February 9, 2018: Linking Design to Analysis of Cluster Randomized Trials: Covariate Balancing Strategies

Speaker

Fan Li, PhD Candidate in Biostatistics
Department of Biostatistics and Bioinformatics
Duke University

Topic

Linking Design to Analysis of Cluster Randomized Trials: Covariate Balancing Strategies

Keywords

Pragmatic clinical trial; Cluster randomized trial; CRT; Covariates; Constrained randomization; Study design

Key Points

  • In cluster-randomized trials, intervention occurs at the cluster level (such as clinics or hospitals) and outcomes are measured at the individual level.
  • The goal in a cluster-randomized trial is to leverage design-based control of baseline covariates through stratification, pair matching, and constrained randomization.
  • Constrained randomization allows a researcher to assess balance for different allocation schemes and to randomize only within a constrained space with “balanced” schemes.
  • Two lessons learned in statistical analysis are model-based inference and permutation inference; in both, analysis of trial results should account for design.

Discussion Themes

The “Reminder/Recall Immunization Study” example demonstrates 16 randomized counties (clusters), balanced to ensure that urban and rural counties were equally represented in control and treatment groups.

Constrained randomization is often a preferable technique to balance multiple baseline covariates in small cluster-randomized trials because it avoids categorization of continuous covariates (versus stratification).

Software to perform constrained randomization is available in “Stata” and “R” by the Duke biostatistics group.

 

For information on cluster randomized trials, visit The Living Textbook http://bit.ly/2skjlTW

Tags

@PCTGrandRounds, @FrankFanLi; @DukeMedSchool, #randomizedtrials, #pragmatictrials, #pctGR

February 2, 2018: Early Progress on the All of Us Research Program

Speaker

Joshua C. Denny, MD, MS, FACMI
Professor of Biomedical Informatics and Medicine
Director, Center for Precision Medicine
Vice President for Personalized Medicine
Vanderbilt University Medical Center

Topic

Early Progress on the All of Us Research Program

Keywords

Pragmatic clinical trial; All of Us; Electronic health record

Key Points

  • The Framingham Heart Study was a major influence of All of Us, because it followed a small number of participants, but followed them very closely, and showed a significant impact in lowering cardiovascular disease.
  • Engagement and diversity are core goals of the All of Us Program, and seeing patients as partners is a guiding principle.
  • Participants can enroll in the protocol through either the traditional route of health care provider organizations, or as direct volunteers.
  • There will be centralized electronic health (EHR) data broken into three tiers: Public, registered, and controlled, with any obvious identifiers removed from all.
  • A major goal is to give All of Us participants access to information, including study updates and aggregated results.

Discussion Themes

The All of Us Program does not intend to replicate the U.S. population, but rather to focus on under-represented populations.

Researchers can apply for data from the All of Us Biobank, a repository that stores and manages biological samples, by posting their research questions. There has been some pushback from researchers on publicly sharing their questions, but it seems to promote efficiency and collaboration.

Patient portals and smartphone apps are two vehicles that the All of Us Program will use to deliver information and results back to participants.

 

For information on All of Us, visit http://bit.ly/2tMgH6b

Tags

@PCTGrandRounds, @jdnashville, @VUMChealth, @AllofUsResearch, #pctGR

January 26, 2018: The Lumbar Imaging with Reporting of Epidemiology (LIRE) Trial: Subsequent Cross-Sectional Imaging Through 90 Days—Preliminary Results

Speakers

Jeffrey (Jerry) G. Jarvik MD MPH
Professor, Radiology, Neurological Surgery and Health Services
Adjunct Professor, Pharmacy and Orthopedics & Sports Medicine
Co-Director, Comparative Effectiveness, Cost and Outcomes Research Center
Director, UW CLEAR Center for Musculoskeletal Disorders
University of Washington

Patrick J. Heagerty PhD
Gilbert S. Omenn Endowed Chair in Biostatistics
Professor and Chair, Department of Biostatistics
University of Washington

Topic

The Lumbar Imaging with Reporting of Epidemiology (LIRE) Trial: Subsequent Cross-Sectional Imaging Through 90 Days—Preliminary Results

