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Big data, Clinical Trials, Ethics, Public Health, Research, Stanford News, Technology

Build it (an easy way to join research studies) and the volunteers will come

Build it (an easy way to join research studies) and the volunteers will come

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Just nine days after the launch of Stanford Medicine’s MyHeart Counts iPhone app, 27,836 people have consented to participate in this research study on cardiovascular health.

“To recruit that many patients into a traditional clinical trial would take years and hundreds of thousands of dollars,” said Michael McConnell, MD, professor of cardiovascular medicine and principal investigator for the MyHeart Counts study.

MyHeart Counts was built with Apple’s new ResearchKit, a software development framework that can be used to create apps that turn an iPhone into a research and data collection tool. Leveraging a smartphone’s built-in accelerometers, gyroscopes, camera and GPS sensors, medical researchers can easily and inexpensively collect streams of data on exercise, diet and biometrics. Unlike most traditional clinical trials, which capture only a snapshot of patient data, ResearchKit studies are able to collect data from thousands of participants simultaneously, over long periods of time.

While the potential for this technology to accelerate medical research is tantalizing, the ethical issues of this shift in researcher-volunteer interactions took Stanford researchers and collaborator Sage Bionetworks nine months to work out.

“One of the big challenges in designing this study was to develop an ethical mechanism for informed consent on mobile devices,” David Magnus, PhD, director of the Stanford Center for Biomedical Ethics, told me. “It was essential that volunteers understand the nature of the research and what it means for them.”

The concept of informed consent is an important tenet of any research institution’s commitment to respect individuals and to “do no harm.” Without face-to-face meeting between a researcher and volunteer, there could be misunderstandings about risks, benefits and time commitments.

Stanford bioethicists are on the leading edge of addressing the communications challenges of these new frontiers in medical research. Rethinking long, text-based consent forms, they are exploring alternatives, such as audio, video, animation and interactivity.

For example, a team of bioethicists from Stanford and the University of Washington recently released animated videos that explain comparative-effectiveness research within medical practices to potential volunteers. Next, they’ll be developing media-rich tools to explain the risks and benefits of research that uses electronic medical records and stored biological samples.

To solicit ideas on how to best regulate this brave new world of informed consent, the U.S. Food and Drug Administration just posted draft guidance on “Use of Electronic Informed Consent in Clinical Investigations.” Public comments will be accepted through May 7, 2015.

To sign up for the MyHeart Counts study, visit the iTunes store.

Previously: Harnessing mobile health technologies to transform human healthMyHeart Counts app debuts with a splashStanford launches iPhone app to study heart health and Video explains why doctors don’t always know best
Photo by iMore

Big data, Public Health, Research, Technology

Harnessing mobile health technologies to transform human health

Harnessing mobile health technologies to transform human health

McConnell-YeungAn estimated seven in ten U.S. adults say they track at least one health indicator, and 21 percent of this group use some form of technology to track their health data, according to data from the Pew Research Center. But these figures are likely to skyrocket thanks to health platforms such as Google Fit, Apple’s HealthKit and AT&T ForHealth, which use sensors built into smartphones and wireless fitness devices to record physical activity.

This data deluge is a goldmine for biomedical research and drug development, particularly with the introduction of Apple’s ResearchKit. The software, which powers the Stanford-developed MyHeart Counts app, allows users to better understand their health data while providing researchers the opportunity to access it for future studies.

In a recent Huffington Post article, Ida Sim, MD, PhD, professor of medicine at University of California, San Francisco, noted that such technologies hold the potential to encourage the general public to participate in medical studies and make the research community more collaborative and open. “There’s a new movement in academic research called participatory research, where patients are part of the groups that should be asking: ‘What questions are interesting? What should we test?’” Sim said in the piece. “The public could start seeing research as something that isn’t imposed on [them], but as an activity that we all do together so that we can learn together.”

This May, Sim, who co-directs of Biomedical Informatics at UCSF’s Clinical and Translational Sciences Institute, will speak at Stanford’s Big Data in Biomedicine Conference on how health information collected on mobile devices holds the potential to inform clinical decisions and transform health care. As a co-founder of non-profit Open mHealth, she and colleagues are leading the charge to build open source software that facilitates sharing and integration of digital health data.

