A1c-Derived Average Glucose Research Articles

The glycemic gap as a prognostic indicator in cardiogenic shock: a retrospective cohort study

BackgroundStress-induced hyperglycemia (SIH) is associated with poor outcomes in cardiogenic shock (CS), and there have been inconsistent results among patients with or without diabetes mellitus (DM). The glycemic gap (GG) is derived by subtracting A1c-derived average glucose from blood glucose levels; it is a superior indicator of SIH. We aimed to explore the role of GG in the outcomes of patients with CS.MethodsData on patients diagnosed with CS were extracted from the MIMIC-IV v2.0 database to investigate the relationship between GG and 30-day mortality (Number of absolute GG subjects = 359; Number of relative GG subjects = 357). CS patients from the Second Affiliated Hospital of Wenzhou Medical University were enrolled to explore the correlation between GG and lactic acid (Number of absolute GG subjects = 252; Number of relative GG subjects = 251). Multivariate analysis, propensity score-matched (PSM) analysis, inverse probability treatment weighting (IPTW), and Pearson correlation analysis were applied.ResultsAbsolute GG was associated with 30-day all-cause mortality in CS patients (HRadjusted: 1.779 95% CI: 1.137–2.783; HRPSM: 1.954 95% CI: 1.186–3.220; HRIPTW: 1.634 95% CI: 1.213–2.202). The higher the absolute GG level, the higher the lactic acid level (βadjusted: 1.448 95% CI: 0.474–2.423). A similar trend existed in relative GG (HRadjusted: 1.562 95% CI: 1.003–2.432; HRPSM: 1.790 95% CI: 1.127–2.845; HRIPTW: 1.740 95% CI: 1.287–2.352; βadjusted:1.294 95% CI: 0.369–2.219). Subgroup analysis showed that the relationship existed irrespective of DM. The area under the curve of GG combined with the Glasgow Coma Scale (GCS) for 30-day all-cause mortality was higher than that of GCS (absolute GG: 0.689 vs. 0.637; relative GG: 0.688 vs. 0.633). GG was positively related to the triglyceride-glucose index. Kaplan–Meier curves revealed that groups of higher GG with DM had the worst outcomes. The outcomes differed among races and GG levels (all P < 0.05).ConclusionsAmong patients with CS, absolute and relative GGs were associated with increased 30-day all-cause mortality, regardless of DM. The relationship was stable after multivariate Cox regression analysis, PSM, and IPTW analysis. Furthermore, they reflect the severity of CS to some extent. Hyperlactatemia and insulin resistance may underlie the relationship between stress-induced hyperglycemia and poor outcomes in CS patients. They both improve the predictive efficacy of the GCS.

Association of Relative Dysglycemia With Hospital Mortality in Critically Ill Patients: A Retrospective Study.

Relative dysglycemia has been proposed as a clinical entity among critically ill patients in the ICU, but is not well studied. This study aimed to clarify associations of relative hyperglycemia and hypoglycemia during the first 24 hours after ICU admission with in-hospital mortality and the respective thresholds. A single-center retrospective study. An urban tertiary hospital ICU. Adult critically ill patients admitted urgently between January 2016 and March 2022. None. Maximum and minimum glycemic ratio (GR) was defined as maximum and minimum blood glucose values during the first 24 hours after ICU admission divided by hemoglobin A1c-derived average glucose, respectively. Of 1700 patients included, in-hospital mortality was 16.9%. Nonsurvivors had a higher maximum GR, with no significant difference in minimum GR. Maximum GR during the first 24 hours after ICU admission showed a J-shaped association with in-hospital mortality, and a mortality trough at a maximum GR of approximately 1.12; threshold for increased adjusted odds ratio for mortality was 1.25. Minimum GR during the first 24 hours after ICU admission showed a U-shaped relationship with in-hospital mortality and a mortality trough at a minimum GR of approximately 0.81 with a lower threshold for increased adjusted odds ratio for mortality at 0.69. Mortality significantly increased when GR during the first 24 hours after ICU admission deviated from between 0.69 and 1.25. Further evaluation will necessarily validate the superiority of personalized glycemic management over conventional management.

Can HbA1c Alone Be Safely Used to Guide Insulin Therapy in African Youth with Type 1 Diabetes?