Keywords

Pragmatic clinical trial; LIRE; Lumbar imaging; Spinal imaging; Low back pain; Benchmark data

Key Points

  • The Lumbar Imaging with Reporting of Epidemiology (LIRE) study hypothesis was that adding prevalence benchmark data to spinal imaging would reduce future injections, surgery, and opioid prescriptions.
  • A recent study by Fried et al. in Radiology showed that inclusion of benchmark data in lumbar reports is associated with decreased utilization of high-cost low back pain management.
  • The study has enrolled over 240,000 patients at 4 sites, with the majority of patients older than 40 and receiving imaging through standard x-rays or MRIs.
  • Researchers have analyzed data from two of the four sites so far, and results showed minor reduction in follow-up care for intervention group versus control group. Due to the large, complex data set, they will need more time to review and look at fixed effects.

Discussion Themes

The LIRE study used a stepped-wedge design with five waves, where the randomization was broken down so that all five waves would receive the intervention by the end of the accrual period.

Researchers have consolidated duplicate records within the data set during their analysis by cross-referencing different data pulls over time.

This study could be considered “research to see the details of delivery,” in that researchers learned a lot about patterns of clinician x-ray/MRI ordering behavior, and this helped them to determine definitions of what constitutes an index or an outcome.

 

For information on the LIRE study, visit The Living Textbook: http://bit.ly/2nftls1

Tags

@PCTGrandRounds, @UWMedicine, @kpwashington, @MayoClinic, @KPShare, @HenryFordNews, #lowbackpain, #pctGR

January 19, 2018: The Healthcare Pivot: Technology and Transformation of Healthcare

Speaker

Kevin A. Schulman, MD
Professor of Medicine
Associate Director, Duke Clinical Research Institute
Visiting Scholar, Harvard Business School

Topic

The Healthcare Pivot: Technology and Transformation of Healthcare

Keywords

Pragmatic clinical trial; mHealth; Electronic health data; Data mining; Machine Learning; Health IT

Key Points

  • Health IT implementation is affected by multiple factors including data analytics, business workflow and process improvement, and patient engagement.
  • Mobile health (mHealth) can deliver actionable data to clinicians, with detailed reporting, peer comparisons, and provider “report cards.”
  • The mPower initiative aims to enable patients to take ownership over their healthcare, including storing and accessing their electronic health records via their mobile phones.
  • The HeartStrong trial demonstrated the positive effect of electronic reminders and online social support on medication adherence and overall patient outcomes.
  • What if 50% of healthcare was delivered via mHealth by 2025?

Discussion Themes

A family of four with a median income of $75,000 will pay over $18,000 in healthcare expenses per year, so it is critical to build value and efficiency within the system.

Informatics is central to the top five fastest growing companies in the U.S., so leveraging health IT will help with scalability of the digitalization of healthcare data.

Who will drive innovation in healthcare in the future?  Academic medical centers?  Insurance companies? It will depend on the leadership of a group deciding that innovation will solve large-scale societal healthcare issues.

 

For information on the role of mHealth in healthcare systems, visit http://bit.ly/2BfchqX

Tags

@PCTGrandRounds, @Collaboratory1, @DCRINews, #Healthcare, #mHealth, #EHR, #drugadherence, #machinelearning, #pctGR

January 12, 2018: Behavioral Economic Principles to Understand and Change Clinician Behavior

Speaker

Jeffrey A. Linder, MD, MPH, FACP
Chief, Division of General Internal Medicine and Geriatrics
Michael A. Gertz Professor of Medicine
Northwestern University Feinberg School of Medicine

Topic

Behavioral Economic Principles to Understand and Change Clinician Behavior

Keywords

Pragmatic clinical trial; Behavioral economics; Antibiotic resistance; BEARI; Antibiotic stewardship

Key Points

  • As many as 30% of antibiotic prescriptions are unnecessary. Can behavioral economics explain and help guide how to change this clinician prescribing behavior?
  • Habit, pressure from patient, and a “just to be safe” mentality are the most common factors driving inappropriate antibiotic prescribing, but antibiotic stewardship is critical to improving patient outcomes.
  • The Behavioral Economics/Acute Respiratory Infection (BEARI) trial looked at three behavioral interventions to reduce inappropriate antibiotic prescribing for acute respiratory infections: suggested alternatives to antibiotics, accountable justification, and peer comparison.
  • Because doctors are people who are affected by emotion and social interaction, peer comparison had greatest effect on prescribing behavior, even after the intervention period ended.

