Although maintenance hemodialysis has long been a staple of care for patients with end-stage renal disease, there are limited data from clinical trials to inform optimal approaches, including the optimal duration of hemodialysis sessions. The TiME trial investigators, in partnership with 2 large dialysis provider organizations, evaluated the effects of a longer hemodialysis session duration on mortality and hospitalization rate among more than 7000 patients receiving care in 266 dialysis facilities.
The TiME trial was discontinued early (median follow-up, 1.1 years) because there was an insufficient difference in mean hemodialysis session duration between the intervention group and the usual care group. The investigators observed no reduction in mortality or hospitalization rate in either group.
Despite ending early, the trial met important objectives for informing the implementation of large pragmatic clinical trials embedded in health care systems. In a large multicenter study with no onsite research personnel, the investigators quickly and efficiently enrolled a large number of participants using an opt-out consent approach. The study data were obtained entirely from the electronic health and administrative records of the partnering dialysis provider organizations and were generated from routine clinical care delivery.
“The TiME trial provides an important foundation for future pragmatic trials in dialysis as well as in other settings,” said Dr. Laura M. Dember of the University of Pennsylvania Perelman School of Medicine, the principal investigator of the TiME trial.
The TiME trial was supported within the NIH Collaboratory by a cooperative agreement from the National Institute of Diabetes and Digestive and Kidney Diseases and received logistical and technical support from the NIH Collaboratory Coordinating Center. Download a study snapshot about the TiME trial, and learn more about the NIH Collaboratory Demonstration Projects.
Michelle B. Leavy, MPH
Head, Healthcare Research and Policy
Elise Berliner, PhD
Director, Technology Assessment Program
Center for Evidence and Practice Improvement (CEPI)
Agency for Healthcare Research and Quality (AHRQ)
Development of Harmonized Outcome Measures for Use in Research and Clinical Practice
Health outcomes; Patient-centered outcomes; Agency for Healthcare Research and Quality; Patient registries; Clinical data; Patient-reported outcomes; Value-based care; Electronic health records; Learning health system; Conceptual framework
The goal of the Outcome Measures Framework is to create a common conceptual model for classifying the range of outcomes that are relevant to patients and providers across most conditions.
Harmonization of outcome measures is essential to comparing and aggregating results between and among registries, clinical research, and quality reporting, and to facilitating performance and value-based measurement.
A minimum measure set is the minimum set of harmonized measures that can be captured consistently in research and clinical practice.
Developing the framework used a stakeholder-driven process that categorized outcomes as clinical responses, patient-reported, survival, resource utilization, and events of interest for a sample set of 5 clinical areas.
The benefits of developing a core set of measures include reduced clinician burden and improved patient care.
How is this work informing the HL7 work group that is defining standards for registries?
Next steps include implementation of the minimum measure sets in EHRs, registries, and other research efforts; demonstrating the value of a minimum measure set; and encouraging adoption of the measures.
Miguel A. Vazquez, MD
Professor of Medicine
Clinical Chief Nephrology Division
UT Southwestern Medical Center
George (Holt) Oliver, MD, PhD
Vice President Clinical Informatics
Parkland Center for Clinical Innovation
ICD-Pieces: Lessons Learned in an Ongoing Trial
Multiple chronic conditions; Chronic kidney disease; Hypertension; Diabetes; Pieces™; Electronic health record; Parkland Center for Clinical Innovation; Primary care; U.S. Department of Veterans Affairs
Improving Chronic Disease Management with Pieces™ (ICD-Pieces) is an NIH Collaboratory Demonstration Project that is implementing a novel technology platform (Pieces) to enable the use of electronic health record data in the management of chronic kidney disease, diabetes, and hypertension within primary care practices.
The aim of the study is to reduce hospitalizations, emergency department visits, readmissions, and cardiovascular events and deaths for patients with multiple chronic conditions.
ICD-Pieces is employing centralized clinical decision support across 4 large, diverse healthcare systems in addition to the use of Practice Facilitators within primary care.
In embedded pragmatic clinical trials conducted in real-world settings, it is important to anticipate changes over the course of the study, which could involve changes at every level, from staff turnover to changes in national policies or standards.