The relationship of HbA1c versus the mean blood glucose (MBG) is an important guide for diabetes management but may differ between ethnic groups. In Africa, the patient's glucose information is limited or unavailable and the management is largely guided by HbA1c. We sought to determine if the reference data derived from the non-African populations led to an appropriate estimation of MBG from HbA1c for the East African patients. We examined the relationship of HbA1c versus MBG obtained by the continuous glucose monitoring in a group of East African youth having type 1 diabetes in Kenya and Uganda (n = 54) compared with the data obtained from A1c-derived average glucose (ADAG) and glucose management indicator (GMI) studies. A self-identified White (European heritage) population of youth (n = 89) with type 1 diabetes, 3-18 years old, living in New Orleans, LA, USA metropolitan area (NOLA), was studied using CGM as an additional reference. The regression equation for the African cohort was MBG (mg/dL) = 32.0 + 16.73 × HbA1c (%), r = 0.55, p < 0.0001. In general, the use of the non-African references considerably overestimated MBG from HbA1c for the East African population. For example, an HbA1c = 9% (74.9 mmol/mol) corresponded to an MBG = 183 mg/dL (10.1 mmol/L) in the East African group, but 212 mg/dL (11.7 mmol/L) using ADAG, 237 mg/dL (13.1 mmol/L) using GMI and 249 mg/dL (13.8 mmol/L) using NOLA reference. The reported occurrence of serious hypoglycemia among the African patients in the year prior to the study was 21%. A reference table of HbA1c versus MBG from the East African patients was generated. The use of non-African-derived reference data to estimate MBG from HbA1c generally led to the overestimation of MBG in the East African patients. This may put the East African and other African patients at higher risk for hypoglycemia when the management is primarily based on achieving target HbA1c in the absence of the corresponding glucose data.

New stress-induced hyperglycaemia markers predict prognosis in patients after mechanical thrombectomy

ObjectiveStress-induced hyperglycaemia (SIH) is a frequent phenomenon that occurs in patients with acute ischaemic stroke. The aim of this study was to investigate the relationship between SIH and the prognosis of mechanical thrombectomy (MT) patients according to the stress hyperglycemia ratio (SHR) and glycaemic gap (GG) indicators, as well as explore its relationship with haemorrhagic transformation (HT).MethodsPatients were enrolled from January 2019 to September 2021 in our centre. SHR was calculated as fasting blood glucose divided by the A1c-derived average glucose (ADAG). GG was calculated as fasting blood glucose minus ADAG. Logistic regression was used to analyse SHR, GG with outcome and HT.ResultsA total of 423 patients were enrolled in the study. The incidence of SIH was as follows: 191/423 of patients with SHR > 0.89, 169/423 of patients with GG > -0.53. SHR > 0.89 (OR: 2.247, 95% CI: 1.344–3.756, P = 0.002) and GG>-0.53 (OR: 2.305, 95% CI: 1.370–3.879, P = 0.002) were both associated with poor outcomes (modified Rankin Scale > 2) at Day 90 and an increase risk of HT. Additionlly, receiver operating characteristic curves were used to assess the predictive performance of the SHR and GG on outcomes. The area under the curve for SHR to predict poor outcomes was 0.691, with an optimal cut-off value of 0.89. The area under the curve for GG was 0.682, with an optimal cut-off value of -0.53.ConclusionHigh SHR and high GG are strongly associated with poor 90-day prognosis in MT patients and an increased risk of HT.

Interpretation of A1C measurement in sub-Saharan Africa beyond the global A1C-Derived Average Glucose (ADAG) equation.

optimal metabolic control is crucial for prevention of diabetes associated complications. HbA1c is a correlate of chronic hyperglycemia and is associated with long-term diabetes complications. We investigate the relationship between A1C and estimated average blood glucose (eAG) from the multicenter A1C-Derived Average Glucose (ADAG) study, in a sub-Saharan African population. forty-seven patients with diabetes mellitus and ten normoglycemic individuals were consecutively recruited from a tertiary reference hospital in Cameroon. This observational study was conducted in the framework of the ADAG study. eAG was derived from single values obtained from self-monitored blood glucose (SMBG) and from continuous glucose monitoring (CGM). Spearman correlation coefficient was used to examine the relationship between eAG and A1C levels. there was a strong linear relationship between eAG using SMBG with A1C level; eAG (mmol/l) =1.22 x A1C (%) - 0.25; R2 = 0.58; p<0.001. This suggests that a one percent increase in A1C corresponds to a 1.22 mmol/l increment of eAG. A similar relationship was found between A1C level and eAG from the continuous glucose monitoring (CGM) measurements albeit with a smaller accretion; eAG (mmol/l) =0.95 x A1C (%) + 1.52; R2 = 0.52; p<0.001. The bias of the global ADAG equation was lower than 5% below A1C level of 7% and progressively increased with higher values of A1C. consistent with previous reports, using a population specific equation, A1C can be better derived from eAG in individuals from sub-Saharan African origin.

