Data standards in diabetes patient registries

Design and rationale of DISCOVER global registry in type 2 diabetes: Real-world insights of treatment patterns and its relationship with cardiovascular, renal, and metabolic multimorbidities

AIM

The DISCOVER Global Registry (DGR) aims to provide insights into patient attributes and treatment patterns in patients with type 2 diabetes mellitus (T2DM) seen in clinical practice and understand the patterns and impact of treatment strategies on cardio-renal-metabolic multimorbidities. It aims to augment the real-world evidence base created by the DISCOVER study.

METHODS

The ongoing study is a global, prospective, open-source, physician-led registry and involves non-interventional data collection through cloud-based electronic case report form platform from participants with T2DM receiving care as part of routine clinical practice. The DGR will collect longitudinal prospective data on the following: (a) patient, healthcare provider, and healthcare system characteristics; (b) treatment patterns and factors influencing therapy changes; (c) disease duration and glycemic control; (d) management of micro and/or macrovascular complications; (e) management of associated risk factors; (f) outcomes (hospitalization/death), (g) quality of care indicators (eye/foot examination); (h) healthcare resource utilization; and (i) patient-reported outcomes.

CONCLUSION

Establishment of this long-term, scalable, and sustainable global registry offers opportunities to enhance understanding of care gaps, establish quality benchmarks, and understand the role of various treatment strategies in addressing the multifactorial pathophysiology of T2DM and associated comorbidities- potentially enabling transformation of clinical data into actionable insights for improving patient outcomes.

Harnessing Digital Health Technologies to Remotely Manage Diabetic Foot Syndrome: A Narrative Review

About 422 million people worldwide have diabetes and approximately one-third of them have a major risk factor for diabetic foot ulcers, including poor sensation in their feet from peripheral neuropathy and/or poor perfusion to their feet from peripheral artery disease. The current healthcare ecosystem, which is centered on the treatment of established foot disease, often fails to adequately control key reversible risk factors to prevent diabetic foot ulcers leading to unacceptable high foot disease amputation rate, 40% recurrence of ulcers rate in the first year, and high hospital admissions. Thus, the latest diabetic foot ulcer guidelines emphasize that a paradigm shift in research priority from siloed hospital treatments to innovative integrated community prevention is now critical to address the high diabetic foot ulcer burden. The widespread uptake and acceptance of wearable and digital health technologies provide a means to timely monitor major risk factors associated with diabetic foot ulcer, empower patients in self-care, and effectively deliver the remote monitoring and multi-disciplinary prevention needed for those at-risk people and address the health care access disadvantage that people living in remote areas. This narrative review paper summarizes some of the latest innovations in three specific areas, including technologies supporting triaging high-risk patients, technologies supporting care in place, and technologies empowering self-care. While many of these technologies are still in infancy, we anticipate that in response to the Coronavirus Disease 2019 pandemic and current unmet needs to decentralize care for people with foot disease, we will see a new wave of innovations in the area of digital health, smart wearables, telehealth technologies, and “hospital-at-home” care delivery model. These technologies will be quickly adopted at scale to improve remote management of diabetic foot ulcers, smartly triaging those who need to be seen in outpatient or inpatient clinics, and supporting acute or subacute care at home.

A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)

Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs.

Diabetes self-care in primary health facilities in India - challenges and the way forward

India has approximately 73 million people living with diabetes and another 37 million with prediabetes while nearly 47% of the diabetes cases are undiagnosed. The high burden of poor glycemic control and early onset of complications with associated economic costs indicates a high prevalence of poor self-management practices. It is well-established that achieving patient-centered primary care consistent with a chronic care model ensures optimum diabetes self-management support and improves long-term clinical and health outcomes in diabetes patients. The public sector primary care system in India provides services free of cost to beneficiaries but lacks patient-centered care that undermines diabetes self-management education and support. Furthermore, factors like poor patient knowledge of diabetes, suboptimal medication adherence, persistent clinical inertia, lack of data for monitoring and evaluation through clinical audit worsens the standards of diabetes care in primary care settings of India. There is a need for government initiatives to be directed towards the provision of comprehensive outpatient care that is inclusive of uninterrupted supply of drugs, provision of essential laboratory investigators, training and availability of qualified diabetes educators and availability of specialist support when required. Furthermore, the integration of depression screening and smoking cessation services at the primary care level is warranted.

Identifying diabetes cases in health administrative databases: a validation study based on a large French cohort

OBJECTIVES

In the French national health insurance information system (SNDS) three diabetes case definition algorithms are applied to identify diabetic patients. The objective of this study was to validate those using data from a large cohort.

METHODS

The CONSTANCES cohort (Cohorte des consultants des Centres d'examens de santé) comprises a randomly selected sample of adults living in France. Between 2012 and 2014, data from 45,739 participants recorded in a self-administrated questionnaire and in a medical examination were linked to the SNDS. Two gold standards were defined: known diabetes and pharmacologically treated diabetes. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) and kappa coefficients (k) were estimated.

RESULTS

All three algorithms had specificities and NPV over 99%. Their sensitivities ranged from 73 to 77% in algorithm A, to 86 and 97% in algorithm B and to 93 and 99% in algorithm C, when identifying known and pharmacologically treated diabetes, respectively. Algorithm C had the highest k when using known diabetes as the gold standard (0.95). Algorithm B had the highest k (0.98) when testing for pharmacologically treated diabetes.

