December 18, 2019: NIH Collaboratory Shares New Findings and Fresh Insights in 2019

NIH Collaboratory researchers in 2019 continued to generate new knowledge and research methods in pragmatic clinical trials. Their work included insights from the Coordinating Center and Core Working Groups, large-scale analyses of data from the NIH Collaboratory Distributed Research Network, and results and innovative methodological approaches from the Demonstration Projects.

So far this year, the NIH Collaboratory has produced nearly 3 dozen articles in the peer-reviewed literature, including the primary results of the ABATE Infection trial, confirmation by the TiME trial of the feasibility of embedding large pragmatic trials in clinical care, and more:

NIH Collaboratory Coordinating Center

NIH Collaboratory Distributed Research Network

ABATE Infection Demonstration Project

EMBED Demonstration Project

PPACT Demonstration Project

PRIM-ER Demonstration Project

PROVEN Demonstration Project

SPOT Demonstration Project

STOP CRC Demonstration Project

TiME Demonstration Project

TSOS Demonstration Project

June 3, 2019: SPOT Illustrates Use of Real-World Health System Data in Designing Embedded Pragmatic Clinical Trials

An important advantage of embedding pragmatic clinical trials within health care systems is the availability of detailed clinical data on potential participants during trial design. These data can be used to determine eligibility criteria, predict changes in participant characteristics over time, and inform sample size calculations and other design features.

Investigators from the Suicide Prevention Outreach Trial (SPOT), an NIH Collaboratory Demonstration Project, recently shared their experiences with using electronic health record data on patients in the participating health systems to inform trial design. The article was published in Clinical Trials.

SPOT was designed to compare the effect of 2 outreach interventions and usual care on the rate of fatal and nonfatal suicide attempts in 3 large health care delivery systems. The investigators used historical data from the electronic health records of the participating health systems to select eligibility requirements, estimate the distribution of patient characteristics during the trial, and calculate statistical power and sample size. Their experiences offer lessons for others who are designing pragmatic trials embedded in health systems with automated data sources.

SPOT was supported within the NIH Collaboratory by a cooperative agreement from the National Institute of Mental Health and received logistical and technical support from the NIH Collaboratory Coordinating Center. Download a study snapshot of SPOT, and learn more about the NIH Collaboratory Demonstration Projects.

May 8, 2019: Dr. Greg Simon Receives National Suicide Prevention Award

At the Lifesavers Gala in New York last night, Dr. Greg Simon received the American Foundation for Suicide Prevention (AFSP’s) Research Award for his contributions to suicide prevention. Dr. Simon leads the Suicide Prevention Outreach Trial (SPOT), an NIH Collaboratory Demonstration Project that builds on previous work demonstrating that patients who answer “yes” to thoughts of self-harm on routinely administered PHQ-9 questionnaires at primary care visits are more likely to attempt suicide. For these high-risk patients, SPOT explores different modes of outreach (care management or online skills training versus usual care) to prevent suicide.

“There’s a conspiracy of silence around suicidal thoughts, because it’s awkward to discuss. So we’ve found that we have to incorporate talking about it into our standard care. Our suicide prevention work is a great example of how research and care keep influencing each other to improve our patients’ health. When research springs from clinicians’ and patients’ questions, ‘learning health systems’ can put results into practice much faster than the oft-cited 17-year lag.” — Dr. Greg Simon, from the Kaiser Permanente Washington Health Research Institute Press Release

Dr. Simon and his colleagues are also studying how machine-learning models can be used to predict risk of suicide. The models combine the PHQ-9 mental health questionnaire responses with information from electronic health records, including prior suicide attempts and mental health and substance use diagnoses. In a blog post regarding his research (and recent publication) on machine learning, Dr. Simon compares machine learning to warning lights on cars:

Our paper prompted many questions from clinicians and health system leaders about the practical utility of risk predictions:

“Are machine learning algorithms accurate enough to replace clinicians’ judgment?” our clinical partners asked.

“No,” I answered, “but they are accurate enough to direct clinicians’ attention.”

The AFSP also honored four others, including Anderson Cooper, a CNN and 60-minutes correspondent, and Kate Snow, an NBC news correspondent, for their work raising public awareness of suicide prevention.

Read more about what inspired Dr. Simon to study mental health.