk of death in those who died was significantly (p-value <0.0001) lower using the binary strategy (risk = 0.181 95%CI: 0.193 to 0.210) versus the non-binary strategy (risk = 0.222 95%CI: 0.194 to 0.251), representing a risk difference of 28.74 deaths in the deceased patients (n = 701). The binary strategy had a significantly (p-value <0.0001) lower area under the ROC curve of 0.832 (95% CI: 0.819 to 0.845) versus the non-binary strategy (0.853 95% CI: 0.840 to 0.867). Similar results were obtained using data from another hospital.

Conclusions

Dichotomising routine blood test results is less accurate in predicting in-hospital mortality than using actual test values because it underestimates the risk of death in patients who died. Further research into the use of actual blood test values in clinical decision making is required especially as the infrastructure to implement this potentially promising strategy already exists in most hospitals.

Addressing hyperglycemia from hospital admission to discharge

BACKGROUND

This review examines glycemia management practices in hospitalized patients. Optimal glycemic control remains a challenge among hospitalized patients. Recent studies have questioned the benefit of tight glycemic control and have raised concerns regarding the safety of this approach. As a result, medical societies have updated glycemic targets and have published new consensus guidelines for management of glycemia in hospitalized patients. This review highlights recent inpatient glycemic trials, the new glycemic targets and recommended strategies for management of glycemia in hospitalized patients.

METHODS

Medline and PubMed searches (diabetes, hyperglycemia, hypoglycemia, intensive therapy insulin, tight glycemic control, and hospital patients) were performed for English-language articles on treatment of diabetes, insulin therapy, hyperglycemia or hypoglycemia in hospitalized patients published from 2004 to present. Earlier works cited in these papers were surveyed. Clinical studies, reviews, consensus/guidelines statements, and meta-analyses relevant to the identification and management of diabetes and hyperglycemia in hospitalized patients were included and selected. This is not an exhaustive review of the published literature.

RESULTS

Insulin remains the most appropriate agent for a majority of hospitalized patients. In critically ill patients insulin is given as a continuous intravenous (IV) infusion and in non-critically ill inpatients hyperglycemia is best managed using scheduled subcutaneous (SC) basal-bolus insulin regimens supplemented with correction doses as needed and adjusted daily with the guidance of frequent blood glucose monitoring. Prevention of hypoglycemia is equally as important to patient outcomes and is an equally necessary part of any effective glucose control program. Modern insulin analogs offer advantages over the older human insulins (e.g., regular and neutral protamine Hagedorn [NPH] insulin) because their time-action profiles more closely correspond to physiological basal and prandial insulin requirements, and have a lower propensity for inducing hypoglycemia than human insulin formulations. Long-acting basal insulin analogs (glargine, detemir) are suitable and preferred for the basal component of therapy; rapid-acting insulin analogs (aspart, lispro, glulisine) are recommended for bolus and correction doses. Sliding-scale insulin (SSI) regimens are not effective and should not be used, especially as this excludes a basal insulin component from the therapy.

CONCLUSIONS

Optimal glycemic management in the hospital setting requires judicious treatment of hyperglycemia while avoiding hypoglycemia. Insulin is the most appropriate agent for management of hyperglycemia for the majority of hospitalized patients. Intravenous insulin infusion is still preferred during and immediately after surgery, but s.c. basal insulin analogs with prandial or correction doses should be used after the immediate post-operative period, and also should be used in non-critically ill patients. Frequent and effective glucose monitoring is critical for avoiding wide deviations from acceptable glucose levels, which under a recently promulgated consensus guideline currently range between 140 mg/dL and 180 mg/dL. Glucose targets near 140 mg/dL are recommended as being the most appropriate for all hospitalized patients.