Below she outlines how leveraging mobile health data can improve how physicians diagnose, treat and prevent disease and the challenges in facilitating the sharing and integration of this vast treasure trove of data.

What are the large-scale opportunities to harness the rapidly growing reservoir of information to improve biomedical research and human health?

We can use this data to do a variety of things like combining genomic information and behavior data from wearables to discover new insights into health and disease.

We can also move from what works on average to more tailored programs focused on the idea of what works for me. For example, if we employ A/B-like testing with digital health, genomics, and other data combined, we can understand which interventions work for an individual and under what contexts, allowing for more tailored healthcare.

Finally, we can learn about a person beyond their clinical visit – which is only a small slice of their “health pie.” By getting multiple health snapshots, doctors will be able to provide patients with better medical support and preventative strategies that support overall physical and mental well-being.

What are the major challenges in unlocking the potential of digital health data?

When we write a sentence, we construct the sentence with grammar. We use vocabulary to fill in the blanks to give meaning to the sentence. Meaning is lost when either the grammar or the vocabulary is ambiguous or not shared between parties. In a similar way, making sense of data from various digital health devices is challenging when the devices don’t represent data the same way.

Currently, wearable devices and other healthcare tools describe the data they collect using their own languages that are not shared or integrated with other devices. For example, a Wi-Fi enabled weight scale might represent data as “weight: 88” but we have no clue if that means 88 kg, femptograms, lbs, or stones. A calorie counter might represent calories as “calories: 400” but we have no clue if this was calories expended or calories consumed. For clinicians, these kinds of ambiguities are show stoppers that lock up the potential of digital health data.

In addition, data from the devices themselves are stored in silos, meaning that it is not easy for patients or clinicians to combine and view multiple data streams together. Blood pressure from one device isn’t syncing with weight data from another, which can lead to an incomplete picture of a patient’s health over time.

If we strive for greater interoperability with a common language and structure for both understanding and integrating digital health data, we can help to bring clinical and patient needs together for better health-care outcomes.

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Big data, Genetics, Research, Science, Stanford News

Caribbean skeletons hold slave trade secrets

Caribbean skeletons hold slave trade secrets

5598998640_3c9968b4ac_zI was excited yesterday to see the Los Angeles Times cover a really neat story out of the laboratory of geneticist Carlos Bustamante, PhD. He and his colleagues at the University of Copenhagen used genetic analysis to solve a 300-year-old mystery with origins in the city of Philipsburg on the island of Saint Martin.

Philipsburg is an idyllic retreat for thousands of tourists each year. Not so for three skeletons recently unearthed during a construction project in the city. The skeletons were those of African-born slaves who had been shipped from their homeland more than 300 years ago to the Caribbean island to serve as forced laborers. Like millions of other enslaved Africans, the two men and one woman likely led difficult lives and died young.

Now the researchers have identified the regions in Africa the individuals likely lived before their capture. To do so, they examined tiny, highly fragmented bits of ancient DNA that survived the hot, humid conditions of the tropics in the roots of the skeletons’ teeth.  The research was published this week in the Proceedings of the National Academy of Sciences.

As Bustamante explained in our release:

Through the barbarism of the middle passage, millions of people were forcibly removed from Africa and brought to the Americas. We have long sought to use DNA to understand who they were, where they came from, and who, today, shares DNA with those people taken aboard the ships. This project has taught us that we cannot only get ancient DNA from tropical samples, but that we can reliably identify their ancestry. This is incredibly exciting to us and opens the door to reclaiming history that is of such importance.

Bustamante is co-author of a paper describing the research.The study was led by Hannes Schroeder, PhD, a molecular anthropologist from the University of Copenhagen, and Stanford postdoctoral scholar Maria Avila-Arcos, PhD. The research was initiated in Denmark, and the senior author of the study is Thomas Gilbert, PhD, of the University of Copenhagen. More from our release:

Researchers could tell from the skeletons found in the Zoutsteeg area that the three people were between 25 and 40 years old when they died in the late 1600s. The skulls of each also bore teeth that had been filed down in patterns characteristic of certain African groups. But this alone wasn’t enough to pinpoint where the individuals originated on the African continent.