Impact of glycemic gap on 30-day adverse outcomes in patients with acute ST-segment elevation myocardial infarction

Background and aimsStress-induced hyperglycemia (SIH) generally occurs in critical illness. Recently, glycemic gap (GAP) has been considered to be a superior indicator of SIH. However, data on the association between GAP and prognosis in ST-segment elevation myocardial infarction (STEMI) is limited. This observational study aimed to estimate the prognostic value of GAPmean, defined as the difference between mean blood glucose level (MGL) within 24 h after admission and A1c-derived average glucose (ADAG), in patients with acute STEMI. MethodsA total of 4952 patients with acute STEMI were included in the final analysis, and they were divided into four groups according to GAPmean quartiles and diabetes mellitus (DM). The primary outcomes were all-cause mortality and major adverse cardiovascular events (MACEs). Cox proportional hazards regression analysis and net reclassification improvement (NRI) analysis were performed. ResultsAt 30 days of follow-up, 324 (6.5%) deaths and 569 (11.5%) MACEs occurred. With the elevation of GAPmean, the incidence of all-cause mortality (4.0%, 5.6%, 6.5%, and 10.1%) and MACEs (7.3%, 9.6%, 11.4%, and 17.7%) significantly increased. Receiver operating characteristic curve analysis demonstrated that GAPmean was superior to admission blood glucose (ABG) and GAPadm (defined as the difference between ABG and ADAG) to detect adverse outcomes. Multivariate Cox regression analysis revealed that elevated GAPmean was independently associated with all-cause death and MACEs. With the first quartile as a reference, the hazards ratios (HRs) for all-cause death in the second, third, and fourth quartiles were 1.49 (95% CI 1.02–2.18), 1.58 (95% CI 1.09–2.30), and 2.11 (95% CI 1.48–3.02), respectively, and the HRs for MACEs were 1.40 (95% CI 1.05–1.86), 1.60 (95% CI 1.21–2.11), and 2.17 (95% CI 1.66–2.83), respectively, which were independent of DM status. Continuous NRI analysis revealed that GAPmean significantly improved risk stratification for all-cause mortality and MACEs by 21.6% and 19.8%, respectively. ConclusionsThe glycemic gap between MGL within 24 h after admission and ADAG was independently associated with 30-day all-cause mortality and MACEs in patients with acute STEMI, which was not affected by DM status. Further, the glycemic gap provided incremental accuracy in the risk stratification of STEMI.

Association between time in range of relative normoglycemia and in-hospital mortality in critically ill patients: a single-center retrospective study

The aim of this single-center retrospective study was to investigate the association between the time in range (TIR) of relative normoglycemia (RN) and in-hospital mortality. We defined RN as measured blood glucose in the range of 70–140% of A1C-derived average glucose and absolute normoglycemia (AN) as 70–140 mg/dL. We conducted multivariate logistic regression analyses to examine the association between TIR of RN > 80% or TIR of AN > 80% up to 72 h after ICU admission and in-hospital mortality (Model 1 and Model 2, respectively). The discrimination of the models was assessed using the area under the receiver operating characteristic curve (AUROC). Among 328 patients, 35 died in hospital (11%). Model 1 showed that TIR of RN > 80% was associated with reduced in-hospital mortality (adjusted OR 0.16; 95% CI 0.06–0.43; P < 0. 001); however, Model 2 showed that the TIR of AN > 80% was not. The AUROC of Model 1 was significantly higher than that of Model 2 (0.84 [95% CI 0.77–0.90] vs. 0.79 [0.70–0.87], P = 0.008).Our findings provide a foundation for further studies exploring individualized glycemic management in ICUs.

Prognostic value of glycemic gap in patients with spontaneous intracerebral hemorrhage.

