How good is your glucose control?

Poincaré plot quantification for assessing glucose variability from continuous glucose monitoring systems and a new risk marker for hypoglycemia: application to type 1 diabetes patients switching to continuous subcutaneous insulin infusion

BACKGROUND

The Poincaré plot (PCP) is a valuable tool for describing glucose variability (GV) from continuous glucose monitoring (CGM) but remains only visual and qualitative. The aim of this work was to validate metrics for the geometry of the PCP in type 1 diabetes and to apply them to the study of a series of patients switching to continuous subcutaneous insulin infusion (CSII).

PATIENTS AND METHODS

We reviewed the CGM profiles of 44 patients with type 1 diabetes. A subgroup (n=13) used CGM before and after 6 months on CSII. Additionally, we prospectively collected seven recordings from healthy controls. The new PCP metrics were correlated with hypoglycemia and classical GV indices and were compared between groups.

RESULTS

SDs related to the PCP fitting ellipse (SD1, SD2) and area and shape of the fitting ellipse (SFE) were all higher in diabetes patients than in the controls and decreased significantly on CSII. SD1 represented short-term GV and was equivalent to continuous overlapping net glycemic action (CONGA). SD2 represented long-term GV and correlated with the SD of glucose levels (r ≥ 0.98), mean of daily differences (r ≥ 0.91), and mean amplitude of glycemic excursions (r ≥ 0.88). SFE correlated positively with CONGA at 1 h but not with the other indices and was inversely correlated with hypoglycemic episodes (Spearman's ρ=-0.42), independently of the coefficient of variation and the Low Blood Glucose Index in a multivariate analysis (partial r=-0.34).

CONCLUSIONS

PCP metrics are correlated with known GV indices and may be used for the study of CGM recording series in type 1 diabetes. SFE is a new risk marker for hypoglycemia.

Automation of the consensus guidelines in diabetes care: potential impact on clinical inertia

OBJECTIVE

To propose that automation of the consensus guidelines and mandated targets (CG&MT) in glycemia, hemoglobin A1c, and body weight will facilitate optimal clinical management of patients with diabetes.

METHODS

(1) A simplified method for capturing diabetes outcomes at home was devised, (2) relevant portions of the CG&MT were translated into computer code and automated, and (3) algorithms were applied to transform data from self-monitoring of blood glucose into circadian profiles and hemoglobin A1c levels. (4) The resulting procedures were integrated into a USB memory drive for use by health-care providers at the point of care.

RESULTS

For input from patients, a simple form is used to capture data on diabetes outcomes, including blood glucose measurements before and after meals and at bedtime, medication, and lifestyle events in a structured fashion. At each encounter with a health-care provider, the patient's data are transferred into the device and become available to assist in identifying deviations from mandated targets, potential risks of hypoglycemia, and necessary prescription changes. Preliminary observations during a 2 1/2-year period from a community support group dedicated to glycemic control on 20 unselected patients (10 with and 10 without use of the device) are summarized.

CONCLUSION

With use of the automated information, the health professional is supported at the point of care to achieve better, safer outcomes and practice evidence-based medicine entirely in lockstep with the CG&MT. This automation helps to overcome clinical inertia.

Glycaemic risk assessment in children and young people with Type 1 diabetes mellitus

AIM

To ascertain if those with diabetes (and their carers) ascribe a similar level of risk to blood glucose control as healthcare professionals.

METHODS

We used a structured questionnaire to ask fifty healthcare professionals how 'dangerous' a given blood glucose value was. Their answers were modelled to produce an algorithm of assessed risk. To examine if patients (and their carers) would apportion a similar level of risk to that of healthcare professionals, the same questionnaire was issued to fifty children and adolescents with Type 1 diabetes. For patients under 8 years old the carers completed the questionnaires (n = 23). Both patient and carers together completed the questionnaire for those aged 8-11 years (n = 15) and patients over the age of 11 years completed the questionnaire themselves (n = 12). The median results and interquartile range of the assessed level of risk, as determined by the two groups, were compared using a generalized linear model.

