Congratulations to Carol Friedman, who was just elected to the National Academy of Medicine, formerly known as the Institute of Medicine. This is a big deal, and it reflects Carol’s substantial contribution to the field of biomedical informatics as one of the key figures to bring natural language processing to health care as well as her recent work in pharmacovigilance.
Congratulations to Matt Levine and his advisors David Albers and Lena Mamykina for winning best poster in the Columbia Data Science Student Challenge on glucose prediction!
Congratulations to George Hripcsak and collaborators on receiving a grant from the NIH for approximately $4 million in fiscal year 2016 to enroll participants in the Cohort Program of President Barack Obama’s Precision Medicine Initiative – a large-scale research effort to improve our ability to prevent and treat disease based on individual differences in lifestyle, environment and genetics. The five-year award is estimated to total $46.5 million, pending progress reviews and availability of funds.
New York, NY (June 6, 2016)—An international observational study led by Columbia University researchers has uncovered widespread differences in the treatment of patients with common chronic diseases, including type 2 diabetes, hypertension, and depression. Using data from 250 million patient records in four countries, the study demonstrates the feasibility of performing large-scale observational research to obtain information about clinical practice among diverse groups of patients.
Findings from the study, performed in collaboration with the Observational Health Data Sciences and Informatics (OHDSI) program, were published online today in Proceedings of the National Academy of Sciences (PNAS).
The study revealed that the vast majority of patients with diabetes worldwide are initially treated with the medication metformin, although there is wide variation in what second-line treatments are given. In contrast, the study found significant variation in first-line treatment of hypertension and even greater differences in the initial treatment of depression. One surprising finding was that 10 percent of diabetes patients, 11 percent of depression patients, and 24 percent of hypertension patients followed a treatment pathway that was shared with no one else in the study.
“We found that while the world is moving towards more consistent therapy over time for the three diseases, there remain significant differences in how they are treated,” said first author George Hripcsak, MD, MS, the Vivian Beaumont Allen Professor and chair of Biomedical Informatics at Columbia University Medical Center (CUMC), principal investigator of the OHDSI coordinating center and director of Medical Informatics Services at NewYork-Presbyterian/CUMC. “This suggests that randomized clinical trials—the gold standard in evaluating new therapies—may not capture enough of the information needed to make their results more broadly generalizable to different populations.”
Observational research, in which patterns of care are gleaned from large data sets—such as electronic health records, insurance claims, and pharmacy records—have the potential to offer insight into real-world treatment scenarios that may inform clinical trial design and, ultimately, clinical practice. But analyzing data from a variety of sources is often hindered by disparate models for collecting and storing patient records.
To surmount these hurdles, an international group of scientists formed the OHDSI (pronounced ‘odyssey’) program, which allows researchers to combine and analyze patient data from widely different sources in the US and abroad. Columbia University serves as OHDSI’s coordinating center. Currently, the research collaborative involves more than 600 million patient records from 14 countries.
“Modern randomized trials are currently carried out without a clear view of how current treatments are used,” said study leader David Madigan, PhD, executive vice president and dean of the Faculty of Arts and Sciences, professor of statistics at Columbia University, and co-principal investigator of the OHDSI coordinating center. “In the future, before a randomized trial is started, an observational study like ours could be mandatory to determine the appropriate sample size and composition of control groups, among other factors.”
The study relied on the OHDSI distributed data network, in which researchers from around the world convert patient-level data to a standardized model that can run a common analysis protocol. Investigators from the 11 research sites participating in the study shared the final, aggregate results, although individual data were excluded to protect patient privacy. Seven of the research sites had completed their analyses within just three weeks of beginning the study.
“While the findings are quite interesting, the important point is that we’ve shown that large-scale observational research across widely different databases is feasible,” said Jon Duke, MD, director of the Drug Safety Informatics Lab and research scientist at the Regenstrief Institute. “And it can be done in a very short amount of time.”
