Self-Monitoring of Blood Glucose as an Integral Part in the Management of People with Type 2 Diabetes Mellitus

Availability, price and affordability of insulin, delivery devices and self-monitoring blood glucose devices in Indonesia

Insulin is essential for the survival of people with type 1 diabetes and for better management of people with type 2 diabetes. People with diabetes using insulin also require self-monitoring blood glucose (SMBG) devices (e.g., meters, strips, continuous monitoring systems) for day-to-day management. It is essential to ensure that insulin and these devices are available and affordable. This study aimed to evaluate the availability, price, and affordability of insulin and SMBG devices in Indonesia using an adaptation of the World Health Organization/Health Action International (WHO/HAI) price survey. A total of 34 public health facilities (hospitals, primary healthcare centres/Puskesmas) and 37 private pharmacies were sampled. Information from three major online marketplaces was also collected. Prices were expressed as median patient prices (US$). Affordability was defined as the number of days' wages needed by the lowest paid unskilled government worker (LPGW) to purchase 30 days' supply of insulin, delivery devices and SMBGs. Availability of analogue insulin was slightly higher in public facilities (63.6%) than in the private sector (43.2%), with no human insulin available in both sectors. Conversely, better availability was observed in private facilities for SMBG devices as public sector facilities did not supply devices for self-testing. Median prices for 1000IU analogues varied between the public sector (US$ 5.26) and the private sector (US$11.24). The highest median price of analogues was seen in online marketplaces (US$ 28.65). The least costly median price of SMBG devices were observed in online platforms (meter: US$ 18.37, test strip: US$ 0.27, lancet: US$ 0.02). A low-income person had to work 2-3 days to buy 1000IU of analogues. It required 5-7 days' and 4-5 day's wages to purchase a meter and a month's supply of test strips, respectively. The availability and affordability of insulin and SMBG devices remain important issues in Indonesia requiring holistic approaches for further improvement.

Initiating an Insulin Safety Campaign to reduce the incidence of glycemic harm events for hospitalized adults 65 and older

BACKGROUND

During calendar years 2021 through 2023, our health system admitted 200,837 persons 18 years and older with diabetes, of which 61% (n = 123,393) were 65 years and older with orders for insulin administration. The incidence of diabetes among hospitalized persons 65 and older continues to increase in the United States, with 24 million adults 65 and older with diabetes reported in 2020. Insulin, a high-risk medication, has the potential for adverse drug events, which can cause significant harm to patients, potentially resulting in death. With the 2023 initiation of voluntary electronic clinical quality measures reporting for severe glycemic harm events from the Centers for Medicare Services, the study team saw an opportunity to evaluate and standardize insulin-related practices across the system.

METHODS

We implemented an Insulin Safety Campaign (ISC), to review, evaluate, and standardize insulin-related processes across our health system. The primary goal was to reduce severe glycemic harm events system-wide. Insulin-related practices were reviewed for best practice alignment and standardized. Outcomes were measured according to the Centers for Medicare and Medicaid Services' electronic clinical quality measures reporting guidelines.

RESULTS

Comparing pre-and post-implementation results, all five medical centers achieved statistically significant reductions in sever hyper- and hypoglycemic harm events.

CONCLUSIONS

Through a collaborative effort, we were able to identify, address, and reduce insulin-related process variabilities through standardization, reducing the percentage of severe glycemic harm events and improving blood glucose management in our hospitalized persons 65 and older.

Digital technology and healthcare delivery in insulin-treated adults with diabetes: a proposal for analysis of self-monitoring blood glucose patterns using a dedicated platform

PURPOSE

A remote platform for diabetes care (Roche Diabetes® Care Platform, RDCP) has been developed that allows combined face-to-face consultations and remote patient monitoring (RPM).

METHODS

A dedicated flowchart is proposed as a clinical approach to help healthcare professionals in the appropriate interpretation of structured self-monitoring blood glucose data, as visualized on the RDCP during the visits, and in the optimal management of patients using the integrated RDCP-RPM tools.