Schroeder and Avila-Arcos used a technique developed by study co-author Meredith Carpenter, PhD, a postdoctoral scholar in the Bustamante laboratory, to fish out snippets of ancient DNA from the material inside the teeth for sequencing. They then used a different technique called principal component analysis to identify the distinct ethnic groups from which each individual likely originated. The findings illuminate a tumultuous period of time in the Americas and may provide insight into subsequent population patterns and perceived ethnic identities. They also open doors to new advances in genealogy and historical research. As Bustamante told me:

Several years ago, we were part of the team that sequenced the genome of Otzi, the iceman, and we were able to show that the people alive today that most closely match him genetically are Sardinians. This incredible precision was possible because we, as a community, had invested lots of resources in understanding patterns of DNA variation in Europe. I started to talk about the ‘Otzi rule,’ or the idea that we should be able to do for all people alive today what we can do for a 5,000-year-old mummy.

Previously: Melting pot or mosaic? International collaboration studies genomic diversity in Mexico, Caribbean genetic diversity explored by Stanford/ University of Miami researchers and Recent shared ancestry between Southern Europe and North Africa identified by Stanford researchers
Photo by alljengi

Big data, Genetics, Global Health, Infectious Disease, Research, Science

The benefit of mathematical models in medicine

The benefit of mathematical models in medicine

1024px-Free_range_chicken_flockTheoretical modeling sounds like it has, at best, a distant connection to the day-to-day concerns of medical professionals who care for or research the needs of patients. But when I spoke recently with Noah Rosenberg, PhD, a population geneticist at Stanford and editor of the scientific journal Theoretical Population Biology, he pointed out that modeling can offer distinct benefits to those in medical fields like epidemiology and genetics.

“We see a lot of occasions in public discussions of areas like the spread of epidemics, the demography of aging populations, and big data analysis in genomics where part of the backdrop arises from theoretical population biology work,” Rosenberg said. “We hope to spread the word that there is a place for the kinds of theoretical and mathematical insights that can contribute to those important topics.”

Rosenberg noted that papers in the journal often span the divide between mathematics and biology, but they have a few things in common. In an editorial he published last month in the journal, Rosenberg describes an ideal study for the journal – namely that first, “the mathematical work is motivated by a genuine problem in biology, and there’s a need for theory to resolve the problem,” he said. Secondly, the mathematical work is substantial enough that it uncovers new potential relationships or new explanations for a phenomenon, and lastly, that the advances contribute to our understanding of biology – though some of the best papers in the field can also have a big impact on the field of mathematics, too.

When I asked him to talk about what that would mean for studies that touch on health research, he pointed me to a couple of fascinating papers. One is a paper by Shai Carmi, PhD, and colleagues that explains a new way to look at shared DNA strands between people in order to understand their relatedness and the amount of overlap in their genomes. This has implications for how we think about “the way in which genes descend within families, including genes that may be related to a disease.” It’s one of the journal’s most downloaded papers, Rosenberg told me.

The second is a study by Maciej Boni, PhD, and colleagues that incorporates how decisions that poultry farmers in Southeast Asia make about market conditions might affect the spread of avian influenza in their flocks. When avian flu is identified in a region, poultry flocks are usually culled. It’s an interesting example of how human behavior can affect disease dynamics.

Rosenberg noted that the studies and models that are able to incorporate human behavioral patterns are among the most interesting that he sees. Nailing down how people’s decisions affect the course of an outbreak is notoriously difficult, but like the avian influenza paper demonstrates, mathematical models make it possible to explore the consequences of different assumptions about these decisions.

