Speakers

Ken Mahaffey, MD
Professor of Cardiovascular Medicine
Associate Dean for Clinical Research, School of Medicine
Vice Chair for Clinical Research, Department of Medicine
Director, Stanford Center for Clinical Research (SCCR)
Stanford University

Nishant Shah, MD
Assistant Professor of Medicine
Duke University School of Medicine
The REVEAL Project: Identifying Real World Gaps and Areas for Improvement

Neha Pagidipati, MD, MPH 
Associate Professor of Medicine
Duke University School of Medicine
Test to Treat – Improving lipid management on a health system level

Slides

Keywords

CardioHealth Alliance, Research, ASCVD, LDL-C, REVEAL, Test 2 Treat

Key Points

• The CardioHealth Alliance is focused on establishing a health system alliance with engaged clinicians, data scientists, and healthcare leaders to develop new care-pathways and real-world data to improve cardiovascular, renal disease, and metabolic disease outcomes for patients.

• The CardioHealth Alliance wanted to do this work to establish a reusable real-world data platform to rapidly answer clinical questions, shorten implementation of evidence into practice, generate real-world evidence to inform stakeholders, and establish an alliance to address the value of care through policy.

• Since its inception in November 2020, 9 health systems and 6 industry partners have joined the Alliance. The Alliance has 4 pillars that include partnering with clinical scientists to use real-world data to inform real world care; scaling and optimizing best practices; developing and testing new pathways and practices; continuously addressing the value of care through effective policy.

• The CardioHealth Alliance REVEAL study looked at the current landscape for atherosclerotic cardiovascular disease (ASCVD) management because even though the guidelines are clear, implementation of LDL-C management across the world has not been optimal.

• REVEAL looked at two cohorts. One cohort looked at LDL-C testing and management patterns. The study wanted to understand, within a chronic ASCVD population, how many ASCVD patients had LDL-C guideline-recommended goals, what factors were associated with achieving LDL-C goals in patients with ASCVD, and what patterns of liped lowering therapy (LLTs) prescriptions before and after LDL-C test by LDL-C level.

• REVEAL also wanted to assess Lipoprotein (a) (Lp(a)), a biomarker that can lead to early onset and aggressive ASCVD. REVEAL wanted to find out what the proportion of patients with ASCVD had a LP (a) test, what factors associated with undergoing Lp(a) testing in patients with ASCVD, and whether or not Lp(a) testing influenced lipid management.

• REVEAL identified a patient population across the Alliance that had an LDL-C value during the study period, had at least 2 outpatient encounters, and an ASCVD event within the last 5 years. The study identified 216,074 patients with ASCVD across 5 health systems. REVEAL found that only 86,188 (40%) had guideline-recommended LDL-C goals.

• Patients who were at their goal tended to be male, white, had established coronary disease, heart failure, diabetes, and atrial fibrillation. Several factors were independently associated with not achieving the target: female sex, Black race, and Hispanic ethnicity.

• For the Lp(a) cohort, REVEAL identified about 595,000 patients who were active within the health systems and either had or did not have an Lp(a) test. The study found that only 2,588 (0.4%) were tested for Lp(a) and those who were older, Black, or Hispanic were less likely to have Lp(a) testing. Those with a family history of hypercholesterolemia, ischemic stroke/TIA, PAD, prior LLT, or LDL-C greater than 130mg/dL were more likely to be tested for Lp(a). Having elevated Lp(a) was associated with higher initiation of non-statin lipid-lowering therapy (LLT); however, overall initiation of any LLT after an elevated Lp(a) test was low.

• The CardioHealth Alliance study Test 2 Treat is an implementation science project aimed at improving LDL-C management after an ASCVD event by improving coordination of care between inpatient and outpatient teams, with the goal of preventing downstream morbidity and mortality in this high-risk population.

• There is a window of opportunity when a patient is admitted for a coronary event to review medications and understand where there are gaps and risk factors, but this is not happening. Less than half of patients get their LDL-C checked within 6 months of the MI. The goal of Test 2 Treat is to bridge the transition from inpatient to outpatient care.