As one of the largest healthcare providers in the world for patients with chronic kidney disease, the VA has been an effective healthcare system partner in the ICD-Pieces trial.
When partnering with healthcare systems, it is important to align goals and plan together, minimize disruption, anticipate and adapt to changes, and create a sustainable foundation for future studies.
Using death as an endpoint in pragmatic clinical trials is challenging because there are no standardized processes for ascertaining patient deaths in the United States. If a patient dies outside of a clinical care system, ascertaining if and how a death has occurred is considerably complicated. There are multiple sources of vital statistics data, each with different amounts of lag time, linking approaches, costs, and specificity of information. For example, some sources include cause of death while others include only fact of death; some have a lag time of a few months and some may take over a year; some charge by the individual file and some have an annual subscription fee.
“Death identification and adjudication may be more complicated with pragmatic clinical trials (PCTs) that rely on data collected from the patient’s electronic health record (EHR), medical claims, self-report, or medical devices.” —Eisenstein E, et al. Choosing and Specifying Endpoints and Outcomes: Using Death as an Endpoint. In: Rethinking Clinical Trials: A Living Textbook of Pragmatic Clinical Trials.
The sources of data described in this section include the Death Master File, the Medicare Master Beneficiary Summary File, state vital statistics, the Fact of Death File, the National Death Index, and call centers. The section also presents a case study to illustrate a hybrid death identification and verification approach used in the ToRsemide compArisoN with furoSemide FOR Management of Heart Failure (TRANSFORM-HF) PCT (ClinicalTrials.gov Identifier: NCT03296813).
Keith Marsolo, PhD
Department of Population Health Sciences
Duke Clinical Research Institute
Duke University School of Medicine
Approaches to Patient Follow-Up for Clinical Trials: What’s the Right Choice for Your Study?
Pragmatic clinical trial; Real-world data; Distributed research network; Electronic health records; EHR; Health data sources; Data standardization; Common data model; Fast Healthcare Interoperability Resources (FHIR); Application programming interface (API)
Different sites have different capabilities and levels of sophistication around data. Clinical trial investigators should think from the beginning about the questions they want to answer and how much data is needed.
From different sources, such as the EHR, claims, or participant, data can be procured and provided in different ways, either by the patient, staff or clinician, or through IT and data experts.
PCTs with many sites may require a “patchwork quilt” of approaches for patient follow-up depending on the needs of the trial. Clinician-generated reports, direct from patients, and solutions involving application programming interfaces (APIs) are all good options for data exchange.
How do we think through the options for getting patient data where some sites may not be in the distributed research network or use a common data model?
Fast Healthcare Interoperability Resources (FHIR) is a draft standard describing data formats and elements and an application programming interface (API) for exchanging electronic health records. The FHIR interface requests data as an object, and for each defined domain it specifies allowable values and variables and predefines the information that you get out of the system.
Until data are collected/generated using the same standards/formats as the API, there will still be a need to understand the EHR-to-interface mapping.
For more information on using health data in embedded pragmatic clinical trials, visit the NIH Collaboratory’s EHR Core webpage.
Members of the NIH Collaboratory’s Biostatistics and Study Design Core contributed 3 new sections to the Living Textbook exploring issues in the design and analysis of pragmatic clinical trials. The new sections offer insights into emerging issues in embedded pragmatic clinical trials and lessons learned from the NIH Collaboratory’s first round of Demonstration Projects.
The Designing to Avoid Identification Bias section addresses a type of selection bias that can occur in pragmatic clinical trials that use information from electronic health records to determine study population eligibility and in which the study intervention influences who undergoes screening or receives a diagnosis in clinical care.
The Alternative Cluster Randomized Designs section describes alternative design choices for cluster randomized trials and their implications for statistical power and sample size calculations. Modified cluster randomized designs, such as cluster randomization with crossover, may reduce the sample size required for a pragmatic clinical trial and may be particularly feasible in trials embedded in healthcare systems with electronic health records.
In addition to contributing content to the Living Textbook, the Biostatistics and Study Design Core works with the NIH Collaboratory Demonstration Projects to address challenges in their statistical plans and study designs during the planning phase and to develop guidance and technical documents related to study design and biostatistical issues relevant to pragmatic clinical trials.