Rosenberg says that it’s even possible to make mathematical models of cultural practices (like deciding not to immunize your children) and how they spread among groups of people. One public laboratory this interaction is currently playing out in is the measles outbreak that got its start at Disneyland in December. The outbreak topped 100 cases nationwide, mostly among families that refuse to vaccinate their children. “It’s the intersection between human behavior and dynamics of disease,” he said. “Putting those together in a mathematical model to predict what might happen is the kind of work that appears in Theoretical Population Biology.”

Previously: Stanford physician Sanjay Basu on using data to prevent chronic disease in the developing world and Facebook app models how viruses spread through human interaction
Photo by Woodley Wonderworks

Big data, Events, Pediatrics, Pregnancy, Research, Stanford News

Stanford hosts inaugural Childx conference this spring

Stanford hosts inaugural Childx conference this spring

Chandler's 15 Month CheckupRegistration is now open for the first ever Childx conference, a TED-style conference focused on inspiring innovation in pediatric and maternal health. The conference will bring thought leaders from several disciplines to the Stanford campus April 2 and 3 for two days of conversation about how to harness many branches of medicine to solve the health problems of pregnancy, infancy and childhood.

“Pediatric medicine faces unique challenges,” said systems biology researcher Dennis Wall, PhD, who leads the conference’s scientific advisory board. “Most children are quite healthy, which can make it difficult to attract adequate research attention to severe pediatric diseases that affect relatively few children. At the same time, every child’s health status is influenced by a complex array of factors, which cause decades-long ripple effects as today’s children mature into tomorrow’s adults.”

The conference, developed and sponsored by Stanford’s Child Health Research Institute, has five themes:

  • Definitive stem cell and gene therapy for child health
  • The arc of fetal, developmental/cognitive, and adult health
  • Accelerating child and maternal health innovation
  • Precision medicine for rare and historically untreatable childhood disease
  • The health ecosystem and the impact of social, economic, political, environmental, and cultural issues on children’s health and well-being

Featured guests include Martin Andrews, who leads Glaxo Smith Kline‘s rare diseases team; Nadia Rosenthal, PhD, founding director of the Australian Regenerative Medicine Institute; Harvard’s Matthew Gillman, MD, an expert on early-life prevention of chronic disease; Sheena Josselyn, PhD, a neuroscientist at the University of Toronto and the Hospital for Sick Children who studies molecular processes behind learning and memory; and Donald Schwarz, MD, the director of the Robert Wood Johnson Foundation, as well as a large cast of Stanford stars from several areas of pediatric medicine.

“Pediatric medicine needs to turn its focus more to creating advanced, technology-enabled solutions that will increase our ability to detect, monitor and treat child health,” Wall said. “No pediatric conference to-date has combined these key themes of precision healthcare with the most pressing challenges and opportunities in child and maternal health. The inaugural Childx will be the first conference to do so.”

The conference will welcome maternal and child health researchers, clinicians, investors, industry experts and interested community members. Early bird registration is open through February 28.

Big data, NIH, Research, Videos

Fly through the inside of a mouse lung

Fly through the inside of a mouse lung

Take a 50-second ride through the inside of an adult mouse lung in this video created by Rex Moats, PhD, scientific director at Children’s Hospital Los Angeles. A post published today on the NIH Director’s Blog describes the animation and points out that the video is a prime example of how scientists are using big data to make biomedical research more accessible to the public:

We begin at the top in the main pipeline, called the bronchus, just below the trachea and wind through a system of increasingly narrow tubes. As you zoom through the airways, take note of the cilia (seen as goldish streaks); these tiny, hair-like structures move dust, germs, and mucus from smaller air passages to larger ones. Our quick trip concludes with a look into the alveoli — the air sacs where oxygen is delivered to red blood cells and carbon dioxide is removed and exhaled.

… [Moats] created this virtual bronchoscopy from micro-computed tomography scans, which use X-rays to create a 3D image. The work demonstrates the power of converting Big Data (in this case, several billion data points) into an animation that makes the complex anatomy of a mammalian lung accessible to everyone.

Speaking of the power of big data, the Big Data in Biomedicine conference returns to Stanford May 20-22. For more information about the program or to register visit the conference website.