• Test 2 Treat has two programs one focused on the inpatient, hospital-level intervention, the other focused on patients as they transition to the outpatient setting.

• For the inpatient program, a hospital level intervention to address barriers and measure the proportion of patients who get their LDL-C measured and their LLTs adjusted in the hospital.

• For the outpatient program, Test 2 Treat is looking at whether or not a nurse champion can navigate the transition verses usual care. The nurse champion or medical assistant will remotely go along with the patient to navigate inpatient-to-outpatient transition, including addressing questions about LDL-C medications, goals, and access to medications, ensuring follow-up, and facilitating communication with the outpatient team.

• Test 2 Treat will develop intervention components that will be publicly available after the study if it is effective. Test 2 Treat has identified patient stakeholders to make sure the study is making sense from a patient perspective.

Learn more

Visit the CardioHealth Alliance website

Discussion Themes

-What do you see as the major challenges that the Alliance can solve? I think what we have learned through the initial work in the Alliance and other programs is that we have an incredible amount of data. We have learned we can access an incredible amount of data and touch an incredible amount of patients. But the data are messy. We need to continue our efforts in data mapping to curate the data more rigorously to allow us to answer more questions.

–What kind of topics or questions will the Alliance take on in the future? We are starting with cardio/kidney/metabolic and the principles of implementation should be applicable more broadly. I anticipate moving beyond CKMI and anticipate moving outwards beyond the traditional patients that we always reach and moving toward patients that are not traditionally included in routine care or clinical research. We want to move toward equitable care. We also want to leverage the idea of the platforms to address patients in a comprehensive manner, across interventions and diseases, to get at all of the issues but in a way that does not cost too much or is not feasible for health systems.

 

Tags

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Speaker

Gregory Tasian, MD, MSc, MSCE
Associate Professor of Surgery and Epidemiology
Children’s Hospital of Philadelphia

Slides

Keywords

PKIDS, PCORnet, Comparative Effectiveness Research, Kidney, Kidney Stone, Pediatrics

Key Points

• The demographic of people who get kidney stones has changed over the past 30 years, which has left gaps in evidence base to support care. Increasingly, kidney stones affect children and adolescents, particularly adolescent girls. The Pediatric KIDney Stone Care Improvement Network (PKIDS) was founded to address this evidence gap.

• PKIDS study was a prospective observational cohort study across 30 sites nationwide answering the participant-identified research question, what is the best surgery for my child who has a kidney or ureteral stone? Surgery for kidney stones usually includes minimally invasive surgical procedures including ureteroscopy (URS), shockwave lithotripsy (SWL), and percutaneous nephrolithotomy (PCNL).

• The participant population included males and females, 8-21 years of age, undergoing URS, SWL, or PCNL for the removal of at least one kidney and/or ureteral stone.

• The primary outcome was a clinical outcome of stone clearance defined by the absence of any stone under 4mm measured by ultrasound 4-8 weeks after surgery, consistent with standard of care. The secondary outcomes were the physical, social, and emotional health measured by patient-reported outcomes, as well as urinary symptoms, missed school/work, and ED visits, repeat surgery, admissions.

• The trial took place between April 2020-October 2023. There were a total of 1,228 participants enrolled, with 1,070 receiving URS, 197 receiving SWL, and 98 receiving PCNL. There was a 50% completion of ultrasounds within the specified window due to the COVID-19 pandemic; the trial extended the window to 16 weeks with a 75% completion of postoperative ultrasound.
• The PKIDS trial supports revisions of the American Urological Association guidelines including: A new size threshold of 15mm for kidney stones; recommend SWL over URS for stones less than 10mm; consider possible greater stone clearance and worse patient experiences with URS for kidney stones 10-15mm; remove SWL from guidelines for stones greater than 15mm; recommend PCNL over URS for stone greater than 15mm.