Glycemic gap (GG), as a novel biomarker showing the acute glycemic change after the onset of acute illness, has been found to be associated with adverse outcomes in many diseases. This study aimed to explore the prognostic value of GG on long-term outcomes of spontaneous intracerebral hemorrhage (sICH). The current study included 528 patients from a multicenter, prospective, consecutive, observational cohort study. Poor clinical outcome was defined as modified Rankin Scale ≥ 3. GG was calculated using admission blood glucose minus hemoglobin A1c-derived average blood glucose. Logistic regression analyses were performed to determine the association between GG and poor clinical outcomes at 30 days, 90 days, and 1year. Glycemic gap was significantly associated with poor clinical outcomes at 30 days, 90 days, and 1year (p&lt; 0.05 for all models), where patients with higher GG were more likely to have poor clinical outcome. Restricted cubic splines revealed a positive association between GG and poor clinical outcome. In addition, patients with higher GG were more likely to have a higher 1-year mortality rate. The addition of GG to the intracerebral hemorrhage score improved the discrimination and calibration properties for the prediction of poor clinical outcome. Glycemic gap was independently associated with poor outcomes and may be a valuable prognostic factor in patients with sICH.

Association Between Glycemic Gap and Mortality in Critically Ill Patients with Diabetes.

Dysglycemia is associated with poor outcomes in critically ill patients,which is uncertain in patients with diabetes regarding to the situation of glucose control before hospitalization. This study was aimed to investigate the effect of the difference between the level of blood glucose during ICU stay and before admission to ICU upon the outcomes of critically ill patients with diabetes. Patients with diabetes expected to stay for more than 24hs were enrolled, HbA1c was converted to A1C-derived average glucose (ADAG) by the equation: ADAG = [ (HbA1c * 28.7) - 46.7 ] * 18-1, blood glucose were measured four times a day during the first 7 days after admission, the mean glucose level(MGL) and SOFA (within 3, 5, and 7days) were calculated for each person, GAPadm and GAPmean was calculated as admission blood glucose and MGL minus ADAG, the incidence of moderate hypoglycemia(MH), severe hypoglycemia (SH), total dosage of glucocorticoids and average daily dosage of insulin, duration of renal replacement therapy(RRT), ventilator-free hours, and non-ICU days were also collected. Patients were divided into survival group and nonsurvival group according to survival or not at 28-day, the relationship between GAP and mortality were analyzed. 431 patients were divided into survival group and nonsurvival group. The two groups had a comparable level of HbA1c, the nonsurvivors had greater APACHE II, SOFA, GAPadm, GAPmean-3, GAPmean-5, GAPmean-7 and higher MH and SH incidences. Less duration of ventilator-free, non-ICU stay and longer duration of RRT were recorded in the nonsurvival group. GAPmean-5 had the greatest predictive power with an AUC of 0.807(95%CI: 0.762-0.851), the cut-off value was 3.6 mmol/L (sensitivity 77.7% and specificity 76.6%). The AUC was increased to 0.852(95%CI: 0.814-0.889) incorporated with SOFA5 (NRI = 11.34%). Glycemic GAP between the MGL within 5 days and ADAG was independently associated with 28-day mortality of critically ill patients with diabetes. The predictive power was optimized with addition of SOFA5.

To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of this study is to determine to mean random plasma glucose levels and h1bc levels using the Nathan, ADAG (A1C-derived average glucose), and DTTC (Dynamic Temporal and Tactile Cueing) methods. This system can identify and recognize food images, as well as keep track of the food items ingested by the user. Deep learning techniques are mostly utilized for picture recognition and categorization. The KNN (k-nearest neighbors algorithm) classification approach is used to determine if diabetes is normal, pre-diabetic, or chronic. This study employs deep learning and a smart camera app called “calorie mom” to track nutrition from meal photographs. In addition, the commonly used measures of divisions such as accuracy, sensitivity, uniqueness, and recalling diabetic dataset using Python 3 Jupyter Notebook were employed to evaluate the performance of a machine learning classifier.

