Proteins in Plasma Could Provide Information about IPF Disease Severity

December 3, 2019 – A study of proteins in plasma taken from patients in the IPF-PRO registry found that patients with IPF have a distinct circulating set of proteins and that select proteins correlate well with clinical measures of disease severity.

Certain proteins may provide important information about clinical measures of disease severity in patients with idiopathic pulmonary fibrosis (IPF), according to recent research from the DCRI.

IPF is a devastating lung disease characterized by progressive scarring of lung tissue. Although the incidence and prevalence of IPF are increasing, its clinical diagnosis and management remain challenging. The DCRI oversees the IPF-PRO registry, which has enrolled over 1,000 patients with IPF. The DCRI’s Scott Palmer, MD, MHS, serves as the principal investigator for the registry. The prospective, serially collected clinical data and biosamples in IPF-PRO provide a unique and rich infrastructure from which to grow both clinical and translational research initiatives aimed at improving care for patients with IPF.

Jamie Todd, MD, MHSThe most recent study from the IPF-PRO Registry, which was led by DCRI’s Jamie Todd, MD, MHS, and was published in Respiratory Research, included 300 patients in the registry and 100 patients without known lung disease as a control group. The patients without lung disease were drawn from the MURDOCK study, which is led by Kristin Newby, MD. Plasma was collected from patients at enrollment and analyzed using aptamer-based proteomics. Of more than 1,300 proteins assayed, 551 had statistically significantly different expression in patients with IPF as compared with individuals without lung disease—including 47 proteins that appeared to be at least 1.5 fold different in IPF relative to the control group. Notably, several of these proteins also correlated well with clinical measures of IPF severity, including lung function parameters and indicators of the extent of lung fibrosis observed on CT scans of the lungs.

“This research is the first phase of a stepwise approach,” Todd said. “Now that we have a better understanding of protein expression profiles in these patients and their relationship to the clinical measures we use to assess disease severity, we can conduct further studies to consider the value of these proteins as predictors of disease behavior or patient prognosis. We are really just at the tip of the iceberg of what is possible using the rich repository that is IPF-PRO.”

Todd added that she and her team, including Megan Neely, PhD, Robert (AJ) Overton, and Hillary Mulder of DCRI Biostatistics, are now embarking on this effort. Specifically, the team is using multivariable modeling approaches to consider whether sets of these proteins can provide information about clinically relevant outcomes in patients with IPF.

“Early observations indicate that some proteins confer important information about future outcomes in patients with IPF—information that has not been completely captured by considering clinical factors alone,” Todd said. Her team has submitted an abstract with these early observations to the American Thoracic Society meeting.

To Leverage Power of Machine Learning, Focus on Implementation is Needed

November 26, 2019 – With machine learning on the rise, DCRI’s Eric Peterson, MD, MPH, takes a retrospective look at prior efforts to implement risk prediction models and what lessons can be drawn from the past for current and future care.

Machine learning holds great potential for improving patient risk prediction; however, clinicians and health systems still will have challenges getting widespread adoption of these predictive models into clinical care, wrote DCRI’s Eric Peterson, MD, MPH, in a recent JAMA Viewpoint.

The use of automated machine learning algorithms for risk prediction could help physicians and patients make more informed care decisions. However, Peterson notes, machine learning does have limitations; in some instances, machine learning models do not outperform traditional regression models, and clinicians are sometimes frustrated that they cannot identify the factors that influence machine learning models’ predictions.

Eric Peterson, MD, MPHIn order to realize machine learning’s full potential in health care, Peterson writes, implementation is critical. He points to some of the first computer generation models that were developed at Duke in 1968.  While the Duke prediction tools were proven to perform as well or better than skilled clinicians, the models were never fully implemented in clinical care. One challenge was that physicians were not trained in statistics or probabilistic thinking and so did not see the new models’ value. “This failure to adopt predictive analytics into practice would be relived again and again in medicine,” Peterson notes.

The availability of electronic health records (EHRs) could be helpful during the implementation of machine learning models, but challenges still exist in this space, as well, such as developing data standards that can be used across health systems and formatting the data to ensure it is usable.