Previously: Big data = big finds: Clinical trial for deadly lung cancer launched by Stanford study and Peering deeply – and quite literally – into the intact brain: A video fly-through

Big data, Clinical Trials, FDA, NIH, Research, Science

Transparency in clinical trials: The importance of getting the whole picture

Transparency in clinical trials: The importance of getting the whole picture

New rules for clinical trials Scope blog 2015.02.02Last week, the Journal of the American Medical Association ran a Viewpoint article from Francis Collins, MD, PhD, director of the National Institutes of Health and Kathy Hudson, PhD, deputy director of NIH, about the U.S. Health and Human Services’s plans to beef up transparency of clinical trials of FDA-regulated drugs and devices.

As they write, the rate of results-sharing for clinical trials is fairly dismal. Some of the reasons for this go beyond researchers; for example, it’s extremely difficult to get negative results published in scientific journals. Collins and Hudson point out that another avenue exists for sharing summary results: NIH’s ClinicalTrials.gov website. But even there, less than one-third of researchers had shared results within four years of the end of their studies. Collins and Hudson are critical of this lapse in data sharing:

Without access to complete information about a particular scientific question, including negative or inconclusive data, duplicative studies may be initiated that unnecessarily put patients at risk or expose them to interventions that are known to be ineffective for specific uses. If multiple related studies are conducted but only positive results are reported, publication bias can distort the evidence base. Incomplete knowledge can then be incorporated into clinical guidelines and patient care. However, one of the greatest harms from nondisclosure of results may be the erosion of the trust accorded to researchers by trial participants and, when public funds are used, by taxpayers.

The new rules make the expectations to report some summary details about clinical trials, including adverse events, explicit. Although NIH has always encouraged sharing of summary results, the rules haven’t always been explicit. Now that there will be detailed guidance, the penalty for not complying will be harsher:

Thus, with the implementation of clearer requirements, augmented support materials and resources, and facilitated reporting, the NIH expects that investigators and sponsoring organizations will have the necessary tools to provide accurate, complete, and timely trial results submissions. However, for grantees who are subject to the amendments act and fail to comply after sufficient notification, the law is clear that NIH and other federal funders of clinical trials must then withhold further funding for the grant and any future grant to the grantee. In addition, the timely reporting of clinical trials will be taken into consideration during review of subsequent applications for funding.

The proposed changes to the regulations are currently in the public comment period, which will end in a few weeks, on February 19. After a review of the comments (and possible revisions), a final rule will likely be issued in a few months time. Once the rule goes into effect, it will be interesting to watch how this changes the research process for new NIH and FDA-regulated studies.

Previously: Shake up research rewards to improve accuracy, says Stanford’s John IoannidisRe-analyses of clinical trial results rare, but necessary, say Stanford researchersHow important is it to publish negative results?Researchers call for “democratization” of clinical trials data and A critical look at the difficulty of publishing “negative” results
Photo by U.S. Department of Defense

Big data, Cardiovascular Medicine, Chronic Disease, Research, Stanford News

Big data used to help identify patients at risk of deadly high-cholesterol disorder

Big data used to help identify patients at risk of deadly high-cholesterol disorder

Familial hypercholesterolemia is not exactly a catchy name. But Stanford cardiologist Josh Knowles, MD, is determined to make it easier to remember. This little known, high-cholesterol disease is a silent killer. If you don’t know you have it, it can strike suddenly – and years before most people ever start worrying about heart attacks.

Knowles and fellow researchers at Stanford have launched a new research project aimed at identifying people at-risk of having FH. Using “big data” research methods and software that “teaches” a computer how to recognize patterns, researchers plan to comb through electronic medical records at Stanford hospitals and, if successful, pinpoint those who might have the disease and not know it.

In a story I wrote on the new project, Knowles described how this innovative technology could potentially be used to transform health care:

Machine learning, in which computer algorithms learn to recognize patterns within data, is widely used by Internet businesses such as Amazon and Netflix to improve customer experience, get information about trends, identify likes and dislikes and target advertisements. These techniques have not been widely applied in medicine, but we believe that they offer the potential to transform health care, particularly with the increased reliance on electronic health records.