Admission glycemic gap in the assessment of patients with intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke. Glycemic gap, determined by the difference between glucose and the HbA1c-derived average glucose, predicts poor outcomes in various clinical settings. Our main objective was to evaluate association of some admission factors and outcomes in relation to admission glycemic gap (AGG) in patients with ICH. MethodsWe retrospectively analyzed 506 adult patients with ICH between 2014 and 2019. AGG was defined as A1c-derived average glucose (28.7×HbA1c-46.7) subtracted from admission glucose. Admission factors and hospital outcomes indicative of poor outcome (i.e. death, gastrostomy tube, tracheostomy, and discharge status) were compared between patients with elevated (greater than 80 mg/dL) vs. non-elevated (less than or equal-to 80 mg/dL) AGG. Pearson chi-square test was used for independence, and multivariate analysis was used for association. SPSS and excel were used for all data analysis. ResultsWe found that 67 of 506 (13%) ICH patients had elevated AGG with a mean of 137.3 mg/dL compared to 439 (87%) non-elevated AGG with a mean of 12.6 mg/dL. While mean and standard deviation values for age, weight,and body mass index were comparable between groups, the elevated AGG group had significantly higher admission glucose (286.1 ± 84.3 vs. 140.1 ± 42.5, p < 0.001), higher lactic acid (3.26 ± 2.04 mmol/L vs. 1.99 ± 1.33 mmol/L, p < 0.001), lower Glasgow Coma Scale (GCS) scores (7.70 ± 4.28 vs. 11.24 ± 4.14, p < 0.001), and higher ICH score (median 3, IQR 2–4 vs. median 1, IQR 0–3, p < 0.001). Higher AGG was associated with an increased likelihood of mechanical ventilation, and in-hospital mortality (74.6% vs. 38.3% and 47.8% vs. 15.0% respectively, p < 0.001). Placements of tracheostomy and gastrostomy were similar between the two groups (13.4% vs. 11.8%, p = 0.69% and 1.5% and 4.6%, p = 0.34 respectively). The higher AGG group had a more common poor discharge outcome to either long-term acute care, skilled nursing facility, and/or hospice (65.7% vs. 42.6%, p < 0.001). Hospital cost and length of hospitalization did not differ significantly. Although AGG was not an independent predictor of poor outcome, multivariate analysis showed it was significantly associated with poor outcome while admission glucose was not (p < 0.001 vs. p = 0.167). ConclusionElevated AGG was associated with worse GCS and ICH scores on admission, as well as need for mechanical ventilation, in hospital mortality and poor discharge status. Elevated AGG has value in prediction of outcome, but existing understanding is limited.

Effect of glycemic gap upon mortality in critically ill patients with diabetes.

Aims/IntroductionHyperglycemia, hypoglycemia, and blood glucose fluctuation are associated with the outcome in critically ill patients, but the target of blood glucose control is debatable especially in patients with diabetes regarding the status of blood glucose control before admission to ICU. This study aimed to investigate the association between the glycemic gap which is calculated as the mean blood glucose level during the first 7 days after admission to ICU minus the A1C‐derived average glucose and the outcome of critically ill patients with diabetes.MethodThis study was undertaken in two intensive care units (ICUs) with a total of 30 beds. Patients with diabetes who were expected to stay for more than 24 h were enrolled, the HbA1c was tested within 3 days after admission and converted to the A1C‐derived average glucose (ADAG) by the equation: ADAG = [(HbA1c * 28.7) – 46.7 ] * 18‐1, arterial blood glucose measurements were four per day routinely during the first 7 days after admission, the APACHE II score within the first 24 h, the mean blood glucose level (MGL), standard deviation (SD), and coefficient of variation (CV) during the first 7 days were calculated for each person, the GAPadm and GAPmean were calculated as the admission blood glucose and MGL minus the ADAG, respectively, the incidence of moderate hypoglycemia (MH) and severe hypoglycemia (SH), the total dosage of glucocorticoids and average daily dosage of insulin within 7 days, the duration of renal replacement therapy (RRT), ventilator‐free hours, and non‐ICU stay days within 28 days were also collected. The enrolled patients were divided into a survival group and a nonsurvival group according to survival or not at 28 days and 1 year after admission, and the relationship between parameters derived from blood glucose and mortality in the enrolled critically ill patients was explored.ResultsFive hundred and two patients were enrolled and divided into a survival group (n = 310) and a nonsurvival group (n = 192). It was shown that the two groups had a comparable level of HbA1c, the nonsurvivors had a greater APACHE II, MGL, SD, CV, GAPadm, GAPmean, and a higher incidence of hypoglycemia. A lesser duration of ventilator‐free, non‐ICU stay, and a longer duration of RRT were recorded in the nonsurvival group, who received a lower carbohydrate intake, a higher daily dosage of insulin and glucocorticoid. GAPmean had the greatest predictive power with an AUC of 0.820 (95%CI: 0.781–0.850), the cut‐off value was 3.60 mmol/L (sensitivity 78.2% and specificity 77.3%). Patients with a low GAPmean tended to survive longer than the high GAPmean group 1 year after admission.ConclusionsGlycemic GAP between the mean level of blood glucose within the first 7 days after admission to ICU and the A1C‐derived average glucose was independently associated with a 28 day mortality of critically ill patients with diabetes, the predictive power extended to 1 year. The incidence of hypoglycemia was associated with mortality either.

