Tulsa, Okla. – As it is widely hoped that statistical models can improve decision-making related to medical treatments, and because the cost and scarcity of medical outcomes data can make testing data from patients in a different context prohibitive, researchers including Dr. Martin Paulus, Scientific Director and President at Laureate Institute for Brain Research (LIBR) in Tulsa, Okla., examined how well a machine learning model performed across several independent clinical trials of antipsychotic medicine for schizophrenia.
 
Researchers found models predicted patient outcomes with high accuracy within the trial in which the model was developed, but performed no better than chance when applied out-of-sample. Pooling data across trials to predict outcomes in the trial left out did not improve predictions. These results suggest that models predicting treatment outcomes in schizophrenia are highly context-dependent and may have limited generalizability.
 
The research findings, “Illusory Generalizability of Clinical Prediction Models,” were published in the January 11, 2024 edition of Science, the weekly journal of the American Association for the Advancement of Science (AAAS).
 
Researchers used treatment data from five international, multisite randomized controlled trials (RCTs) obtained through the YODA Project (https://yoda.yale.edu/), selected because of their comparability and consistency. All patients had a current DSM-IV diagnosis of schizophrenia at the start of the trial, randomized patients to an antipsychotic medication or placebo, and used the same scale to measure treatment outcomes (the Positive and Negative Syndrome Scale, PANSS). All trials also included a four-week timepoint to measure outcomes and collected similar data about the patients at baseline. The team applied machine learning methods using baseline data to predict whether a patient would achieve clinically significant improvement in symptoms over four weeks of anti-psychotic treatment. Further, the team evaluated the applicability of machine learning models across four distinct scenarios to gain insights into their generalizability: assessing within-trial performance without any external validation; employing within-trial cross-validation; assessing out-of-sample performance in a paired-trial validation; and assessing performance through an extension of the paired-trial validation with a leave-one-trial-out approach.
 
Results suggest that predictive models are fragile and that excellent performance in one clinical context is not a strong indicator of performance on future patients. Researchers from the team say this is “highly concerning” as most predictive studies today rely on internal samples for testing and validation. Further, researchers say the present study offers an “underwhelming but realistic picture of our current ability to develop truly useful predictive models” for schizophrenia treatment outcomes, and that “we should a priori remain skeptical” of any predictive model findings that lack an independent sample for validation.
 
The research team included first author Dr. Adam Chekroud, [email protected], Spring Health, Co-Founder and President, Yale University School of Medicine; Dr. Martin Paulus, [email protected], Laureate Institute for Brain Research; and collaborators from University of Cologne, Max-Planck-Gesellschaft, United Partners Consulting, LLC, Yale University School of Medicine, University Augsburg, and Yale University.
 
No funding source had any role in the study design, data collection, data analysis, data interpretation, writing, or submission of the report. All trials were originally funded by Janssen Research and Development.
 
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CONTACT: For more information about the project, contact Martin Paulus, M.D., at Laureate Institute for Brain Research at [email protected]. For press inquiries, contact: Aimee Tonquest Mehl, Kingmaker Public Relations at [email protected].
 
ABOUT LAUREATE INSTITUTE FOR BRAIN RESEARCH (LIBR)
Launched in 2009, the Laureate Institute for Brain Research (laureateinstitute.org) is home to a multidisciplinary team of scientists and clinical research staff who apply neuroimaging, generic, pharmacological and neuropsychological tools to investigate the biology of neuropsychiatric disorders. LIBR’s creation was supported by The William K. Warren Foundation for the purpose of conducting studies aimed at developing more effective treatments and/or prevention strategies for these disorders. The studies are led by scientists from diverse backgrounds, including physics, cognitive neuroscience, psychology, psychiatry, neurology, developmental neuroscience, computer science, and genetics.

Dr. Eric Garland - February 6, 2024

"Mindfulness-Oriented Recovery Enhancement: Clinical Outcomes and Neurophysiological Mechanisms of an Evidence-Based Treatment for Addiction and Chronic Pain"

William K. Warren, Jr.  Frontiers in Neuroscience Lecture 
12:00 pm - 1:00 pm     Program in the LPCH auditorium  

Dr. Eric Garland, PhD, LCSW is Distinguished Endowed Chair in Research, Distinguished Professor, and Associate Dean for Research in the University of Utah College of Social Work and Director of the Center on Mindfulness and Integrative Health Intervention Development (C-MIIND). Dr. Garland is the developer of an innovative mindfulness-based therapy founded on insights derived from neuroscience, called Mindfulness-Oriented Recovery Enhancement (MORE). He has published more than 230 scientific articles and received more than $80 million in research grants from the National Institutes of Health (NIH) and the Department of Defense (DOD) develop and test novel mindfulness-based treatments for addiction. In 2019 was appointed by NIH Director Dr. Francis Collins to the NIH HEAL Multi-Disciplinary Working Group comprised of national experts on pain and addiction research to help guide the nation’s $2 billion HEAL initiative to use science to halt the opioid crisis.  In addition to being a clinical researcher, Dr. Garland is a licensed psychotherapist and Distinguished Fellow of the National Academies of Practice, with more than 20 years of clinical experience treating addiction. In a recent bibliometric analysis of mindfulness research published over the past 55 years, Dr. Garland was found to be the most prolific author of mindfulness research in the world.
 