Robert J. Mentz, MD
Associate Professor of Medicine
Duke University School of Medicine
Kevin J. Anstrom, PhD
Professor of Biostatistics and Bioinformatics
Director of Biostatistics, Duke Clinical Research Institute
Duke University School of Medicine
Eric Eisenstein, DBA
Associate Professor in Medicine
Duke University School of Medicine
Stephen J. Greene, MD
Fellow, Division of Cardiology and Duke Clinical Research Institute
Duke University School of Medicine
Eric J. Velazquez. MD, FACP, FACC, FASE, FAHA
Robert W. Berliner Professor of Medicine, Yale University
Chief, Cardiovascular Medicine, Yale New Haven Hospital
Physician-in-Chief, Heart and Vascular Center, Yale New Haven Health
TRANSFORMing Research for Patients With Heart Failure
Pragmatic clinical trial; Heart failure; PRECIS-2; Hospitalization; TRANSFORM-HF; Clinical equipoise; Electronic health records; National Heart, Lung, and Blood Institute (NHLBI)
The traditional approach to conducting clinical trials is unsustainable in many respects, including operational complexities, low enrollment rates, high costs, and failure to leverage existing resources. Incorporating pragmatic elements in the design of trials may improve efficiencies and conduct.
TRANSFORM-HF is a pragmatic trial evaluating torsemide versus furosemide treatment for long-term clinical outcomes among patients hospitalized for heart failure. Study randomization is 1:1, and the primary endpoint is all-cause mortality.
Advantages of trials with pragmatic designs include real-world effectiveness; broad patient/provider groups; reduced number and complexity of visits; streamlined data collection; potential for faster results; and results that will be more generalizable.
The clinical question involving starting a treatment (Should we start with furosemide or torsemide?) versus switching a treatment (Should we attempt to switch patients from furosemide to torsemide?) would seem to lead to different study designs.
While the peer review process for funding TRANSFORM-HF was challenging and required modifying the approach, it ultimately led to a better design.
Read more about PRECIS-2 domains along the explanatory-pragmatic continuum of a clinical trial in the Living Textbook.
Niteesh K. Choudhry, MD, PhD
Professor, Harvard Medical School
Executive Director, Center for Healthcare Delivery Sciences, Brigham and Women’s Hospital
Cluster Randomized Trials in Health Care Delivery Systems: Lessons from STIC2IT
STIC2IT; Pragmatic clinical trial; Learning health system; Cluster randomization; Medication adherence; Telepharmacy; Electronic health record; Stakeholder engagement
STIC2IT, a pragmatic, cluster-randomized trial, evaluated a telepharmacy intervention to improve medication adherence for people with chronic diseases.
Pragmatic aspects of STIC2IT included outcomes assessed using routinely collected data, cluster randomization by physician practice, intention-to-treat analysis, and use of the EHR to collect research data.
While medication adherence did improve in the STIC2IT intervention group, secondary clinical outcomes did not improve. Future trials within health systems should incorporate multilevel engagement across the health system, physicians and staff, and patients.
It is important to do ongoing outreach at the health system leadership level to ensure understanding and commitment to the study and keep providers aware of the trial. Study teams should be mindful of the priorities of their partner health system.
Using the EHR for research data required some upfront work building special modules and generating custom reports.
The EMBED pragmatic trial is evaluating a clinical decision support tool designed to automatically identify and facilitate management of eligible patients with opioid use disorder in the emergency department (ED).
From July 2016 to Sep 2017, there was a 30% increase in visits to the ED for opioid overdose (Morbidity and Mortality Weekly Report, March 9, 2018).
With medication-assisted treatment, patients are 2 times more likely to be engaged in addiction treatment at 30 days.
EMBED’s user-centered design aims to streamline workflows, address barriers to adoption, embed ED-initiated buprenorphine into routine ED care, and optimize adoption, dissemination, implementation, and scalability.
Poor usability of health information technology (HIT) is major source of frustration for clinicians. Electronic health record usability is a fundamental barrier to implementation of evidence-based medicine.
The science of usability in healthcare is still in the early stages. The EMBED study wants to improve the HIT experience.
How much does the study rely on EHR data for outcomes, and how detailed are the pilot outcomes data requested from each system? How do you plan to verify the accuracy of those data?