615-P: Developing Self-Monitored Blood Glucose Metrics Correlated to Glycated Hemoglobin to Improve Real-World Evidence Generation

Background: Self-monitoring blood glucose (SMBG) values have been used to estimate A1c (eA1c) using the A1c-Derived Average Glucose (ADAG) equation with modifications causing limitations with the results. Our study's objective was to develop new glucose management metrics leveraging real-world SMBG values and checking patterns correlated to lab A1c values.Methods: We conducted a retrospective analysis of data from the Livongo remote management Program for Diabetes. Spearman rank was used to measure the correlation between lab A1c and mean SMBG values by relationship to meals during a 90-day time-period. Using the ADAG equation, mean SBMG metrics were used to generate eA1c values for comparison.Results: There were 815 participants with 906 lab A1c and SMBG values with mean age of 56 years, 54% male, and 10% type 1. Three metrics in Table 1 most correlated (r >= 0.70) to lab A1c had differences of eA1c from -0.5% to -0.6%. Post-meal SMBG had slightly lower correlation (0.66) but with a mean eA1c more similar to the laboratory value.Conclusions: Though real-world SMBG values are correlated to lab A1c values, checking patterns impact the ability of the ADAG equation to estimate A1c. Future work will develop a new linear transformation equation based on meal distribution checking patterns to improve A1c estimation and the ability to leverage SMBG data for real-world evidence generation.View largeDownload slideView largeDownload slide DisclosureM. Perez-nieves: Employee; Self; Eli Lilly and Company. W. Lu: Employee; Self; Teladoc Health. E. Meadows: Employee; Self; Eli Lilly and Company, Employee; Spouse/Partner; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. F. Gelsey: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. L. Fan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. R. James: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. B. Shah: Employee; Self; Teladoc Health, Stock/Shareholder; Self; Teladoc Health.

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

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.

Correlation between self-monitored mean blood glucose and average plasma glucose estimated from glycated haemoglobin in patients attending the diabetes clinic at Dr George Mukhari Academic Hospital, Pretoria, South Africa.

Glycated haemoglobin (HbA1c) has been used for decades as a measure of chronic glycaemia. A simple linear relationship between HbA1c values and mean blood glucose (MBG) has been identified and led to conversion of HbA1c values into estimated average glucose (eAG) levels, following the findings of the A1c-Derived Average Glucose (ADAG) Study Group. The intention was to help patients with diabetes mellitus (DM) understand their glycaemic control better, as eAG is reported in the same units as self-monitored glucose levels. However, factors other than glycaemia have been found to affect the relationship between HbA1c and MBG. To: (i) determine the relationship between self-monitored MBG levels and HbA1c values; and (ii) evaluate the correlation between MBG levels and eAG levels calculated from HbA1c values using the regression equation derived from the ADAG Study Group in black South African patients with DM. This was a prospective observational study of 96 diabetic patients. MBG levels were calculated using glucose measurements downloaded from the glucose meters for the previous 90 days (3 months). High-performance liquid chromatography was used for measurement of HbA1c values, collected at the end of 3 months. eAG was calculated using the regression equation from the ADAG Study Group, as follows: eAG (mmol/L) = 1.5944 × HbA1c (NGSP, %) - 2.594. A positive correlation was found between MBG and HbA1c in all participants (R2=0.69, p&lt;0.0001). There was a wide range of MBG levels for any given HbA1c value. Clinically significant differences between MBG and eAG were found, with a ≥10% difference in 65.6% of the participants. eAG overestimated MBG in ~71.8% of the study population, with an overestimation of ≥1.6 mmol/L (28.7 mg/dL, equivalent to a 1% change in HbA1c value) in ~50% of the total study population. Our findings showed an imperfect relationship between MBG levels and HbA1c values. eAG significantly overestimated MBG, and this disagreement may cause confusion among both patients and clinicians. The risk of hypoglycaemic episodes may also increase if HbA1c and eAG alone are used to intensify therapy. We recommend that the use of eAG should be validated prior to implementation in clinical practice. It would be ideal to evaluate the relationship between average glucose and HbA1c in each individual patient in order to provide more personalised diabetes care.

