Assessing Quality of Life for New Diabetes Treatments and Technologies: A Simple Patient-Centered Score

Background

A simple quality of life measure is needed for use in diabetes, particularly for the assessment of new treatments and technologies. We devised and validated a patient-centered quality of life (PCQoL) measure that should be applicable to routine clinical practice or trial of therapies.

Methods

People with diabetes completed a two-part, PCQoL questionnaire where they nominated five aspects of general- and diabetes-related life judged most important for their overall quality of life and rated each for current level of satisfaction. Scores derived from the questionnaire were compared with a reference measure—the Diabetes Control and Complications Trial diabetes quality of life (DQoL) score. Both were repeated after 1 month. The participants were 72 diabetic patients (60% with type 1 diabetes); 29 people with type 1 diabetes were treated by continuous subcutaneous insulin infusion (CSII) and 14 were treated by multiple daily insulin injections (MDI).

Results

Patients most often cited family and relationships or fear of complications and hypoglycemia as important facets of quality of life for them. The PCQoL measure was highly correlated with the reference DQoL measure (r = 0.73, p < 0.0001), had high repeatability (r = 91, p < 0.0001), and could be completed in less than 5 minutes. The test was sensitive enough to detect a better quality of life in CSII-treated patients vs MDI-treated patients.

Conclusion

The PCQoL measure is simple, quick, valid, and suitable for routine use in diabetes or trials of new treatments.     

Personalized Medicine for Diabetes: Technologies for Diabetes Genomics

The genetic risk for diabetes largely depends on the type of diabetes and the penetrance and severity of the effect of the contributing genes. This ranges from the high-risk mutations of neonatal diabetes and maturityonset diabetes of the young to the lower, but still significant, risk conferred by common human leukocyte antigen alleles in type 1 diabetes to the still-lower risk conferred by the common variants associated with type 2 diabetes. There are many new molecular technologies, each with their own set of methodological issues, that have been used for genome-wide association studies and that can be used for determining the genetic risk for these various types of diabetes. These technologies include whole genome single nucleotide polymorphism microarrays, high-throughput polymorphism analyzers, next-generation sequencers, and copy-number variant technologies.     

Data Mining Technologies for Blood Glucose and Diabetes Management

Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management context is particularly well suited to a data mining approach. The availability of electronic health records and monitoring facilities, including telemedicine programs, is leading to accumulating huge data sets that are accessible to physicians, practitioners, and health care decision makers. Moreover, because diabetes is a lifelong disease, even data available for an individual patient may be massive and difficult to interpret. Finally, the capability of interpreting blood glucose readings is important not only in diabetes monitoring but also when monitoring patients in intensive care units. This article describes and illustrates work that has been carried out in our institutions in two areas in which data mining has a significant potential utility to researchers and clinical practitioners: analysis of (i) blood glucose home monitoring data of diabetes mellitus patients and (ii) blood glucose monitoring data from hospitalized intensive care unit patients.     

European Association for the Study of Diabetes 2018

Martin J. Kurian, Peter J. Rentzepis, Ann M. Carracher, and Kelly L. Close are of Close Concerns ( http://www.closeconcerns.com ), a healthcare information company focused exclusively on diabetes and obesity care. Close Concerns publishes Closer Look, a periodical that brings together news and insights in these areas. Each month, the Journal of Diabetes includes this News feature, in which Kurian, Rentzepis, Carracher, and Close review the latest developments relevant to researchers and clinicians.     

The Benefits,Limitations, and Cost-Effectiveness of Advanced Technologies in the Management of Patients With Diabetes Mellitus

Background:

Hypoglycemia mitigation is critical for appropriately managing patients with diabetes. Advanced technologies are becoming more prevalent in diabetes management, but their benefits have been primarily judged on the basis of hemoglobin A1c. A critical appraisal of the effectiveness and limitations of advanced technologies in reducing both A1c and hypoglycemia rates has not been previously performed.

Methods:

The cost of hypoglycemia was estimated using literature rates of hypoglycemia events resulting in hospitalizations. A literature search was conducted on the effect on A1c and hypoglycemia of advanced technologies. The cost-effectiveness of continuous subcutaneous insulin infusion (CSII) and real-time continuous glucose monitors (RT-CGM) was reviewed.