Discussion Themes

Overall, clinicians in the BEARI trial expressed desire to follow guidelines for good antibiotic stewardship, but some of the responses in the intervention group indicated a misunderstanding of the guidelines.

Is some of the impact of interventions due to the Hawthorne effect, in that these clinicians knew they were being  enrolled in a trial, and thereby aware they are being watched and having their work reviewed?

Older doctors have been shown to inappropriately prescribe at a higher rate than younger doctors, but the question remains whether this is based on generational learning or based on decision fatigue over time.

There have been significant efforts by the Centers for Disease Control and Prevention and other public health groups to spread awareness in recent years about the antibiotic resistance and the importance of good antibiotic stewardship, so it is possible outside factors also impacted clinician behavior modification.

Tags

@PCTGrandRounds, @Collaboratory1, @NUFeinbergMed, #BehavioralEconomics, #AntibioticResistance, #AntibioticStewardship, #pctGR

January 5, 2018: IMPACT-AFib: An 80,000 Person Randomized Trial Using the Sentinel Initiative Platform

Speakers

Noelle M. Cocoros, DSc, MPH
Epidemiologist, Department of Population Medicine
Harvard Medical School and Harvard Pilgrim Health Care Institute

Christopher B. Granger, MD, FACC, FAHA
Professor of Medicine, Duke University
Director, Cardiac Care Unit
Duke University Medical Center

Richard Platt, MD, MS
Professor and Chair, Department of Population Medicine
Harvard Medical School and Harvard Pilgrim Health Care Institute

Sean Pokorney, MD
Assistant Professor of Medicine, Duke University

Topic

IMPACT-AFib: An 80,000 Person Randomized Trial Using the Sentinel Initiative Platform

Keywords

Pragmatic clinical trial; Sentinel Initiative; IMPACT-AFib; Atrial fibrillation; Data sharing

Key Points

  • The Sentinel Initiative uses the Common Data Model to curate and distribute large amounts of electronic health record (EHR) data from a diverse group of data partners.
  • IMPACT-AFib (an atrial fibrillation trial) used Sentinel registry data to find eligible patients with at least one oral anticoagulation prescription fill.
  • The trial looked at usual care with delayed provider anticoagulation intervention versus early patient and provider anticoagulation intervention, using access to pharmacy records.
  • Is there an ethical question raised by delaying intervention in the usual care group?

Discussion Themes

Oral anticoagulant (OAC) underuse is a public health priority and also a priority of health plans, which made health plan stakeholders very engaged in the IMPACT A-Fib trial.

IMPACT-AFib found efficiencies with a single IRB that facilitated streamlined processes across multiple institutions.

Research weighed practical considerations against ethical concerns, in that by consenting individuals for the trial, researchers would be performing a type of intervention and thereby negating the comparison between true control and intervention groups.

FDA sponsored the IMPACT-AFib trial to demonstrate feasibility but researchers hope that other sponsors will be open to trials leveraging Sentinel in the next year or so.

For More Information

For information on data sharing solutions, visit the Living Textbook http://bit.ly/2m24zMc
Tags

@PCTGrandRounds, @Collaboratory1, #SentinelInitiative, #EHR, #PatientBrochure, #PatientBrochure, #pctGR

December 15, 2017: Does Machine Learning Have a Place in a Learning Health System?

Speakers

Michael Pencina, PhD
Professor of Biostatistics and Bioinformatics, Duke University
Director of Biostatistics
Duke Clinical Research Institute

Topic

Does Machine Learning Have a Place in a Learning Health System?

