Medications for type 2 diabetes: how will we be treating patients in 50 years?

The past 50 years have seen the development of many new options for treating and preventing type 2 diabetes. Despite this success, the individual and societal burden of the disease continues unabated. Thus, the next 50 years will be critical if we are going to quell the major non-communicable disease of our time. The knowledge we will gain in the next few years from clinical studies will inform treatment guidelines with regard to which agents to use in whom and whether more aggressive approaches can slow the development of hyperglycaemia in those at high risk. Beyond that, we anticipate identification of novel targets and techniques for therapeutic intervention. These advances will lead to more personalised approaches to treatment. Most importantly, we will need to focus our political and economic efforts on enhancing and implementing public health approaches aimed at prevention of diabetes and its co-morbidities. This is one of a series of commentaries under the banner ‘50 years forward’, giving personal opinions on future perspectives in diabetes, to celebrate the 50th anniversary of Diabetologia (1965–2015)

ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks

Cross-institutional healthcare predictive modeling can accelerate research and facilitate quality improvement initiatives, and thus is important for national healthcare delivery priorities. For example, a model that predicts risk of re-admission for a particular set of patients will be more generalizable if developed with data from multiple institutions. While privacy-protecting methods to build predictive models exist, most are based on a centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure (and single-point-of-breach) and accidental or malicious modification of records. In this article, we describe a new framework, ModelChain, to adapt Blockchain technology for privacy-preserving machine learning. Each participating site contributes to model parameter estimation without revealing any patient health information (i.e., only model data, no observation-level data, are exchanged across institutions). We integrate privacy-preserving online machine learning with a private Blockchain network, apply transaction metadata to disseminate partial models, and design a new proof-of-information algorithm to determine the order of the online learning process. We also discuss the benefits and potential issues of applying Blockchain technology to solve the privacy-preserving healthcare predictive modeling task and to increase interoperability between institutions, to support the Nationwide Interoperability Roadmap and national healthcare delivery priorities such as Patient-Centered Outcomes Research (PCOR)

‘They need to tell you and not just do it’: Veteran and physician perspectives on point-of-care research in VA

Point-of-care research (POCr) is part of a larger effort to advance the Veterans Health Administration (VHA) as a learning health system. It has the potential to improve health care outcomes and drive down costs of research by evaluating “in-use” medications and therapies. However, patients and physicians must be inclined to participate in this type of research. There is a need to assess patient and physician willingness, decision making, and methods of informed consent with respect to patient and physician participation in POCr. An exploratory study was conducted involving three focus groups, two with VA patients (n=8) and one with physicians (n=6) affiliated with a Midwestern VA Health Care System, to explore attitudes and preferences towards issues in POCr. Emerging themes were captured through qualitative content analysis. Four primary themes emerged from the focus group data: (1) a qualified willingness to participate in POCr; (2) the doctor-patient relationship as a context for POCr; (3) transparency and choice in POCr participation; and (4) protecting patient confidentiality and privacy. Our exploratory study among VA physicians and patients suggests that POCr may be perceived as intervening or undermining the physician-patient relationship in cases where randomization supplants doctor-patient decision making, or where a waiver of informed consent may diminish the need for physician-patient interaction. Informed consent is important in POCr because it offers a way for patients and physicians to establish rapport and trust, particularly in cases where randomization removes the need for clinical decision making.</p

The Use of Routinely Collected Data in Clinical Trial Research

RCTs are the gold standard for assessing the effects of medical interventions, but they also pose many challenges, including the often-high costs in conducting them and a potential lack of generalizability of their findings. The recent increase in the availability of so called routinely collected data (RCD) sources has led to great interest in their application to support RCTs in an effort to increase the efficiency of conducting clinical trials. We define all RCTs augmented by RCD in any form as RCD-RCTs. A major subset of RCD-RCTs are performed at the point of care using electronic health records (EHRs) and are referred to as point-of-care research (POC-R). RCD-RCTs offer several advantages over traditional trials regarding patient recruitment and data collection, and beyond. Using highly standardized EHR and registry data allows to assess patient characteristics for trial eligibility and to examine treatment effects through routinely collected endpoints or by linkage to other data sources like mortality registries. Thus, RCD can be used to augment traditional RCTs by providing a sampling framework for patient recruitment and by directly measuring patient relevant outcomes. The result of these efforts is the generation of real-world evidence (RWE). Nevertheless, the utilization of RCD in clinical research brings novel methodological challenges, and issues related to data quality are frequently discussed, which need to be considered for RCD-RCTs. Some of the limitations surrounding RCD use in RCTs relate to data quality, data availability, ethical and informed consent challenges, and lack of endpoint adjudication which may all lead to uncertainties in the validity of their results. The purpose of this thesis is to help fill the aforementioned research gaps in RCD-RCTs, encompassing tasks such as assessing their current application in clinical research and evaluating the methodological and technical challenges in performing them. Furthermore, it aims to assess the reporting quality of published reports on RCD-RCTs

Evaluating complex interventions using routinely collected data: Methods to improve the validity of randomised controlled trials and observational studies

This thesis addresses the evaluation of complex interventions using routinely collected data, specifically the internal validity of observational studies and the generalisability of Randomised Controlled Trials (RCTs). Following a literature review, this thesis has four main objectives: to estimate the effect of telephone health coaching on hospital utilisation in an observational study; to assess optimal choices of control area in observational studies; to estimate the effect of telehealth within a large RCT; and to develop methods to assess aspects of the generalisability of RCTs empirically. The first paper compares health-coached patients with matched controls. Controls were selected from areas of England that were first matched to the characteristics of the intervention area. Health coaching did not reduce hospital admissions in this study. A second paper uses simulations to assess the relative bias and statistical precision in the treatment effects estimated under alternative approaches to selecting control areas. Lower bias is reported when using local controls than when selecting controls from matched areas, except when there is little unexplained area-level variation in outcomes, when the opposite is true. The third paper reports that, in the RCT, telehealth patients had fewer hospital admissions than controls, but admissions increased unexpectedly among controls after recruitment, leading to concerns about generalisability. Placebo tests find that control patients in the RCT experienced more admissions than matched non-participants receiving usual care. To address the concern that the control group did not receive ‘usual care’, sensitivity analyses are presented that contrast outcomes between the telehealth patients in the RCT and matched non-participants. In this comparison, telehealth is associated with a trend towards more admissions than usual care. The thesis concludes that careful control matching and placebo tests can address important aspects of the validity of observational studies and RCTs, but that further development of evaluation methods is warranted