Speakers

Lauren Milner, PhD
Office of Clinical Policy
U.S. Food and Drug Administration (FDA)

Jonathan Casey, M.D., MSCI
Assistant Professor, Division of Pulmonary and Critical Care Medicine
Vanderbilt University Medical Center
Director, Coordinating Center Pragmatic Critical Care Research Group

Matthew Semler, M.D., MSCI
Associate Professor, Division of Pulmonary and Critical Care Medicine
Vanderbilt University Medical Center
Director, Steering Committee Pragmatic Critical Care
Research Group
Co-Director, Vanderbilt Center for Learning Healthcare

Slides

Keywords

Food and Drug Administration, FDA, Waiver of Consent, Alteration of Informed Consent, IRB, Ethics, Regulatory

Key Points

• Section 3024 of the FDA 21^st Century Cures Act (2016) amends the Food, Drug and Cosmetics Act and provides the U.S. Food and Drug Administration (FDA) with the authority to permit an exception from informed consent requirements when the proposed clinical testing poses no more than minimal risk to the human subject and includes appropriate safeguards to protect the rights, safety, and welfare of participants.

• In 2018, FDA issued the Proposed Rule that would allow an IRB to waive or alter certain informed consent elements, or to waive the requirement to obtain informed consent, under limited conditions, for certain minimal risk clinical investigators.

• The Final Rule (2023) finalizes 4 criteria for waiver or alteration of consent with minor edits and adopts a 5^th criterion for waiver or alteration of consent for identifiable information and biospecimens.

• The Final Rule is not new for IRBs and investigators familiar with the minimal risk waiver/alteration provision in the Common Rule. It has the same criteria, same process. It is relatively new to FDA’s regulated community. Previously, FDA regulations allowed waiver only for certain types of emergency research.

• The goal of the Final Rule is to advance medical product development without compromising the rights, safety and welfare of people participating in clinical research. FDA will develop a draft guidance to accompany the final rule and communicate with researchers, IRBs, patient communities and other interested parties about the rule.

• To understand how the regulations are being applied currently, a team at Vanderbilt systematically reviewed all studies meeting the NIH definition of clinical trial, published in the last year (May 2023 to April 2024) in JAMA and the New England Journal of Medicine (NEJM). Each trial was reviewed to determine whether informed consent prior to enrollment was required.

• During that time period, 33 trials were published in JAMA or NEJM that did not require informed consent. The reasons they did not require informed consent fell into 3 buckets: 1. Emergency and critical care studies (19 RCTs enrolling more than 15,000 patients where informed consent before enrollment was considered impracticable because of the urgency of the intervention and the condition of the patient); 2. Cluster-level: Infection Prevention (11 RCTs enrolling almost 3 million patients where consent was considered impracticable because the intervention was delivered to a group of patients different than the patients who would experience the outcomes (i.e. infection control) and due to the scale); 3. Interventions to promote communication and facilitate care (3 RCTs).

• In all, the systematic review found that 13% of trials did not require informed consent before enrollment, and 89% of patients (more than 3 million) in trials that did not require informed consent before enrollment.

• Upcoming FDA guidance on waiver and alteration of consent could provide the first regulatory guidance for minimal risk interventional research. While U.S. federal regulators do not currently provide guidance on minimal risk for interventional trial, trials are occurring with waiver and alteration.

• Upcoming FDA regulations present an important opportunity for the NIH Collaboratory’s goal of facilitating Learning Healthcare Systems capable of using embedded pragmatic trials to improve patient outcomes

 

Discussion Themes

-What is next from FDA in terms of the release of any further guidance? The timeline is challenging. It is under development and the FDA is working through the process.

–How does one judge the minimal risk criteria? For example, there are a lot of people who treat patients with diabetes with various medications. They may vary in terms of side effects, some of which might be rare. How do you identify if it is minimal risk? You have to meet 2 criteria to be considered for waiver of informed consent, both minimal risk and impracticability. Regardless of how you assess risk for a modestly sized study of a non-urgent intervention, then it would be conceivable to do it under consent and you would not need a waiver. If you are talking about health care system level studies, and for example, a study where these medications are already being used commonly and they are considered to be equivalent, that might be a case where people could consider choices like that. The framework of thinking is always “compared to what” and who is making the decisions. In clinical care is this something where the data is already good enough that physicians feel confident most of the time about one choice or would the patient have a strong preference about or a situation where the patient and clinician are not making the decision and there’s a spectrum in between. The question what does it look like now in clinical care can give a qualitative sense about the risk.