RESULTS

The platform organizes patterns in different blocks: (i) hypoglycemia; (ii) hyperglycemia; (iii) blood glucose variability; (iv) treatment adherence, which identifies a possible individual pattern according to glycemic control challenges, potential causal factors, and behavioral type patterns. The flowchart proposed for use of the RDCP-RPM is self-explanatory and entails 3 steps: (1) evaluation of quality and quantity of self-monitoring blood glucose data; (2) pattern analysis; (3) personalized suggestions and therapy changes.

CONCLUSION

The main aim of the remote treatment flowchart proposed is to support healthcare professionals in the identification of hypoglycemic and hyperglycemic patterns using the RDCP regardless of the HbA1c value and ongoing treatment, which however, become crucial in combination with pattern analysis in the therapeutical choice.

Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

How to evaluate over 60 million blood glucose data - The design of the MÉRY Diabetes Database

AIMS

The aim of the article is to describe the method for creating a close to ideal diabetes database. The MÉRY Diabetes Database (MDD) consists of a large quantity of reliable, well-maintained, precise and up-to-date data suited for clinical research with the intention to improve diabetes care in terms of maintaining targeted blood glucose levels, avoiding hypoglycemic episodes and complications and improving patient compliance and quality of life.

METHODS

Based on the analysis of the databases found in the literature and the experience of our research team, nine important characteristics were identified as critical to an ideal diabetes database. The data for our database is collected using MÉRYkék glucometers, a device that meets all requirements of international regulations and measures blood glucose levels within the normal range with appropriate precision (10 %).

RESULTS

Using the key characteristics defined, we were able to create a database suitable for the analysis of a large amount of data regarding diabetes care and outcomes.

CONCLUSIONS

The MDD is a reliable and ever growing database which provides stable and expansive foundation for extensive clinical investigations that hold the potential to significantly influence the trajectory of diabetes care and enhance patient outcomes.

The Evolution of Diabetes Technology - Options Towards Personalized Care

Advances in diabetes technology, especially in the last few decades, have transformed our ability to deliver care to persons with diabetes (PWD). Developments in glucose monitoring, especially continuous glucose monitoring systems (CGM), have revolutionized diabetes care and empowered our patients to manage their disease. CGM has also played an integral role in advancing automated insulin delivery systems. Currently available and upcoming advanced hybrid-closed loop systems aim to decrease patient involvement and are approaching the functionality of a fully automated artificial pancreas. Other advances, such as smart insulin pens and daily patch pumps, offer more options for patients and require less complicated and costly technology. Evidence to support the role of diabetes technology is growing, and PWD and clinicians must choose the right type of technology with a personalized strategy to manage diabetes effectively. Here, we review currently available diabetes technologies, summarize their individual features and highlight key patient factors to consider when creating a personalized treatment plan. We also address current challenges and barriers to the adoption of diabetes technologies.

Direct-to-consumer testing - benefits for consumers, people with disease and public health

Direct-to-consumer (DTC) tests can be defined as any in-vitro diagnostic (IVD) test or, more broadly, any medical test using an IVD or medical device, that is marketed directly to consumers without involvement of a health care provider (HCP). Examples are pregnancy tests, alcohol breath tests, blood pressure measurements (medical device), coagulation tests (INR), self-monitoring of blood glucose, continuous glucose monitoring (medical device), HIV tests, HPV tests, SARS-CoV-2 antigen tests, or genetic tests. DTC tests fulfil various customer needs such as making rapid decisions (e.g. glucose monitoring for insulin dosing, SARS-CoV-2 antigen test, hormone test identifying fertile days, alcohol test), monitoring chronic conditions between consultations (e.g. diabetes, lipidaemia, hypertension), saving time and reducing consultations (e.g. INR, SARS-CoV-2 antigen test, blood pressure monitoring), screening for disease when no symptoms are present (e.g. occult blood, cholesterol, triglycerides, SARS-CoV2 antigen test), or maintaining privacy (e.g. pregnancy test, HIV test, HPV test, certain genetic tests). Further, DTC tests can reduce cost and expand access to care in countries with limited resources and can support healthcare systems in extraordinary circumstances such as a pandemic. Valid concerns about DTC testing need to be described, addressed and resolved with the help of authorities and regulators in collaboration with HCP and should not detract from the advantages DTC tests can provide. HCP should play a more prominent role in educating the public through mass media and social media on the proper use of DTC tests and help to pinpoint problem areas.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

Background

Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.

Methods

Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Results

The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.

Conclusions

The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.