An elevated glycemic gap predicts adverse outcomes in diabetic patients with necrotizing fasciitis

BackgroundDiabetes is the most common comorbidity of necrotizing fasciitis (NF), but the effect of stress-induced hyperglycemia (SIH) on diabetic patients with NF has never been investigated. The aim of this study was to assess whether SIH, as determined by the glycemic gap between admission glucose levels and A1C-derived average glucose levels, predicts adverse outcomes in diabetic patients hospitalized with NF.MethodsWe retrospectively reviewed the glycemic gap and clinical outcomes in 252 diabetic patients hospitalized due to NF from 2011 to 2018 in a single medical center in Taiwan. A receiver operating characteristic (ROC) curve was used to analyze the optimal cutoff values for predicting adverse outcomes. Univariate and multivariate logistic regression analyses were employed to identify significant predictors of adverse outcomes.ResultsIn total, 194 diabetic NF patients were enrolled. Compared with patients without adverse outcomes, patients with adverse outcomes had significantly higher glycemic gaps, Acute Physiology and Chronic Health Evaluation (APACHE) II scores and C-reactive protein (CRP) levels; lower albumin and hemoglobin levels; greater incidence of limb loss; and longer hospital and intensive care unit stays. The glycemic gap positively correlates with the laboratory risk indicator for NF scores, APACHE II scores and CRP levels. A glycemic gap of 146 mg/dL was the optimal cutoff value for predicting adverse outcomes using the ROC curve. Compared with patients with glycemic gaps ≤146 mg/dL, those with glycemic gaps >146 mg/dL had higher APACHE II scores and incidence rates of adverse outcomes, especially bacteremia and acute kidney injury. Multivariate analysis revealed that a glycemic gap >146 mg/dL and APACHE II score >15 were independent predictors of adverse outcomes, while the presence of hyperglycemia at admission was not.ConclusionsAn elevated glycemic gap was significantly independently associated with adverse outcomes in diabetic NF patients. Further prospective studies are warranted to validate the role of the glycemic gap in NF patients with diabetes.

Elevated Glycemic Gap Predicts Acute Respiratory Failure and In-hospital Mortality in Acute Heart Failure Patients with Diabetes

Diabetes is a common comorbidity in patients hospitalized for acute heart failure (AHF), but the relationship between admission glucose level, glycemic gap, and in-hospital mortality in patients with both conditions has not been investigated thoroughly. Clinical data for admission glucose, glycemic gap and in-hospital death in 425 diabetic patients hospitalized because of AHF were collected retrospectively. Glycemic gap was calculated as the A1c-derived average glucose subtracted from the admission plasma glucose level. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff value for glycemic gap to predict all-cause mortality. Patients with glycemic gap levels >43 mg/dL had higher rates of all-cause death (adjusted hazard ratio, 7.225, 95% confidence interval, 1.355–38.520) than those with glycemic gap levels ≤43 mg/dL. The B-type natriuretic peptide levels incorporated with glycemic gap could increase the predictive capacity for in-hospital mortality and increase the area under the ROC from 0.764 to 0.805 (net reclassification improvement = 9.9%, p < 0.05). In conclusion, glycemic gap may be considered a useful parameter for predicting the disease severity and prognosis of patients with diabetes hospitalized for AHF.

Diagnostic Accuracy of Glycated Haemoglobin and Average Glucose Values in Type 2 Diabetes Mellitus Treated with Premixed Insulin.

IntroductionStudies assessing the relationship between glycated haemoglobin (HbA1c) and average blood glucose (ABG) were conducted in small groups of patients on different treatments and may be biased for these reasons. The aim of the study was to assess the relationship between HbA1c and ABG in a large group of type 2 diabetes patients treated with premix insulin.MethodsIn 4257 patients treated with premixed insulin, the parallel point-of-care assessment of HbA1c and ABG from the preceding 90 days (ABG90), calculated automatically from all values measured by the glucometer, was performed twice. The regression formulas and respective values of HbA1c and ABG90 were calculated.ResultsThe mean number of recorded glucose values/patient was 2.37 estimations per day. The regression formula calculated using data from the first assessment was HbA1c = 5.28 + 0.01487 × ABG90 and that using data from the second one was HbA1c = 4.78 + 0.01683 × ABG90. The slopes of the regression lines are lower than that in a similar analysis from the A1c-Derived Average Glucose (ADAG) study. The comparison of ADAG formula and the formula derived from the present study shows that both formulas give similar results at low HbA1c values, but differ at higher HbA1c values. Additionally, the 95% confidence interval is broader in the PROGENS study e.g. a 95% probability of certainty that the actual HbA1c value was greater than 7.0% (53 mmol/mol) was achieved only at an ABG90 value of 220 mg/dL.ConclusionThe relationship between HbA1c and ABG estimations may be different in various patients; therefore, it seems that the use of one equation in all populations may not be reliable. Broad assessment of ABG as a tool that may replace HbA1c measurements should be recommended only with caution, providing the possible limitations and confidence intervals.FundingBioton S.A.