Results:

Severe hypoglycemia in insulin-using patients with diabetes costs $4.9-$12.7 billion. CSII reduces A1c in some but not all studies. CSII improves hypoglycemia in patients with high baseline rates. Bolus calculators improve A1c and improve the fear of hypoglycemia but not hypoglycemia rates. RT-CGM alone and when combined with CSII improve A1c with a neutral effect on hypoglycemia rates. Low-glucose threshold suspend systems reduce hypoglycemia with a neutral effect on A1c, and low-glucose predictive suspend systems reduce hypoglycemia with a small increase in plasma glucose levels. In short-term studies, artificial pancreas systems reduce both hypoglycemia rates and plasma glucose levels. CSII and RT-CGM are cost-effective technologies, but their wide adoption is limited by cost, psychosocial, and educational factors.

Conclusions:

Most currently available technologies improve A1c with a neutral or improved rate of hypoglycemia. Advanced technologies appear to be cost-effective in diabetes management, especially when including the underlying cost of hypoglycemia.     

Computerized Learning Technologies for Diabetes: A Systematic Review

Background

The objective of this study was to evaluate computerized learning technology interventions that can empower patients in the self-management of diabetes and support diabetes education over a distance.

Methods

We searched Medline (1966–2006), CINAHL (1982–2006), and the Cochrane Central Register of Controlled Trials (first quarter 2007) databases. We also reviewed reference lists from included studies to identify additional studies. We included 25 articles representing 21 randomized controlled trials that evaluated a computerized learning technology and measured the outcome of patient care. We extracted patient sample, intervention, educational content topics, outcome measures, and statistical significance.

Results

Of 21 eligible trials, 18 trials (85.7%) reported significant positive outcomes. Almost 44% (43.8%) of the outcomes demonstrated significant improvements (49 of 112 outcomes).

Conclusions

Patient self-management behaviors are important in chronic disease management, and initial evidence suggests that computerized learning technology interventions can play a significant role in the future.     

Health Technologies for Monitoring and Managing Diabetes: A Systematic Review

Background

The primary objective of this review was to determine the strength of evidence for the effectiveness of self-monitoring devices and technologies for individuals with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) based on specific health-related outcome measures. Self-monitoring devices included those that assist patients with managing diabetes and preventing cardiovascular complications (CVCs). A secondary objective was to explore issues of feasibility, usability, and compliance among patients and providers.

Methods

Study criteria included individuals ≥14 years and youth (7–14 years) with T1DM or T2DM, intervention with a self-monitoring device, assessment of clinical outcomes with the device, literature in English, and ≥10 participants. Relevant published literature was searched from 1985 to 2008. Randomized controlled trials and observational studies were included. Data were extracted for clinical outcomes, feasibility and compliance methods, and results. Selected studies were independently evaluated with a validated instrument for assessing methodological quality.

Results

Eighteen trials were selected. Predominant types of device interventions included self-monitoring of blood glucose, pedometers, and cell phone or wireless technologies. Feasibility and compliance were measured in the majority of studies.

Conclusions

Self-monitoring of blood glucose continues to be an effective tool for the management of diabetes. Wireless technologies can improve diabetes self-care, and pedometers are effective lifestyle modification tools. The results of this review indicate a need for additional controlled trial research on existing and novel technologies for diabetes self-monitoring, on health outcomes associated with diabetes and CVCs, and device feasibility and compliance.     

Adverse Event Data for Years 2018 to 2020 for Diabetes Devices

Unlike performance evaluations, which are often conducted under ideal conditions, adverse events occur during actual device use for people with diabetes. This report summarizes the number of adverse events for the years 2018 to 2020 for the 3 diabetes devices: blood glucose meters (BG), continuous glucose monitors (CGM), and insulin pumps. A text example of a CGM injury is provided. Possible reasons are suggested for trends. Whereas the rate per test result (events/usage) is exceedingly small, the rate per patient (events/people with diabetes that use insulin) is of concern. Hence, it is important to determine event causes and provide corrective actions. The first step is to put in place routine analysis of adverse event data for diabetes devices.     

Proteomic Technologies for the Discovery of Type 1 Diabetes Biomarkers

In this review, we discuss several important issues concerning the discovery of protein biomarkers for complex human diseases, with a focus on type 1 diabetes. Serum or plasma is the first choice of specimen due to its richness in biological information and relatively easy availability. It is a challenging task to comprehensively characterize the serum/plasma proteome because of the large dynamic range of protein concentration. Therefore, sample pretreatment is required in order to explore the low- to medium-abundance proteins contained in serum/plasma. In this regard, enrichment of low-abundance proteins using random hexapeptide library beads has distinct advantages over the traditional immune-depletion methods, including higher efficiency, higher binding capacity, and lower cost. In-depth mining of serum/plasma proteome using different separation techniques have also been evaluated and are discussed in this review. Overall, the shotgun proteomics—multidimensional separation of digested peptides followed by mass spectrometry analysis—is highly efficient and therefore has become a preferred method for protein biomarker discovery.     

