Translating the A1C assay into estimated average glucose values: response to Nathan et al

Relative Hypoglycemia is Associated with Delirium in Critically Ill Patients with Diabetes: A Cohort Study

PURPOSE

Critically ill patients with premorbid diabetes can suffer from relative hypoglycemia (RHG), falling below the normal blood glucose (BG) target. However, these events have not been well defined or studied. In the present study, we aimed to explore the incidence and clinical significance of RHG events in critically ill patients with diabetes.

PATIENTS AND METHODS

Patients with a history of diabetes who stayed in the intensive care unit (ICU) for more than three days with at least 12 BG recordings were retrospectively included in the study. A BG level > 30% below the estimated average according to patient hemoglobin A1c measured at admission was defined as a single RHG event. Outcomes were compared between patients with and those without RHG events.

RESULTS

In total, 113 patients were included in the final analysis. RHG was detected in 73 patients (64.6%). Those who experienced RHG events had a significantly higher incidence of ICU delirium. They also had a higher risk of 28-day mortality, but this was not statistically significant. However, patients with a higher frequency of RHG events did have a significantly higher risk of overall mortality (57.1% for more than four events vs 15.4% for three to four events, P=0.006 and 15.1% for one to two events, P=0.003).

CONCLUSION

In conclusion, RHG is a common finding in critically ill patients with diabetes and is associated with mortality and the occurrence of delirium.

Blood Glucose and Renal Function Evaluation in Patients with Viral Hepatitis

PURPOSE

To evaluate the blood glucose and renal function, determine the prevalence of hyperglycemia/diabetes mellitus (DM) and renal disease (nephropathy), and investigate the association between hyperglycemia/DM and renal disease in patients with viral hepatitis (VH).

PATIENTS AND METHODS

A total of 491 subjects were included in the study. Patients with VH were further divided into the hepatitis B virus (HBV) infection, hepatitis C virus (HCV) infection, and HBV-HCV co-infection subgroups. Fasting blood glucose, glycated hemoglobin (HbA1c), glycated albumin (GA), glutamic oxaloacetic transaminase (GOT), creatinine (Cr), and cystatin C (Cys C) levels were measured. Urine microalbumin levels were also assessed. Formulas for estimated average glucose calculated using glycated albumin(eAG(GA)), estimated average glucose calculated using HbA1c (eAG(HbA1c)), and estimated glomerular filtration rate calculated using cystatin C (eGFRcys) were used to evaluate the average glucose and renal function.

RESULTS

The prevalence of hyperglycemia/DM and renal disease was significantly higher in the VH group, especially in the HCV subgroup. The prevalence of renal disease was significantly higher in patients with VH with eAG(GA) ≥200 mg/dL.

CONCLUSION

Our study used multiple parameters to evaluate blood glucose and renal function in patients with VH and found that hyperglycemia/DM and renal disease are closely associated with VH, especially in subjects with HCV infection. Patients with VH, especially those with HCV infection and hyperglycemia/DM, were particularly vulnerable to renal disease.

Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study

Background

Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health data in a standard representation of medical data such as Fast Healthcare Interoperability Resources (FHIR), from patients systems like mobile health apps and display the data directly to their users—the clinicians. However, although Norwegian EHRs are working on implementing FHIR, no solution or graphical interface is available today to display self-collected health data.

Objective

The objective of this study was to design and assess a dashboard for displaying relevant self-collected health data from patients with diabetes to clinicians.

Methods

The design relied on an iterative participatory process involving workshops with patients, clinicians, and researchers to define which information should be available and how it should be displayed. The assessment is based on a case study, presenting an instance of the dashboard populated with data collected from one patient with diabetes type 1 (in-house researcher) face-to-face by 14 clinicians. We performed a qualitative analysis based on usability, functionality, and expectation by using responses to questionnaires that were distributed to the 14 clinicians at the end of the workshops and collected before the participants left. The qualitative assessment was guided by the Standards for Reporting Qualitative Research.

Results

We created a dashboard permitting clinicians to assess the reliability of self-collected health data, list all collected data including medical calculations, and highlight medical situations that need to be investigated to improve the situation of the patients. The dashboard uses a combination of tables, graphs, and other visual representations to display the relevant information. Clinicians think that this type of solution will be useful during consultations every day, especially for patients living in remote areas or those who are technologically interested.

Conclusions

Displaying self-collected health data during consultations is not enough for clinicians; the data reliability has to be assured and the relevant information needs to be extracted and displayed along with the data to ease the introduction during a medical encounter. The prestudy assessment showed that the system provides relevant information to meet clinicians’ need and that clinicians were eager to start using it during consultations. The system has been under testing in a medical trial since November 2018, and the first results of its assessment in a real-life situation are expected in the beginning of next year (2020).

Estimated average glucose and self-monitored mean blood glucose are discordant estimates of glycemic control

OBJECTIVE

The A1C-Derived Average Glucose study recommended reporting A1C in estimated average glucose (eAG) equivalents. We compared eAG with self-monitored mean blood glucose (MBG) to determine whether eAG is systematically biased due to biological variation in the relationship between MBG and A1C.

RESEARCH DESIGN AND METHODS

MBG and A1C were recorded from charts of 202 pediatric type 1 diabetic patients at 1,612 clinic visits. Patients were divided into groups with low, moderate, or high A1C bias based on a hemoglobin glycation index (HGI).

RESULTS

The mean +/- SD values for MBG versus eAG were as follows: total population, 194 +/- 34 vs. 196 +/- 36 mg/dl; low-HGI group, 186 +/- 31 vs. 163 +/- 20 mg/dl; moderate-HGI group, 195 +/- 28 vs. 193 +/- 19 mg/dl; and high-HGI group, 199 +/- 42 vs. 230 +/- 31 mg/dl.

CONCLUSIONS

eAG underestimated MBG in low HGI patients and overestimated MBG in high HGI patients. Disagreement between eAG and MBG downloaded from patient glucose meters will cause confusion if eAG is implemented for clinical use.

The association between estimated average glucose levels and fasting plasma glucose levels

OBJECTIVE

The level of hemoglobin A1c (HbA1c), also known as glycated hemoglobin, determines how well a patient's blood glucose level has been controlled over the previous 8-12 weeks. HbA1c levels help patients and doctors understand whether a particular diabetes treatment is working and whether adjustments need to be made to the treatment. Because the HbA1c level is a marker of blood glucose for the previous 120 days, average blood glucose levels can be estimated using HbA1c levels. Our aim in the present study was to investigate the relationship between estimated average glucose levels, as calculated by HbA1c levels, and fasting plasma glucose levels.

METHODS

The fasting plasma glucose levels of 3891 diabetic patient samples (1497 male, 2394 female) were obtained from the laboratory information system used for HbA1c testing by the Department of Internal Medicine at the Izmir Bozyaka Training and Research Hospital in Turkey. These samples were selected from patient samples that had hemoglobin levels between 12 and 16 g/dL. The estimated glucose levels were calculated using the following formula: 28.7 x HbA1c - 46.7. Glucose and HbA1c levels were determined using hexokinase and high performance liquid chromatography (HPLC) methods, respectively.

RESULTS

A strong positive correlation between fasting plasma glucose levels and estimated average blood glucose levels (r=0.757, p<0.05) was observed. The difference was statistically significant.

CONCLUSION

Reporting the estimated average glucose level together with the HbA1c level is believed to assist patients and doctors determine the effectiveness of blood glucose control measures.