 

Tags

#pctGR, @Collaboratory1

Speaker

Joseph S. Ross, MD, MHS
Professor of Medicine and Public Health
Yale University

Slides

Keywords

YODA, Open Data Access, Data Sharing

Key Points

• Data sharing and open science are important because underreporting research through selective publication and reporting are common. Fifty percent of clinical trials are never published. Even when published, many trial publications are delayed more than 2 years, many are underreported, statistically significant findings are most likely to be reported, and nearly two-thirds of studies had a primary outcome that was changed, introduced, or omitted. Yet patients and physicians frequently make treatment decisions based on only a portion of the potentially available clinical data.

• The 1997 FDA Modernization Act (section 113) provided public access to information about ongoing clinical trials, which led to the creation of ClinicalTrials.gov. The International Committee of Medical Journal Editors (ICMJE) realized that ClinicalTrials.gov had slow growth, and it decided not to accept papers that had not been registered.

• In 2007 the FDA Amendments Act (FDAAA) broadened the scope, requiring expanded registry, trial results uploaded within 12 months of study completion, sharing of basic results and adverse events. Other funders started requiring similarly.

• Since 2007 there has been a sea change in thinking about data sharing. ICMJE said effective July 2018 manuscripts must contain a data sharing statement, and trials that begin enrolling participants on or after January 2019 must include a data sharing plan in the trial’s registration. Effective January 2023, all research funded or conducted by NIH must include in the proposal plans for management and sharing of all data necessary to validate and replicate research findings.

• NIH is now implementing these large-scale efforts for data sharing. Researchers have to submit a data management and sharing plan including data types and amount as well as metadata; related tools, software, and/or code that will be generated; standards (formats, documentation, dictionaries); data preservation, access, associated timelines; access, distribution or reuse considerations; oversight of data management and sharing; and budgets for allowable costs.

• The YODA Project platform launched to find ways to make clinical trial data more available for investigators to use. YODA started with core principles to answer what would the ideal clinical trial data sharing platform would look like.

• YODA was launched in partnership with Johnson & Johnson in 2014 after a proof-of-concept effort with Medtronic. J&J started sharing trial data from all pharmaceutical products (including legacy trials), device and diagnostic products as of 2015, and consumer products as of 2017. YODA established data access policy and procedures with input from a Steering Committee, experts, stakeholders and public comment.

• YODA has 459 trials currently available with about 90% that have been thus far requested. Of 385 requests submitted, 368 have been approved, 4 remain under review, 11 were withdrawn/closed, 2 were rejected. Nearly all of the requests that come in require some administrative revision, but one-quarter required scientific revisions after review for clarity. 157 manuscripts and 93 abstracts have been submitted and 119 and 89 of which have been published or presented, respectively.

• There is valuable strengthening of science that happens in these data sharing efforts. Numerous studies that might not otherwise been feasible to pursue. Data sharing has facilitated direct collaborations and developed efficiencies. Replication studies have supported the original study. There have been no instances of patient privacy breaches, no publications of spurious safety findings that received unwarranted attention or disrupted patient care, and no data have been used for commercial or litigious purposes.

Learn More

Visit the YODA Project website.

Discussion Themes

-What were some surprises compared to expectations over the 10 years of the YODA project? It is firmly in our mind that we should be living in an open science data world, and part of that is sharing data and trying to help improve patient care. How do we get the resources on the academic side to help investigators who want to do this work? On the industry side, partners have stepped up and made their data available. Across the field, how do we get more prior data available?

 

Tags

#pctGR, @Collaboratory1

Speaker

Erik van Zwet, PhD
Department of Biomedical Data Sciences
Leiden University Medical Center, the Netherlands

Slides

Keywords

P Values, Randomized Clinical Trials, Biostatistics

Key Points

• The “essence” of a clinical trial is a set of 3 numbers:  β, b, and s. β is the unobserved, “true” effect of the treatment. B is a normally distributed, unbiased estimator of β with standard error s. It is helpful to think of the estimate b as the true effect β plus a normally distributed “error” (b= β + N (0,s).

