Adam White

Review of the effect of internet therapeutic intervention in patients with type 1 and type 2 diabetes.

The use of the Internet has changed the way health care professionals manage diabetes, with platforms now available allowing patients to upload self-monitoring of blood glucose data and share with their health care provider (1). Previous studies have established the efficacy of Internet blood glucose monitoring systems (1–3). It is now our standard of care to offer an Internet blood glucose monitoring system to patients. We currently have 1,100 patients enrolled and have outcome data on the first 409 patients. Of the 409 patients, 388 had HbA1c at baseline and at least one subsequent HbA1c determination within 9 months. HbA1c values from 3–9 months were averaged to generate follow-up data. The relationship of reporting frequency and HbA1c change was determined by dividing patients into frequent reporters, who reported more than once per month, and infrequent reporters. Patients were instructed to upload self-monitoring …

A Comparison of Internet Monitoring with Continuous Glucose Monitoring in Insulin-Requiring Type 2 Diabetes Mellitus

OBJECTIVE To compare the effects of real-time continuous glucose monitoring (RT-CGM) and an Internet blood glucose monitoring system (IBGMS) on glycated hemoglobin levels in patients with type 2 diabetes mellitus treated with insulin. METHODS Fifty-seven patients with type 2 diabetes treated with insulin were assigned randomly to 1 of 2 groups. Group 1 had the results of their self-monitoring of blood glucose level monitored biweekly using an IBGMS. Group 2 used RT-CGM and were monitored biweekly. Both groups used a secure website to upload data and to receive feedback from their endocrinologist. A1C and laboratory test results were collected at 0, 3 and 6 months. RESULTS The baseline parameters were not significantly different. After a 6-month follow-up period, both IBGMS and RT-CGM showed significant within-group improvements in A1C level. In the IBGMS group, the A1C level decreased from 8.79%±1.25% to 7.96%±1.30% (p<0.05). The RT-CGM group decreased from 8.80%±1.37% to 7.49%±0.70% (p<0.001). IBGMS and RT-CGM did not show significantly different A1C levels at baseline, 3 and 6 months (p>0.05). CONCLUSIONS The use of both IBGMS and RT-CGM significantly improved A1C levels in patients with type 2 diabetes treated with insulin in a randomized trial over a 6-month period. There were no significant differences in A1C values between groups after 6 months.

Cost-benefit analysis of internet therapeutic intervention on patients with diabetes.

Background: With the emergence of IBGMS for allowing for patients to communicate their self-monitored blood glucose (SMBG) readings with their health care providers, their impact on the management of diabetes is becoming well-supported with regards to clinical benefits. Their impact on healthcare costs, however, has yet to be investigated. This study aims to determine the cost-benefits of such interventions in comparison to routine care. Objectives: To analyze the cost-benefit of an Internet Blood Glucose Monitoring Service (IBGMS) in comparison to routine diabetes care. Patients and Methods: 200 patients were surveyed to assess the cost associated with doctor appointments in the past 12 months. Annual number of visits to medical services for diabetes and costs of transportation, parking, and time taken off work for visits were surveyed. Self-reported frequency of SMBG and most recent A1C were also surveyed. We compared 100 patients who used the IBGMS with 100 patients who only used routine care. Results: There is a trend of lowered total cost in the intervention group compared to the control group. The control group spent

Blood glucose self-monitoring and internet diabetes management on A1C outcomes in patients with type 2 diabetes.

210.89 per year on visits to physicians; the intervention group spent

WITHDRAWN: Internet Diabetes Management: A Practical Approach

131.26 (P = 0.128). Patients in control group visited their endocrinologist 1.76 times per year, those in intervention group visited their endocrinologist 1.36 times per year, significantly less frequently than the control group (P = 0.014). Number of visits to other medical services is similar between the groups. Average A1C in intervention group is 7.57%, in control group is 7.69% (P = 0.309). Conclusions: We have demonstrated that IBGMS, while not reaching statistical significance, may be associated with slightly reduced A1C and cost due to visiting physicians.

Determining starting basal rates of insulin infusion for insulin pump users: a comparison between methods

Objectives The purpose of this study was to determine any correlation between frequency of self-monitoring of blood glucose (SMBG), frequency of patient-provider communication of SMBG (reporting), and hemoglobin A1C for patients with non-insulin-dependent diabetes solely on oral medications. Research design and methods 191 charts of patients with type 2 diabetes treated solely with oral hypoglycemic agents were reviewed retrospectively. A1C, SMBG frequency, and frequency of online communication with an endocrinologist within the most recent 6-month period were used in the analyses. Regression analysis was used to determine correlations to A1C. For subsequent subgroup analysis, patients were separated into infrequent and frequent SMBG groups, defined as those who test on average once or less per day or twice or more per day. Results Although testing frequency did not correlate with A1C, higher reporting frequency correlated with lower A1C. Subgroup analysis of the frequent SMBG group showed a significantly lower A1C in frequent reporters when compared to infrequent reporters (N=118, p<0.05). This trend was not observed in the infrequent SMBG group (N=73, p=0.161). Conclusions The inverse correlation between reporting frequency and A1C, as well as the significant difference in A1C only for the frequent testers, suggests that frequent SMBG has an effect on reducing A1C only when combined with regular, frequent communication of SMBG with a healthcare provider.

Microfluidic cell trap and assay apparatus for high-throughput analysis

Hugh D. Tildesley, MD, Nelson Chow, BSc, Adam White, MD, Monika Pawlowska, MD, Stuart A. Ross, MD a Division of Endocrinology, St Pauls Hospital, Vancouver, British Columbia, Canada, and Department of Endocrinology and Metabolism, University of British Columbia, Vancouver, British Columbia, Canada b Department of Biochemistry, University of British Columbia, Vancouver, British Columbia, Canada c Department of Endocrinology and Metabolism, University of British Columbia, Vancouver, British Columbia, Canada d Department of Medicine, University of Calgary, Calgary, Alberta, Canada

Papillary thyroid carcinoma: prognostic significance of cancer presentation

Objective We aimed to assess the accuracy and safety of presently available methods of estimating starting basal insulin rates for patients with type 1 and 2 diabetes, and to compare them against an empirically derived standard basal rate and a newly developed regression formula. Research design and methods Data on 61 patients with type 1 diabetes on continuous subcutaneous insulin infusion (CSII) therapy and 34 patients with type 2 diabetes on CSII were reviewed. Patient data were first analyzed for correlations between initial patient parameters and final basal rates. Starting basal rates were then retrospectively calculated for these patients according to the weight-based method (WB-M), the total daily dose (TDD) of insulin method (TDD-M), a flat empiric value, and a new formula developed by regression analysis of clinical data. These 4 methods were subsequently compared in their accuracy and potential risk of hypoglycemia. Results For type 1 diabetes, patient weight and TDD of long-acting insulin correlated with final basal rates. Both the regression formula and the TDD-M appeared safer than the WB-M and empirical estimates. For type 2 diabetes, only patient TDD of long-acting insulin correlated with final basal rates. The regression formula was significantly more accurate for patients with type 2 diabetes overall, but the TDD-M estimate was marginally safer. Conclusions The pre-existing TDD-M was found to be the safest presently recommended estimate of initial basal rates for pump initiation in both type 1 and 2 diabetes. The best-fit regression was found to have potential use for type 2 CSII initiation.