Multiple initiatives need to be put into place in order to leverage the power of EHRs combined with machine learning to improve patient care. Initiatives should target the entire health care delivery, from practitioners to patients; better quantitative training is needed to empower clinicians to use novel predictive models, while improved communication tools could better educate patients on their risk.

To read more from Peterson on the implementation of machine learning models, read the Viewpoint piece in JAMA.

AHA 2019: DCRI Study Tests Apixaban versus Warfarin in New Patient Population

November 25, 2019 – The exploratory study of patients with atrial fibrillation and on hemodialysis evaluated rates of bleeding when randomized to apixaban versus warfarin.

Apixaban may be a reasonable treatment option for patients with atrial fibrillation who also have renal disease and are receiving hemodialysis, according to late-breaking clinical trial results from RENAL-AF presented Saturday, Nov. 16 at the American Heart Association Scientific Sessions 2019.

Apixaban is one of a class of drugs called oral anticoagulants (OACs), which are used for stroke prevention in patients with atrial fibrillation. Previous trials, such as the DCRI-led ARISTOTLE, had shown apixaban, a newer OAC, to be superior in both safety and efficacy to the traditionally used warfarin; however, these drugs had not previously been tested in patients receiving hemodialysis to see if these results held true in a patient population with end-stage renal disease.

The DCRI’s Sean Pokorney, MD, MBA, was a member of the trial steering committee and presented the results of the study, which aimed to test the hypothesis that apixaban results in less bleeding than warfarin when given to patients on hemodialysis. Although the exploratory study was terminated early due to slower than anticipated enrollment, it revealed important findings that can be built upon in future studies. Notably, minority patients were underrepresented in the previous landmark trials evaluating the newer OACs, while 44.9 percent of the patients included in this study were black.

The study was underpowered, given the challenges with enrolling hemodialysis patients in this cardiovascular clinical trial.  The study found similar rates of major bleeding or clinically relevant non-major bleeding in both groups (25.6 percent on apixaban versus 22.2 percent on warfarin). Ongoing pharmacokinetic analyses may shed light that could help guide apixaban dose selection in this high-risk population.

“Bleeding rates and mortality rates are high in this population, and nearly three-quarters of the clinically relevant non-major bleeding we saw was associated with the hemodialysis access site,” Pokorney said. “Of course, any time a patient is taking an anticoagulant, measures to reduce bleeding, such as avoiding aspirin, should be considered. Although larger trials are needed, the first randomized data show us that apixaban may be comparable to warfarin when treating patients receiving hemodialysis.”

Other DCRI contributors include co-principal investigator Christopher Granger, MD, and steering committee members Hussein Al-Khalidi, PhD; Renato Lopes, MD, PhD; and Kevin Thomas, MD. The DCRI provided statistical support, and statistician Kerry Lee, PhD, served on the data safety monitoring board. The DCRI’s Clinical Events Classification group also made important contributions to the study by adjudicating all clinical events. RENAL-AF was funded by Bristol-Myers Squibb and Pfizer.

DCRI Faculty Recognized on Global “Highly Cited” List

November 22, 2019 – Fifty-four researchers from Duke, including nine from the DCRI, were recognized for their high citation rates.

This year’s “Highly Cited Researchers” recognizes nine researchers from the DCRI as “researchers with broad community influence.”

The list, which is released annually by ISI-Web of Science, calculates how many times authors’ work appear in the citations of other papers. The list also enables a global comparison of academic institutions’ citation rates—Duke University, with 54 researchers recognized, is in a four-way tie for eighth most cited university.

The data to develop this metric is taken from 21 broad research fields defined by sets of journals. This year’s honorees from the DCRI, as well as the fields for which they were recognized, include:

  • Lesley Curtis, PhD – Cross-Field
  • Pamela Douglas, MD – Clinical Medicine
  • Christopher Granger, MD – Clinical Medicine
  • Adrian Hernandez, MD, MHS – Clinical Medicine
  • Magnus Ohman, MBBS – Clinical Medicine
  • Manesh Patel, MD – Clinical Medicine
  • Michael Pencina, PhD – Social Sciences and Clinical Medicine
  • Eric Peterson, MD, MPH – Clinical Medicine
  • Bryce Reeve, PhD – Social Sciences

Several of those recognized have also made the list in previous years. Former DCRI faculty members Christopher O’Connor, MD, Robert Harrington, MD, and Robert Califf, MD, were also recognized in the field of clinical medicine.