• There are 2 more quantities to consider. The z-stat, where z= b/s and the signal-to-noise ration (SNR= β/s). Usually in a clinical trial we want to test the null hypothesis that the treatment has zero effect. If the z statistic is greater than 1.96 then the p-value is less than 0.05. The power depends on the SNR. For example, if SNR = 2.8 then the power is 80%.

• Researchers have been studying data from the Cochrane Database of Systematic Reviews (CDSR) that include about 23,000 z-stats for the primary efficacy outcome of RCTs in the database.

• From the CDSR, we can estimate the distribution of the z-stat and, also, surprisingly, of the signal-to-noise ration. This is possible because there is such a simple relationship between the two. First, estimate the distribution of z directly from the observed z-stats, then derive the distribution of SNR by “removing” the standard normal error component.

• Researchers can use the estimated distributions of the z-stats and the SNRs to build a “synthetic” version of the CDSR with the same statistical properties of the real CDSR. With the synthetic database researchers can get insights to the real CDSR.

• The first thing to look at is power. RCTs are designed to have 80% or 90% power for testing that the true effect is actually 0 against an alternative that is considered to be of clinical interest, or plausible, or both. But the SNR is larger than 2.8 in only 12% of the CDSR trials.

• Many trials have low power against true effect. This has 2 consequences: if p is greater than 0.05 you might be discarding a useful treatment because you didn’t collect enough information to show that it works. If p is less than 0.05 you got very lucky. The effect estimate is likely overestimated and replication attempts will likely fail. A potential solution is to calculate the shrinkage estimation.

Learn More

Read more in NEJM Evidence.

Discussion Themes

-Overestimation was found across medical disciplines. There are a lot of small low power trials that should be published and part of math analyses. There is a lot of financial pressure to design a trial that is not too large, not to small and will have a good chance of success.

–Do you have any thoughts on how this applies to non-inferiority trials? Did you exclude those from these analyses? We did not exclude those. There is a small minority where we know they were non-inferiority trials. Maybe they would be differently sized.

Tags

#pctGR, @Collaboratory1

Speaker

Warren Jones, PhD
Director of Research, Marcus Autism Center
Children’s Healthcare of Atlanta
Norman Nien Distinguished Chair in Autism
Associate Professor, Dept. of Pediatrics
Emory University School of Medicine

Slides

Keywords

Autism Spectrum Disorder, Biomarkers, Social Visual Engagement

Key Points

• Autism affects 1 in every 36, impacting more than 9.1 million individuals and their families in the U.S. When we think about conditions that affect young children and their families, autism is one of the most common.

• Parents in the U.S. spend an average of 2-3 years between the time when they first begin to worry and the time when they finally receive a diagnosis. There are not enough expert clinicians or expert centers to meet public need. Disadvantaged families wait even longer.

• Clinicians need more measures that are objective, quantitative, dimensional and fine-grained, performance-based, standardized, efficient and community-viable, able to capture core features of social disability, and mechanistically relevant.

• Social visual engagement measures how children look at and learn from their surrounding social environment. Children look at and acquire information from what they are looking at. Researchers use eye tracking data to measure social visual engagement, which reflects early-emerging differences in autism spectrum disorder (ASD).

• In 3 studies, researchers tested the performance of eye-tracking-based assays of social visual engagement in 16-30-month-old children to accurately assess presence of ASD and accurately assess severity of ASD.

• The Discovery study included 719 participants, and the Replication study included 370 participants aged 16-30 months old. The initial Discovery study and first Replication study showed high sensitivity and specificity when comparing eye-tracking-based measures of social visual engagement with expert clinician diagnosis in children approximately 16-30 months old.

• With the results from the initial studies, the trial team embarked on a multi-site clinical trial at 6 sites across the U.S. 475 participants completed eye-tracking measurement of social visual engagement, expert clinical diagnosis, and a rating of expert diagnostic certainty.