Using these methods to help identify patients with FH is a good place to start, Knowles said, since there are currently few systematic approaches to finding people with FH, and many doctors are unfamiliar with the disease. As he told me:

This disorder certainly leads to premature death in thousands of Americans each year … Less than 10 percent of cases are diagnosed, leaving an estimated 600,000 to 1 million people undiagnosed. If found early enough and treated aggressively with statin-based regimens, people can live longer, healthier lives.

The project is part of a larger initiative called FIND FH (Flag, Identify, Network, Deliver), a collaborative effort involving Stanford Medicine, Amgen Inc., and the nonprofit Familial Hypercholesterolemia Foundation to use innovative technologies to identify individuals with the disorder who are undiagnosed, untreated, or undertreated.

Previously: Registration for Big Data in Biomedicine conference now open, Hope for patients with familial hypercholesterolemia and Born with high cholesterol
Photo by Dwight Eschliman

Big data, In the News, Patient Care, Pediatrics, Stanford News

Examining the potential of big data to transform health care

Examining the potential of big data to transform health care

Updated 1-6-15: The piece also aired this week on NPR’s All Things Considered.

***

9-29-14: Back in 2011, rheumatologist Jennifer Frankovich, MD, and colleagues at Lucile Packard Children’s Hospital Stanford used aggregate patient data from electronic medical records in making a difficult and quick decision in the care of a 13-year-old girl with a rare disease.

Today on San Francisco’s KQED, Frankovich discusses the unusual case and the potential of big data to transform the practice of medicine. Stanford systems-medicine chief Atul Butte, MD, PhD, also weighed in on the topic in the segment by saying, “The idea here is [that] the scientific method itself is growing obsolete.” More from the piece:

Big data is more than medical records and environmental data, Butte says. It could (or already does) include the results of every clinical trial that’s ever been done, every lab test, Google search, tweet. The data from your fitBit.

Eventually, the challenge won’t be finding the data, it’ll be figuring out how to organize it all. “I think the computational side of this is, let’s try to connect everything to everything,” Butte says.

Frankovich agrees with Butte, noting that developing systems to accurately interpret genetic, medical or other health metrics is key if such practices are going to become the standard model of care.

Previously: How efforts to mine electronic health records influence clinical care, NIH Director: “Big Data should inspire us”, Chief technology officer of the United States to speak at Big Data in Biomedicine conference and A new view of patient data: Using electronic medical records to guide treatment

Big data, Events, Stanford News

Registration for Big Data in Biomedicine conference now open

Registration for Big Data in Biomedicine conference now open

big_data

Last spring, in a blog post on a study from Stanford systems-medicine chief Atul Butte, MD, PhD, National Institutes of Health Director Francis Collins, MD, PhD, noted that “we are at a point in history where big data should not intimidate, but inspire us.”

The theme of how large-scale data analysis and technology can inspire ways to improve disease prevention, diagnosis and treatment will take center stage at Stanford this spring for the annual Big Data in Biomedicine conference.  The event, which is co-sponsored by Stanford and Oxford University, will be held May 20-22 at the School of Medicine’s Li Ka Shing Center for Learning & Knowledge. Registration is now open on the website.

More than 40 speakers will participate in the conference, including Margaret Hamburg, MD, commissioner of the U.S. Food and Drug Administration; Nobel laureate and Stanford professor Michael Levitt, PhD; Peter Norvig, PhD, director of Research at Google; and 23andMe founder Anne Wojcicki. The three-day event will also include a technical showcase where attendees can browse displays and demos highlighting public and private companies’ innovations related to big data.

For those interested in viewing the keynotes and panel discussions from the 2014 conference, the videos are available to watch online.

Previously: Big Data approach identifies new stent drug that could help prevent heart attacks, Examining the potential of big data to transform health care, Stanford’s Big Data in Biomedicine chronicled in tweets, photos and videos and Videos of big data in biomedicine keynotes and panel discussions now available online
Photo, of 2014 conference speaker Colin Mahony, by Saul Bromberger

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