Keywords

Machine Learning; Artificial Intelligence; AI; Learning Health Systems

Key Points

  • Machine learning has many different applications for generating evidence in meaningful ways in a learning health system (LHS).
  • Although other industries are using machine learning, the health care industry has been slow to adopt artificial intelligence (AI) methodologies.
  • The Forge Center was formed under the leadership of Dr. Robert Califf and uses team science—biostatisticians, engineers, computer scientists, informaticists, clinicians, and patients collaborate to develop machine learning solutions and prototypes to improve health.
  • In a learning health system, the process is to identify the problem, formulate steps to solve it, find the right data and perform analysis, test the proposed solution (by embedding randomized experiments in a LHS), and implement or modify the solution.
  • Machine learning is a small piece of a LHS, but an important one, and methods are characterized by the use of complex mathematical algorithms trained and optimized on large amounts of data.

Discussion Themes

Demonstrating enhanced value of machine learning over existing algorithms will be an important next step. An ongoing question is how do models get translated into clinical decision making? Machine learning is a tool to develop a model, but implementation of the findings will require team science.

Prediction models can be calibrated to work across health systems to an extent, but there are many unique features of individual health systems, so large health systems should use their own data to optimize the information and learning in a specific setting.  

There are key issues related to accurate ascertainment of data, especially with relation to completeness. For example, inpatient data collected during a hospital stay are likely to yield models that have value. If data rely on events that happen outside the system, it can be harder to get the complete picture.

For More Information

For information on #MachineLearning in a #LearningHealthSystem visit http://bit.ly/2D5ATEG

Tags

@PCTGrandRounds, @Collaboratory1, @DukeForge, @Califf001, #MachineLearning, #LearingHealthSystem, #pctGR

December 8, 2017: Data and Safety Monitoring in Pragmatic Clinical Trials

Speakers

Greg Simon, MD, MPH
Senior Investigator
Kaiser Permanente Washington Health Research Institute

Jeremy Sugarman, MD, MPH, MA
Harvey M. Meyerhoff Professor of Bioethics and Medicine
Johns Hopkins Berman Institute of Bioethics

Susan S. Ellenberg, PhD
Professor of Biostatistics
Professor of Medical Ethics and Health Policy
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania

Topic

Data and Safety Monitoring in Pragmatic Clinical Trials

Keywords

Pragmatic clinical trial; Safety monitoring; Data monitoring committee; Data and safety monitoring board; DSMB; Patient privacy; Research ethics

Key Points

  • An external approach to monitoring can yield usable results, guard trial integrity, and also ensure patients aren’t exposed to undue risk.
  • A Data Safety and Monitoring Board (DSMB) panel of expertise is not set in stone, so an ethicist, a patient advocate, or a sponsor representative could make valuable additions.
  • There are special issues needing monitored in pragmatic trials including protocol adherence, eligibility, and subjective study outcomes.
  • Privacy concerns may prevent merging data from multiple electronic health record systems at one central site; therefore, quality control across sites is crucial to assure analyses are conducted identically.

Discussion Themes

Emerging experiences and data can pose ethical quandaries for investigators in meeting their obligations to minimize risk to participants, which is why monitoring is so crucial.

Since pragmatic trials will typically be addressing questions intended to impact health practices, an expert oversight group will be important for most PCTs.

A DSMB typically considers monitoring study quality as one of its mandates, and may be uncomfortable making recommendations based on observed treatment effects without a sense of how effectively interventions are being administered.

For More Information

For information on Data & Safety Monitoring for #pragmatictrials, visit the Living Textbook http://bit.ly/2Bv2RZR #pctGR

Tags

@PCTGrandRounds, @Collaboratory1, @GregSimonKPWHRI, @KPWaResearch, @JohnsHopkinsSPH, @UPenn_MedEthics, #DataSafety, #PatientAdvocate, #Pragmatictrials, #EHR, #Qualitycontrol, #AdverseEvents #pctGR

December 1, 2017: Providing a Shared Repository of Detailed Clinical Models for All of Health and Healthcare