Recent statistics compiled by the DCRI show that since 1996, the DCRI’s work has been cited in more than 760,000 scientific articles.

“I am proud to see our faculty being cited by our peers and colleagues,” said Lesley Curtis, PhD, the DCRI’s interim executive director. “By delivering on our mission to share knowledge, we are enabling other researchers to build upon our work to conduct further studies and make more discoveries.”

Data Monitoring Committees Need Complete Data to Protect Patients

November 21, 2019 – Data monitoring committees could be provided more and better data in order to fulfill its role of protecting trial participants.

Data monitoring committees (DMCs) play a critical role in clinical trials by protecting patients—especially in studies involving high-risk populations or potentially harmful treatments—and in order to fulfill this role successfully, these committees should have access to all data at each interim review, wrote the authors of a recent paper published in the Annals of Internal Medicine.

Frank Rockhold, PhDThe DCRI’s Frank Rockhold, PhD, and Robert Bigelow, PhD, served on the writing group for the paper, which provided recommendations for summaries produced by statistical data analysis centers (SDACs) and provided to DMCs, or data and safety monitoring boards.

The authors note that often DMCs only receive the data thought necessary for making decisions about safety. However, they argue, when these groups receive limited data and information, they are lacking important context. “To fulfill its mandate to protect trial participants, the DMC needs timely access to all relevant information, including efficacy data,” the authors write.

The paper also identifies problems with reports currently produced by SDACs, which are often lengthy and difficult to digest. “To foster efficient, informed decision making, reports should be streamlined, concise documents that display important data in optimally informative ways,” the authors recommend.

In addition, the authors advocate for implementation of graphical summaries that integrate benefits and harms. Visuals could help the DMCs make more accurate risk-benefit analyses with less room for bias and misinterpretation of the data. The paper walks through several examples of graphical representations that could provide information that would be helpful to the DMC. This will require investment in resources to develop data visualizations, which can then be reused in future trials.

“In writing this paper, we concluded that several immediate steps can be taken to help DMCs do their jobs more effectively,” Rockhold said. “DMCs will only be equipped to protect patients to the fullest extent when they have complete and easily digestible information to guide their decisions.”

AHA 2019: Machine Learning Not Always the Answer, DCRI Study Finds

November 20, 2019 – A DCRI-led study used two registries to compare three different types of machine learning algorithms with stepwise logistic regression.

Although machine learning is a novel technique that has impressive applications in health care, in some settings, these novel approaches do not improve upon traditional approaches, according to a recent oral abstract DCRI fellow Zak Loring, MD, presented Saturday at the American Heart Association 2019 Scientific Sessions.

The analysis compared three different machine learning techniques—random forests, gradient boosting, and neural networks—with traditional stepwise logistic regression to determine which technique produced the most accurate outcomes model to predict risk for atrial fibrillation patients.

The study team tested the models in two different registries of patients with atrial fibrillation: ORBIT-AF, which includes 23,000 patients, and GARFIELD-AF, which includes 52,000 patients across 35 countries. The team also developed a common data model so that each model could be used across both registries to test external validity.  This is important, Loring said, because often machine learning algorithms are powered for to be highly predictive in one specific patient population.

Zak Loring, MD“Some machine learning algorithms yield impressive results, but may not yield the same results when applied outside the original sample,” Loring said. “Often we build algorithms in clean clinical trial datasets, but when we apply it outside that setting to a wider population that would not have been eligible for the clinical trial, we see weaker performance. It is important to account for generalizability when building these algorithms.”

In comparing the machine learning models to the logistic regression model, the team examined two other measures in addition to external validity: discrimination capacity and calibration. In discrimination capacity, the machine learning method performed as well or slightly worse than traditional regression; in calibration, machine learning performed worse.