Interpretation of HbA1c in primary care and potential influence of anaemia and chronic kidney disease: an analysis from the Copenhagen Primary Care Laboratory (CopLab) Database.

To investigate, in a large population in primary care, the relationship between fasting plasma glucose and HbA1c measurements, as well as the clinical implications of anaemia or chronic kidney disease for the interpretation of HbA1c values. From a primary care resource, we examined HbA1c and fasting plasma glucose as well as haemoglobin and estimated GFR. We stratified observations by chronic kidney disease stage and anaemia level. The estimation of the mean fasting plasma glucose level from HbA1c alone, and from HbA1c , haemoglobin and estimated GFR, respectively, was evaluated. In 198 346 individuals, the fasting plasma glucose-HbA1c relationship mimicked the regression described in the A1c-Derived Average Glucose (ADAG) study, which was based on average capillary and interstitial glucose. The fasting plasma glucose-HbA1c relationship was unaffected in mild to moderate chronic kidney disease and in mild to moderate anaemia. The correlation changed only in severe hyperglycaemia and concurrent severe anaemia or when estimated GFR was <45 ml/min/1.73m², so that glucose concentration was underestimated by HbA1c in anaemia and overestimated in chronic kidney disease. The prevalence of estimated GFR <30 ml/min/1.73m² was 0.82%, while the prevalence of haemoglobin <81 g/l (5.0 mmol/l) was 0.11%. The relationship between fasting plasma glucose and HbA1c mimics that of the people with diabetes included in the ADAG study. Mild to moderate anaemia and CKD do not have a significant impact on the interpretation of HbA1c as a marker of retrograde glycaemia. Hence, it seems justified to use HbA1c without adjustment in primary care.

Relative contributions of preprandial and postprandial glucose exposures, glycemic variability, and non-glycemic factors to HbA1c in individuals with and without diabetes

Background/objectiveThere is substantial interest in dietary approaches to reducing postprandial glucose (PPG) responses, but the quantitative contribution of PPG to longer-term glycemic control (reflected in glycated hemoglobin, HbA1c) in the general population is not known. This study quantified the associations of preprandial glucose exposure, PPG exposure, and glycemic variability with HbA1c and estimated the explained variance in HbA1c in individuals with and without type 2 diabetes (T2D).Subjects/methodsParticipants in the A1c-Derived Average Glucose (ADAG) study without T2D (n = 77) or with non-insulin-treated T2D and HbA1c<6.5% (T2DHbA1c < 6.5%, n = 63) or HbA1c ≥ 6.5% (T2DHbA1c ≥ 6.5%, n = 34) were included in this analysis. Indices of preprandial glucose, PPG, and glycemic variability were calculated from continuous glucose monitoring during four periods over 12 weeks prior to HbA1c measurement. In linear regression models, we estimated the associations of the glycemic exposures with HbA1c and calculated the proportion of variance in HbA1c explained by glycemic and non-glycemic factors (age, sex, body mass index, and ethnicity).ResultsThe factors in the analysis explained 35% of the variance in HbA1c in non-diabetic individuals, 49% in T2DHbA1c < 6.5%, and 78% in T2DHbA1c ≥ 6.5%. In non-diabetic individuals PPG exposure was associated with HbA1c in confounder-adjusted analyses (P < 0.05). In the T2DHbA1c < 6.5% group, all glycemic measures were associated with HbA1c (P < 0.05); preprandial glucose and PPG accounted for 14 and 18%, respectively, of the explained variation. In T2DHbA1c ≥ 6.5%, these glycemic exposures accounted for more than 50% of the variation in HbA1c and with equal relative contributions.ConclusionsAmong the glycemic exposures, PPG exposure was most strongly predictive of HbA1c in non-diabetic individuals, suggesting that interventions targeting lowering of the PPG response may be beneficial for long-term glycemic maintenance. In T2D, preprandial glucose and PPG exposure contributed equally to HbA1c.