RESULTS

A significant difference (P < 0.0001) was identified between the median risk assessments of the two groups. The zero level of assessed risk was upward shifted in the patient group by 0.8 mmol/l and indicated the patients' view of risk increased.

CONCLUSIONS

Patients with Type 1 diabetes (and their carers) evaluate the risk from blood glucose values differently from healthcare professionals. The euglycaemic state (zero ascribed risk) that patients chose was 0.8 mmol/l greater than that of healthcare professionals, indicating, perhaps, hypoglycaemia avoidance, a more pragmatic approach or less exposure to current trends in glycaemic control.

A method for assessing quality of control from glucose profiles

AIM

As the practice of multiple assessments of glucose concentration throughout the day increases for people with diabetes, there is a need for an assessment of glycaemic control weighted for the clinical risks of both hypoglycaemia and hyperglycaemia.

METHODS

We have developed a methodology to report the degree of risk which a glycaemic profile represents. Fifty diabetes professionals assigned risk values to a range of 40 blood glucose concentrations. Their responses were summarised and a generic function of glycaemic risk was derived. This function was applied to patient glucose profiles to generate an integrated risk score termed the Glycaemic Risk Assessment Diabetes Equation (GRADE). The GRADE score was then reported by use of the mean value and the relative percent contribution to the weighted risk score from the hypoglycaemic, euglycaemic, hyperglycaemic range, respectively, e.g. GRADE (hypoglycaemia%, euglycaemia%, hyperglycaemia%).

RESULTS

The GRADE scores of indicative glucose profiles were as follows: continuous glucose monitoring profile non-diabetic subjects GRADE = 1.1, Type 1 diabetes continuous glucose monitoring GRADE = 8.09 (20%, 8%, 72%), Type 2 diabetes home blood glucose monitoring GRADE = 9.97 (2%, 7%, 91%).

CONCLUSIONS

The GRADE score of a glucose profile summarises the degree of risk associated with a glucose profile. Values < 5 correspond to euglycaemia. The GRADE score is simple to generate from any blood glucose profile and can be used as an adjunct to HbA1c to report the degree of risk associated with glycaemic variability.

Averting iatrogenic hypoglycemia through glucose prediction in clinical practice: progress towards a new procedure in diabetes

BACKGROUND

Hypoglycemia is a risk factor common to all insulin therapy. The hypothesis is that efforts to reduce or prevent this adverse side effect may fail because providers generally lack the resources to predict not only future blood glucose levels but also future risks of hypoglycemia. This lack has been remedied. A controlled study was undertaken to test the hypothesis.

METHODS

Twenty-two insulin dependent subjects suffering more than one (1) episode/week of hypoglycemia with similar insulin regimens, similar diabetes education and similar self-management training participated in this study. For all subjects, a remote monitoring resource (registry and database) was used to capture daily SMBG and afford a return path for provider interventions and decision support. Identical telemedical methods were used which differed only for the provider either by the presence (prediction group) or by the absence (control group) of an on-screen, visual display of predicted glycemia and predicted risks of hypoglycemia. The study lasted 2 months.

RESULTS

Over an average of 41 days from baseline to follow up and while using the glycemic prediction resource, providers intervened more effectively in the prediction group reducing rates of hypoglycemia nine-fold (P<0.0001) and insulin therapy by just -9 U/day (P<0.01). Mean pre-meal glycemia was not compromised. Over 61 days from baseline to final follow up but without glycemic predictions in the control group, providers' interventions were less effective and resulted in no net changes in rates of hypoglycemia, daily insulin therapy, or mean pre-meal glycemia.

CONCLUSIONS

Given knowledge of future glycemia and future risks of hypoglycemia, providers in clinical practice can now avert iatrogenic hypoglycemia in less than 2 months. A shared diabetes data center furnishing remote data capture and decision support is fundamental to the implementation of this as a new clinical procedure in diabetes.