In addition, the traditional regression used structured data elements like case report forms, a positive in this scenario because it makes for an interpretable model in which clinicians can identify risk factors.

“One major complaint associated with machine learning models is that they can sometimes be a bit of a black box,” Loring said. “That is, even if they can accurately predict risk, they can’t tell you why that risk is present.”

Loring added that these results show that despite the promise of machine learning, there are likely tasks that are better suited for older techniques. One area that warrants more discussion is the structure of registries. In order to fully harness the power of machine learning, it might benefit researchers to build registries with fewer binary variables and more continuous data.

Other DCRI contributors to this analysis include Jonathan Piccini, MD, MHS; David Carlson, PhD; Eric Peterson, MD, MPH, and former DCRI statistician Karen Pieper, MS.

AHA 2019: Data Highlight Differences in Discrimination of Noninvasive CAD Testing

November 18, 2019 – A new analysis of a DCRI study revealed that younger patients and older patients may need to undergo different types of testing for coronary artery disease in order to more accurately predict risk.

In middle-aged patients with stable symptoms suggestive of coronary artery disease (CAD), anatomic testing with coronary CT angiography (CTA) provided better prediction of risk, also known as prognostic discrimination.

Angela Lowenstern, MD

In contrast, functional (stress) testing provided better prognostic discrimination for older members of this patient population, according to a new analysis of the DCRI-led PROMISE study presented Monday by DCRI fellow Angela Lowenstern, MD, at the American Heart Association Scientific Sessions 2019 in Philadelphia.

“This study indicates that in patients with stable symptoms suggestive of CAD, there are important differences in presentation, test results, and prognostic capabilities of noninvasive testing across age,” Lowenstern said. “Specifically, coronary CTA offers additional risk stratification information for patients aged 45-64, with stress testing results associated with risk for cardiovascular death or myocardial infarction among patients 65 and above. These results support further exploration of age-specific approaches to the noninvasive evaluation of CAD.”

Suspected CAD is one of the most common, potentially life-threatening diagnostic problems encountered by clinicians. As many as five million patients with chest pain undergo noninvasive tests each year, with the goal of confirming the diagnosis and providing information about future risk.

Lowenstern was lead author of the AHA poster, titled “Differences in the Non-Invasive Evaluation for Coronary Artery Disease and Its Prognostic Implications by Patient Age: An Evaluation of the PROMISE Trial.” Other DCRI contributors were Karen Alexander, MD; C. Larry Hill, PhD; Brooke Alhanti, PhD; Michael Nanna, MD; Rajendra Mehta, MD; and Pamela Douglas, MD. The results were published simultaneously in JAMA Cardiology, with Lowenstern as lead author, and the same DCRI contributors.

Sponsored by Duke in collaboration with the National Heart Lung and Blood Institute (NHLBI), the PROMISE study was a prospective, randomized trial comparing the effectiveness of two initial diagnostic strategies—coronary computed tomographic angiography or functional testing—in patients with symptoms suggestive of CAD from 193 North American sites. A total of 8,966 patients were randomized to undergo testing and had interpretable results. The main outcome measure was a composite of cardiovascular death and myocardial infarction over a median follow-up of 25 months.

“Our findings indicate that there is no ‘one size fits all’ diagnostic strategy for patients with suspected coronary artery disease,” said Pamela Douglas, MD, PROMISE principal investigator and the senior author of the JAMA Cardiology publication. “Instead, physicians need to personalize imaging decisions by pursuing the best test for each individual patient after taking into consideration the growing body of data regarding imaging outcomes.”

AHA 2019: Stent Thrombosis is Rare but Serious in Patients with AF and Recent PCI

November 16, 2019 – Rates of stent thrombosis were lowest in patients treated with apixaban rather than a vitamin K antagonist (VKA), such as warfarin, and with aspirin rather than placebo.

Stent thrombosis occurs in less than 1 percent of patients with atrial fibrillation (AF) in the six months after percutaneous coronary intervention (PCI), according to a new analysis of the results from the DCRI-led AUGUSTUS trial.