CONCLUSIONS

The SNDS is an excellent source for diabetes surveillance and studies on diabetes since the case definition algorithms applied have very good test performances.

Balancing the Optimal and the Feasible: A Practical Guide for Setting Up Patient Registries for the Collection of Real-World Data for Health Care Decision Making Based on Dutch Experiences

OBJECTIVES

The aim of this article was to provide practical guidance in setting up patient registries to facilitate real-world data collection for health care decision making.

METHODS

This guidance was based on our experiences and involvement in setting up patient registries in oncology in the Netherlands. All aspects were structured according to 1) mission and goals ("the Why"), 2) stakeholders and funding ("the Who"), 3) type and content ("the What"), and 4) identification and recruitment of patients, data handling, and pharmacovigilance ("the How").

RESULTS

The mission of most patient registries is improving patient health by improving the quality of patient care; monitoring and evaluating patient care is often the primary goal ("the Why"). It is important to align the objectives of the registry and agree on a clear and functional governance structure with all stakeholders ("the Who"). There is often a trade off between reliability, validity, and specificity of data elements and feasibility of data collection ("the What"). Patient privacy should be carefully protected, and address (inter-)national and local regulations. Patient registries can reveal unique safety information, but it can be challenging to comply with pharmacovigilance guidelines ("the How").

CONCLUSIONS

It is crucial to set up an efficient patient registry that serves its aims by collecting the right data of the right patient in the right way. It can be expected that patient registries will become the new standard alongside randomized controlled trials due to their unique value.

Electronic health records and the evolution of diabetes care: a narrative review

Adoption of electronic health records (EHRs) has increased dramatically since the 2009 implementation of the Health Information Technology for Economic and Clinical Health (HITECH) Act. The latest data from the Centers for Disease Control and Prevention (CDC) indicate that the majority of U.S. hospitals and nearly half of U.S. health care professionals have implemented an EHR with advanced functionality.(1) The goals of the HITECH act were not only to incentivize the adoption of EHRs, but also to increase the quality, safety, and efficiency of health care by promoting the concept of "meaningful use."(2,3) The stepwise implementation of "meaningful use" is now entering the latter stages with a focus on improving patient outcomes.(4).

A European venous registry: pitfalls and opportunities

Introduction:

Evidence based medicine is not the ideal way to assess and evaluate treatment success, failure and outcomes. Modern technology makes it possible to register a multitude of information. Advances in the venous field are fast and require a more efficient way to allow selection based on outcomes and quality. Registries are theoretically contain the data needed to investigate venous treatments and instruments.

Materials and methods:

A literature review was performed and twenty-five articles were selected for review.

Results:

Current registries fail to perform as needed and do not deliver the needed information. Separate frameworks and applications are available, but up until now no centralized and combined effort has been made to create a true all encompassing European venous registry.

Conclusions:

A European venous registry containing standardized variables regarding all aspects of venous disease is needed to truly investigate and improve our care. An intuitive and integrated EHR application can facilitate the gathering of data needed to create such a registry. A number of rules apply though.

The NIH Office of Rare Diseases Research patient registry Standard: a report from the University of New Mexico's Oculopharyngeal Muscular Dystrophy Patient Registry

Patient registries remove barriers to performing research by assembling patient cohorts and data in a systematic, efficient, and proactive manner. Consequently, registries are a valuable strategy for facilitating research and scientific discovery. Registries for rare diseases are arguably even more valuable since there is difficulty in assembling cohorts of adequate size for study. Recently, the NIH Office of Rare Diseases Research created a rare disease registry Standard to facilitate research across multiple registries. We implemented the Standard for the Oculopharyngeal Muscular Dystrophy patient registry created at the University of New Mexico Health Sciences Center. We performed a data element analysis for each Common Data Element defined in the Standard. Problems included the use of previous HL7 versions, non-structured data types, and a recent update to the Standard. Overall, the Standard is an excellent first step toward standardizing patient registries to facilitate work on broader questions and promote novel interdisciplinary collaborations.

Diabetes and asthma case identification, validation, and representativeness when using electronic health data to construct registries for comparative effectiveness and epidemiologic research

BACKGROUND

Advances in health information technology and widespread use of electronic health data offer new opportunities for development of large scale multisite disease-specific patient registries. Such registries use existing data, can be constructed at relatively low cost, include large numbers of patients, and once created can be used to address many issues with a short time between posing a question and obtaining an answer. Potential applications include comparative effectiveness research, public health surveillance, mapping and improving quality of clinical care, and others.

OBJECTIVE AND DISCUSSION

This paper describes selected conceptual and practical challenges related to development of multisite diabetes and asthma registries, including development of case definitions, validation of case identification methods, variation in electronic health data sources; representativeness of registry populations, including the impact of attrition. Specific challenges are illustrated with data from actual registries.

Construction of a multisite DataLink using electronic health records for the identification, surveillance, prevention, and management of diabetes mellitus: the SUPREME-DM project

Introduction

Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research.

Methods

We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment.

Results

The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases.

Conclusion

The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.