Speakers

Stanley M. Huff, MD
Chief Medical Informatics Officer
Intermountain Healthcare and Professor (Clinical) of Biomedical Informatics
University of Utah

W. Ed Hammond, PhD
Duke Center for Health Informatics
Clinical & Translational Science Institute
Duke University

Topic

Providing a Shared Repository of Detailed Clinical Models for All of Health and Healthcare

Keywords

Pragmatic clinical trial; Clinical research; Clinical Information Interoperability Council; Repository; Learning Health System; Patient care; Data collection; Data dissemination

Key Points

  • The Clinical Information Interoperability Council (CIIC) was created to address the lack of standardized data definitions and to increase the ability to share data for improved patient care and research.
  • Accurate computable data should be the foundation of a Learning Health System (LHS), which will lead to better patient care through executable clinical decision-support modules.
  • The ultimate goal of the CIIC is to create ubiquitous sharing of data across medicine including patient care, clinical research, device data, and billing and administration.
  • The three most important questions for the CIIC are what data to collect, how the data should be modeled, and what are computable definitions of the data?

Discussion Themes

All stakeholders need to agree to work together and to allow practicing front-line clinicians to direct the work.

Stakeholders should use and share common tools to create models, and share the models through an open internet accessible repository.

The goal of the repository is to have a common representation digitally for what happened in the real world, by creating agreed-upon names and definitions for a common data set.

What level of vetting is appropriate for data definitions? This should not be a popularity contest for data, but rather a decision made by expert judges.

For More Information

For information on dissemination approaches for different healthcare stakeholders, visit the Living Textbook http://bit.ly/2kcSqGb

Tags

@PCTGrandRounds, @Collaboratory1, @UUtah, @DukeHealth, #Healthcare, #ClinicalDecision Support, #LearningHealthSystem, #ClinicalResearch, #PatientCare, #pctGR

November 17, 2017: ICD-Pieces: From Planning to Performance

Speakers

Miguel A. Vazquez, MD
Professor of Medicine
Clinical Chief Nephrology Division
University of Texas Southwestern Medical Center

George Oliver, MD
Vice President Clinical Informatics
Parkland Center for Clinical Innovation

Topic

ICD-Pieces: From Planning to Performance

Keywords

Pragmatic clinical trial; Multiple chronic conditions; Diabetes; Hypertension; Chronic kidney disease; Pieces™; Parkland Center for Clinical Innovation; PCCI; University of Texas Southwestern Medical Center; Electronic health records

Key Points

  • ICD-Pieces is a pragmatic clinical trial conducted in 4 large, diverse healthcare systems, including a Veterans Affairs (VA) system.
  • This study is evaluating a collaborative care model for improving the management of 3 chronic conditions: diabetes, hypertension, and chronic kidney disease. This model of care combines a novel IT platform, primary care practitioners, and site practice facilitators.
  • The study is conducted with the aid of clinical decision support tools, previsit planning, performance monitoring, and outcomes ascertainment through EHR data and claims data.

Discussion Themes

It is essential to engage key stakeholders early and throughout the pragmatic trial to sustain enthusiasm for the trial’s outcomes. In ICD-Pieces, it was especially important to engage with individual healthcare practitioners to facilitate and simplify their workflow. The study team also collaborated with medical directors and other leaders at the participating healthcare systems.

Practice facilitators at each study site served as a link between the study team and the healthcare system operations.

As with many real-world pragmatic trials, ICD-Pieces needed to address some turnover of key study participants, including principal investigators, practice facilitators, and IT staff.

For More Information

Read more about the ICD-Pieces trial in the Living Textbook: http://bit.ly/2j1qkJu

Dr. Vazquez discusses lessons learned in ICD-Pieces: http://bit.ly/2zx8tEY

Visit the ICD-Pieces Demonstration Project page: http://bit.ly/2zQE1my

For more on dissemination, implementation & sustainability for ICD-Pieces: http://bit.ly/2A6zL5A

Tags

#pctGR, #chronicdisease, @PCTGrandRounds, @Collaboratory1, @PCORnetwork, @UWMedicine, @PCCIpieces