The analysis also found that rates of stent thrombosis are numerically lower in patients treated with apixaban compared with a vitamin K antagonist (VKA), such as warfarin, and also with aspirin rather than placebo.

Renato LopesThe analysis was presented by the DCRI’s Renato Lopes, MD, PhD, (pictured), principal investigator of AUGUSTUS, on Saturday at the American Heart Association Scientific Sessions 2019, in Philadelphia, PA.

The serious clinical impact of stent thrombosis was confirmed by the trial, which involved current generation drug-eluting stents. The analysis found that among patients with definite or probable stent thrombosis, on the day of the thrombotic event, 70 percent (21 patients) also experienced myocardial infarction; 53 percent (16) had an urgent revascularization; and 40 percent (12) died. There were clinical consequences for all patients who had a stent thrombosis.

AUGUSTUS is an international, multicenter randomized trial designed to compare apixaban with vitamin K antagonists and aspirin with placebo. The trial enrolled patients with AF who develop acute coronary syndrome (ACS) and/or undergo PCI and are receiving a P2Y12 inhibitor antiplatelet drug, such as clopidogrel, prasugrel or ticagrelor. A total of 3,498 patients underwent PCI with stenting during their qualifying clinical event and were at risk for stent thrombosis. Overall, there were 57 stent thrombosis events over six months, of which 20 were definite, 10 probable, and 27 possible. Stent thrombosis was adjudicated by a clinical events committee blinded to randomized treatment allocation and classified according to the Academic Research Consortium. The DCRI’s Clinical Events Classification (CEC) group was responsible for the entire adjudication process of the AUGUSTUS trial.

Lopes was lead author of the AHA presentation, titled, “Stent Thrombosis After Percutaneous Coronary Intervention Among Patients With Atrial Fibrillation Treated With Apixaban or Aspirin: Insights From the AUGUSTUS Trial.” Other DCRI authors of the AHA presentation were Daniel Wojdyla, MS; Christopher Granger, MD; and John Alexander, MD, MHS.

The data were published simultaneously in the journal Circulation. In addition to Lopes, Granger, and Alexander, the DCRI’s Laine Thomas, PhD, contributed to the paper.

“Our study provides important insights about the duration of aspirin therapy after PCI in patients with atrial fibrillation,” Lopes said. “For the majority of patients, the AUGUSTUS data support the use of apixaban and a P2Y12 inhibitor without aspirin during the first six months after PCI, based on the almost two-fold increase in bleeding with aspirin use. It is important to keep in mind that almost all patients used aspirin for the initial days after the PCI—on average for six days—before stopping aspirin therapy.”

“However, in patients with a high risk of stent thrombosis and an acceptable risk of bleeding, using aspirin up to 30 days after PCI should be considered, rather than discontinuing aspirin therapy around the time of hospital discharge. Further studies are warranted to identify the patients who might benefit most from this strategy,” said Lopes.

Bristol-Myers Squibb and Pfizer provided funds for the AUGUSTUS study.

DCRI Contributes to Important Results Presented at AHA 2019

November 16, 2019 –The DCRI served as the statistical and data coordinating center for ISCHEMIA, a late-breaking clinical trial that indicated invasive heart procedures may not reduce the chance of experiencing a major, disease-related event for patients with severe but stable heart disease.

The DCRI played an important role as the statistical and data coordinating center for a late-breaking clinical trial presented today at the American Heart Association’s Scientific Sessions 2019.

Results from ISCHEMIA, which were shared today in Philadelphia via four presentations, indicate that invasive heart procedures may not result in lower rates of major, disease-related events for patients with severe but stable heart disease.

DCRI faculty Sean O’Brien, PhD and Karen Alexander, MD, served as co-principal investigators for the statistical and data coordinating center, participating in all of the data structure and cleaning efforts, as well as data analysis and project oversight. The DCRI’s Frank Rockhold, PhD, also supported statistical work on the trial. Data analysis for the data safety monitoring board was provided by Vanderbilt University’s Frank Harrell, PhD.