New Technologies for Diabetes Presented at International Congresses in 2006

Diabetes is a medical specialty that is currently experiencing the rapid development of new technologies that can be applied to clinical management. At three international Diabetes Congresses held in 2006 (the annual meeting of European Association of Diabetes in Copenhagen, the World Congress of the International Diabetes Federation in Cape Town, and the Diabetes and Technology meeting in Atlanta), several new technologies and devices were demonstrated that are applicable to diabetes care. Out of various technological innovations, this article highlights three new interesting areas, which may represent the principal direction of future developments in science that may help improve the quality of life for the person with diabetes.     

Diabetes Technologies and Hospital Care: The Effect of Diabetes on Hospital Readmissions

Hospital readmission is an important contributor to total medical expenditures and is an emerging indicator of quality of care. Diabetes, similar to other chronic medical conditions, is associated with increased risk of hospital readmission. Risk factors include previous hospitalization, extremes in age, and socioeconomic barriers. Preliminary studies suggest that acute and/or chronic glycemic control may be of importance when diabetes is the primary diagnosis or when it is a comorbidity. Very limited evidence from prospective randomized controlled trials aimed at improving glycemic control is available. However, whether one concludes that inpatient or outpatient glycemic control is partly responsible for reduced hospitalizations, attention to glycemic control in the hospital may facilitate sustained glycemic control post-discharge. Limited prospective and retrospective evidence suggest that the involvement of a diabetes specialist team may improve readmission rates, but attention to more generalized comprehensive approaches may also be worthwhile. Prospective interventional studies targeting interventions for improving glycemic control are needed to determine whether glycemic control impacts readmission rates.     

Virus Infections as Potential Targets of Preventive Treatments for Type 1 Diabetes

Environmental factors play an important role in the pathogenesis of type 1 diabetes, and are attractive targets for preventive interventions. Several studies have shown that viruses can cause diabetes in animals, indicating their potential as candidates for environmental triggering agents. However, human studies have been hampered by the complex nature of the disease pathogenesis, leaving the question of viral etiology unanswered. Significant progress has recently been made in this field by searching for viruses within pancreatic tissue samples, and by carrying out prospective studies. Consequently, there is increasing evidence for a group of enteroviruses acting as possible environmental key triggers. In past studies, these viruses have been linked to type 1 diabetes. Recent studies have shown that they exert tropism to pancreatic islets, and that they are associated with the start of the beta-cell damaging process. Also, polymorphisms of the gene coding for the innate immune system sensor for enteroviruses (IFIH1) were found to modulate the risk of diabetes. Based on these findings, interest in the possible development of vaccines against these viruses has increased. However, even if enterovirus vaccines (polio vaccines) are effective and safe, we currently lack necessary information for the development of a vaccine against diabetogenic enteroviruses, e.g. regarding the identification of their specific serotypes and the causal relationship between these viruses and diabetes initiation. Ongoing research projects are currently addressing these questions, and will hopefully increase the consensus in this field. Also, new sequencing technologies will provide additional information about the whole virome, which could enable the discovery of new candidate viruses.     

Diabetes Mellitus in Advanced Heart Failure

BackgroundDiabetes mellitus is associated with increased rates of mortality in patients with less severe (stage C) heart failure (HF). The prevalence of diabetes and its complications in advanced (stage D) HF and their contributions to mortality risk are unknown.Methods and ResultsWe conducted a retrospective population-based cohort study of all adult residents of Olmsted County, Minnesota, who had advanced HF between 2007 and 2017. Patients with diabetes were identified by using the criteria of the Healthcare Effectiveness Data and Information Set. Diabetes complications were captured by using the Diabetes Complications Severity Index. Of 936 patients with advanced HF, 338 (36.1%) had diabetes. Overall, median survival time after development of advanced HF was 13.1 (3.9–33.1) months; mortality did not vary by diabetes status (aHR 1.06, 95% CI 0.90–1.25; P = 0.45) or by glycated hemoglobin levels in those with diabetes (aHR 1.01 per 1% increase, 95% CI 0.93–1.10; P = 0.82). However, patients with diabetes and 4 (aHR 1.24, 95% CI 0.92–1.67) or 5–7 (aHR 1.49, 95% CI 1.09–2.03) diabetes complications were at increased risk of mortality compared to those with ≤ 3 complications.ConclusionsMore than one-third of patients with advanced HF have diabetes. In advanced HF, overall prognosis is poor, but we found no evidence that diabetes is associated with a significantly higher mortality risk.