• The study resulted in 335 participants with a reference standard certain diagnosis for ASD and 140 with a reference standard uncertain diagnosis for ASD. The children seen in the study who did not have autism did have other developmental diagnoses, which highlights the challenge of diagnosing children with developmental delays.

• Study results show a high sensitivity and specificity when comparing eye-tracking-based measures of social visual engagement with expert clinician diagnosis in children as young as 16-30 months old. Results also show a strong correlation with standardized assessments given by experienced clinicians

Learn More

Read more in JAMA.

Discussion Themes

-Is there an eventual hope that a tool like this could be used without a referral to a specialty center? Absolutely. We started at a point to try to develop a tool with gold standard outcomes, and we are going on to test screening studies in other age groups. Our hope is to extend and develop clinical tools that could be easier to use.

–What did you learn that informed the development of tools that would be more generalizable? This work has been conducted in conversation with FDA for many years, successfully moving something that was lab-based to increasingly more real-world. We are working toward making this more usable by general clinicians, asking what they need and want to make it more useful. For the screening studies, we looked at the SMART study framework for clinical trials to get to who is most likely to need a diagnostic evaluation.

 

Tags

#pctGR, @Collaboratory1

Speaker

Edward Qian, MD, MSc
Assistant Professor of Medicine
Vanderbilt University Medical Center
Division of Pulmonary and Critical Care Medicine

Slides

Keywords

ACORN, Antibiotics, Acute Kidney Injury, Critical Care Medicine, Randomized Clinical Trial, EHR

Key Points

• Sepsis is a common cause of critical illness and death. A common treatment is treating with vancomycin plus either piperacillin-tazobactam or cefepime. Piperacillin-tazobactam and cefepime have unique risk/benefit profiles but comparative data are lacking.

• There have been associations with piperacillin and acute kidney injury (AKI). Acute kidney injury is associated with a 6-to-8-fold increase in mortality in critically ill patients. There was concern that there is an association between concurrent vancomycin and piperacillin-tazobactam with creatinine elevations, but a retrospective, observational analysis was inconclusive.

• The ACORN Trial was randomized clinical trial to understand the effect of empiric antibiotic choice for patients in the ED and ICU. The trial enrolled 2,511 patients, who were adults who were in the hospital ED or ICU for less than 12 hours and received at least one dose of empiric cefepime or piperacillin-tazobactam. The intervention was a choice of empiric gram-negative antibiotic (cefepime or piperacillin-tazobactam).

• The screening process was done via an interruptive alert in the electronic health record (EHR) that was activated based on a provider placing an order for drugs. The alert would remind for exclusion criteria, and ask if the patient is eligible for ACORN trial.

• The trial operated under a waiver of informed consent. The research involved no more than minimal risk to subjects, and the research could not be carried out practicably without the waiver or alteration.

• The participant randomization happened within the EHR. The delivery of the intervention happened in the EHR, starting with an alert to enroll. Providers were sent an order set depending on the arm. The study team monitored for compliance within the EHR.

• The primary outcome was AKI ordinate scale between enrollment and Day 14. Secondary outcomes were major adverse kidney events within 14 days and days alive and free of delirium and coma to day 14.

• The trial found that, compared to cefepime, piperacillin-tazobactam does not increase the incidence of AKI. Compared to piperacillin-tazobactam, cefepime may decrease the number of days alive and free of delirium and coma.

Learn More

Read more in JAMA.

Discussion Themes

-How much pilot testing did you do? I was trained as an EPIC physician builder. In preparation for this trial, I underwent this training so all the pieces that you saw, I built and tested in a relatively short time period.

–How did you engage clinicians and make sure the question you were answering was one they wanted the answer to? There was a huge amount of leg work to engage stakeholders. We were talking to the leadership of ICU, nephrology, ID, and others for buy-in, to answer questions, and to make modifications based on suggestions.

–How portable are your EHR modifications? In particular, how big a lift would it have been to implement the same protocol in multiple EPIC installations? This whole trial was done while I was getting a master’s in applied informatics. The answer is no. The screening and randomization could be portable. Each installed EPIC has slight differences and quirks. There will be small differences in the application of the intervention.

 

Tags

#pctGR, @Collaboratory1