Future OHDSI studies will focus on medical product safety surveillance, comparative effectiveness research (making direct comparisons between therapies), patient-level predictive modeling, and other topics. A worldwide request for proposals is planned, in which researchers, citizen scientists, and high school students may propose research questions to be run on the OHDSI network.
“The creation of such a network is a great opportunity, not only to characterize what treatments are actually being used, but also to attempt to identify what treatments are potentially better,” said Nigam Shah, MBBS, PhD, associate professor of medicine at Stanford University. “For example, from the wide variation in second-line treatments for diabetes, we can attempt to identify those that are more effective. OHDSI puts us on a path to creating personalized evidence, which is a form of precision medicine.”
Dr. Hripcsak and Dr. Madigan are also with the Data Science Institute at Columbia University.
The study is titled, “Characterizing treatment pathways at scale using the OHDSI network.” The other contributors are: Patrick Ryan, (Columbia University Medical Center, New York, NY, and Janssen Research and Development, Titusville, NJ), Jon Duke (Regenstrief Institute, Indianapolis, IN), Nigam H. Shah (Stanford University, Palo Alto, CA), Rae Woong Park (Ajou University School of Medicine, Suwon, South Korea), Vojtech Huser (National Institutes of Health, Bethesda, MD), Marc A. Suchard (University of California, Los Angeles, CA), Martijn Schuemie (Janssen Research and Development), Frank DeFalco (Janssen Research and Development), Adler Perotte (Columbia University Medical Center), Juan Banda (Stanford University), Christian Reich (IMS Health, Burlington, MA), Lisa Schilling (University of Colorado School of Medicine, Aurora, CO), Michael Matheny (Vanderbilt University Medical Center, Nashville, TN), Daniella Meeker (University of Southern California, Los Angeles, CA), and Nicole Pratt (University of South Australia, Adelaide, South Australia).
The study was funded in part by grants from the National Library of Medicine (R01 LM006910, R01 LM011369), National Institute of General Medical Sciences (R01 GM101430), National Science Foundation (NSF IIS 1251151), and the Smart Family Foundation. Infrastructure to carry out the project was funded in part by Janssen Research and Development, AstraZeneca, and Takeda Pharmaceuticals International.
The authors declare no other conflicts of interest.
Columbia University Medical Center provides international leadership in basic, preclinical, and clinical research; medical and health sciences education; and patient care. The medical center trains future leaders and includes the dedicated work of many physicians, scientists, public health professionals, dentists, and nurses at the College of Physicians and Surgeons, the Mailman School of Public Health, the College of Dental Medicine, the School of Nursing, the biomedical departments of the Graduate School of Arts and Sciences, and allied research centers and institutions. Columbia University Medical Center is home to the largest medical research enterprise in New York City and State and one of the largest faculty medical practices in the Northeast. For more information, visit cumc.columbia.edu or columbiadoctors.org.
Virginia Lorenzi, Associate in Biomedical Informatics – Columbia University and Manager, HIT Standards and Collaborations – NewYork-Presbyterian Hospital has been elected co-chair of the HL7 standard organization’s Education Work Group. This group ensures the quality and availability of education and learning deliverables provided by HL7 International and nurtures a community of HL7 standard educators. Ms. Lorenzi has been active in the standards world for a very long time, and this new responsibility reflects her tireless championing of information exchange standards in health care.
Data scientists create world’s first therapeutic venom database
VenomKB, short for Venom Knowledge Base, summarizes the results of 5,117 studies in the medical literature describing the use of venom toxins as painkillers and as treatments for diseases like cancer, diabetes, obesity, and heart failure. Credit: Tatonetti lab and Columbia University Medical Center/Data Science Institute
Distinguished poster award:
Influences, Barriers, and Motivations for Healthy Behaviors Among Pediatric Cancer Patients: A Focus Group Approach
M. Chau, E. Ladas, L. Mamykina, Columbia University
Distinguished paper award:
Simulation-based Evaluation of the Generalizability Index for Study Traits
Z. He, P. Chandar, Columbia University; P. Ryan, Columbia University/Janssen Research and Development; C. Weng, Columbia University