The DCRI’s Daniel Mark, MD, oversaw the quality of life outcome assessments, in partnership with John Spertus, MD, at Mid-America Heart Institute.

ISCHEMIA, funded by the NIH’s Heart, Lung, and Blood Institute, sought to determine the most effective course of treatment for patients with ischemia, a chronic, symptomatic condition that is characterized by reduced blood flow to the heart muscle. All patients enrolled in the trial received medication and lifestyle advice, but half of patients underwent routine, invasive procedures—such as stent implants or bypass surgery—while the other half did not. Because intervention can be both risky and costly, the goal of ISCHEMIA was to ascertain whether intervention led to improved outcomes for this group of patients.

Trial results showed that patients who received invasive therapy saw no reduction in five disease-related events when compared to patients who received only medications and lifestyle advice. The five events measured were cardiovascular death, heart attack, hospitalization for unstable angina, hospitalization for heart failure, and resuscitation after cardiac arrest.

However, for patients with symptoms of angina, or chest pain, invasive treatments resulted in improved long-term symptom relief and better quality of life.

To learn more about ISCHEMIA and its results, read the news release.

AHA 2019: Type 2 Myocardial Infarction Results in Higher Risk Than Type 1

November 16, 2019 – Patients who experienced Type 2 myocardial infarction were older and more likely to have comorbidities and risk factors than those who experienced Type 1.

Patients who experience Type 2 myocardial infarction are at higher risk for additional cardiovascular events and death than patients who experience Type 1 myocardial infarction (MI), according to a new analysis from the DCRI.

Chiara Melloni, MDThe analysis, which the DCRI’s Chiara Melloni, MD, presented as an oral abstract Saturday at the American Heart Association Scientific Sessions 2019 in Philadelphia, examined data from three completed DCRI-led trials to shed new light on the differences between patients who experience Type 1 MI and those who experience Type 2 MI.

All three trials—TRACER, which involved patients with acute coronary syndrome; EUCLID, which studied peripheral artery disease; and EXSCEL, which examined patients with Type 2 diabetes—had been adjudicated by the DCRI’s Clinical Events Classification (CEC) group, for which Melloni acts as a faculty adjudicator. This allowed the study team to have access to all the cardiovascular events that had been reported and adjudicated in the three trials and stored in the CEC databases.

Although Type 2 MI was initially defined in 2007, it remains difficult to diagnose because of the heterogeneity of the patient population that experiences it, as well as the different cardiac markers, assays, and cut-off used, said Melloni. While Type 1 MI is caused by a plaque rupture and has clear symptoms, Type 2 MI, which is characterized by a mismatch between oxygen demand and supply in the coronary vessel, is rarer and can be associated with many different comorbidities.

The analysis examined and compared baseline characteristics of patients enrolled in their respective trials based on the type of MI they experienced after inclusion in the study. Patients who experienced a Type 2 MI during the trial tended to be older and were more likely to have comorbidities and CV risk factors—overall, the Type 2 MI group was a sicker population than the Type 1 group.

The study team also examined outcomes of patients who experienced an MI during a trial. When followed from a year after their initial MI, patients with Type 2 MI were at a higher risk for subsequent MIs or death than their Type 1 counterparts.

One of the key messages of the analysis, Melloni said, was that the results held true regardless of the level of the cardiac troponin released in the blood. The rise and fall of troponin levels are used to ascertain whether a patient has experienced MI, and the peak level of troponin was collected for each patient. Even if patients with Type 2 MI experienced relatively lower levels of troponin leak during the time of their first MI compared with type 1 MI patients, they were at higher risk for negative outcomes than patients with Type 1 who had the same levels of troponin leak.

Other DCRI contributors to this analysis include Karen Alexander, MD; Angie Wu, MS; Schuyler Jones, MD; Rajendra Mehta, MD; David Kong, MD; Thomas Povsic, MD; Manesh Patel, MD; Sana Al-Khatib, MD; and Renato Lopes, MD, PhD. Matt Wilson and Marsha Marquess, who both work on the CEC team, were also instrumental in helping the study team identify which trials could provide the most suitable datasets to perform this analysis.