Learning objectives: 

1. Identify two cognitive, affective, and neurobiological mechanisms implicated in substance use disorders, affective disorders, and chronic pain.    
2. Explain two therapeutic techniques to reduce craving, regulate addictive behavior, decrease negative emotions, and/or manage chronic pain. 
3. Describe the clinical outcomes of MORE for chronic pain and opioid misuse.
   
   

For Physicians: Saint Francis Health System is accredited by the Oklahoma State Medical Association to provide continuing medical education for physicians.
Saint Francis Health System designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s)™.  Physicians should claim only the credit commensurate with the extent of their participation in the activity.
 
For Psychologists: The Oklahoma State Board of Examiners of Psychologists, the American Psychological Association and the Oklahoma Psychological Association recognize AMA PRA Category 1  credit™. Saint Francis Health System is accredited by the OSMA
 
For Social Workers: An application has been sent to the Oklahoma State Board of Licensed Social Workers for 1 hour Category 1 Clinical. 
 
For CADCs and LADCs Saint Francis Health System is accredited as a provider of continuing education programs for CADCs and LADCs through the Oklahoma Board of Licensed Alcohol and Drug Counselors.      (1 hour)
 
The LPC/LMFT This event as been approved by the State Board of Behavioral Health Licensure (BBHL) for 1 hour of CE.  

For questions , email:  Lauren Haguewood at [email protected]

Now that the initial fervor of New Year’s Resolutions has passed, you have plenty of time to consider what is most meaningful to you and develop goals that may truly fit with those values.

We talked with LIBR principal investigator Dr. Robin Aupperle about how to combat the tendency of people to abandon their January 1 resolutions—and instead use SMART goals to make lasting life changes.
 
Q: How are SMART goals different from something like a New Year’s resolution?
RA: Good question. There’s a tremendous push—internal and external—to start the New Year with a big, transformative resolution. For instance, I’ve heard resolutions like “I’m going to work out every single day this year,” “I’m going to save 50% of my salary,” or “I’m going to lose 50 pounds,” none of which are likely to be realistic. Some of these goals may also relate to what people feel they “should” do rather than necessarily being related to what is truly important for that individual.
 
Q: Those seem lofty, but if we don’t try, we’ll never do it, right?
RA: It can be very challenging to change our behaviors and ingrained habits. In order to be successful, the new behaviors have to be rewarding and reinforcing in some way. This feeling of reward can come from feeling successful and accomplished for being successful, or from engaging in something we value and enjoy.
 
Q: What’s a better approach?
RA: SMART goals offer a great framework for how to think about goal-setting. SMART is an acronym that helps identify and quantify elements of the goal you’re setting.
 
Q: How does that work?
RA: SMART stands for:
S          Specific         
What is the specific goal that someone wants to accomplish?
“I want to increase my stamina and endurance so that I feel healthier.”
M        Measurable   
What data will be used to measure the goal? How will I measure it?
“I will start by doing something that raises my heart rate for 20 minutes two times a week. My long-term goal will be to work towards 30 minutes, three times per week. I’ll make a calendar to chart my progress and keep myself accountable.”
A         Achievable    
Is the goal doable? Do you have the necessary skills and resources?
“I will walk around my neighborhood (around the office, or at the mall) after work on Mondays and Thursdays, inviting friends to join me.”
R          Relevant        
How does this goal align with your values? Why is the result important?
“I know I need to be less sedentary for my health. I want to be healthier in order to feel like I have more energy throughout my daily life and live longer.”
T          Time-Bound
​What’s the timeframe for accomplishing this goal, beginning to end?
“I will focus on 20 minutes, two times a week for two months and assess my progress and modify my goals as needed.”
 
Q: That’s a lot of work!
RA: Much of this relates to all the thoughts going on in our heads when we are considering behavior change. This just provides a framework for organizing these thoughts, and making them specific. The main idea though, is that any attempts at change offer helpful information. If you aren’t successful in your first attempts, the key is to not get down on yourself! Simply consider what obstacles got in the way and how you can modify your plan to be more effective. For example, this could include starting smaller and breaking things down – for example, starting with walking around the block ONE time, ONCE per week to begin with….and then working up from there. Any amount of successful change goes a long way towards motivating further change.
The University of California created this SMART goals template—it’s really good. You can use it to write your own SMART goals:
 
Initial Goal (Write the goal you have in mind):
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1. Specific (What do you want to accomplish? Who needs to be included? When do you want to do this? Why is this a goal?)
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2. Measurable (How can you measure progress and know if you’ve successfully met your goal?):
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3. Achievable (Do you have the skills required to achieve the goal? If not, can you obtain them? What is the motivation for this goal? Is the amount of effort required on par with what the goal will achieve?):
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4. Relevant (Why am I setting this goal now? Is it aligned with overall values?):
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5. Time-bound (What’s the deadline and is it realistic?):
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S.M.A.R.T. Goal (Review what you have written, and craft a new goal statement based on what the answers to the questions above have revealed):
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Q: Do you have any SMART goals you’re working on?
RA: I am working towards building strength by focusing on lifting weights when I am at the gym. My initial goal is to go twice per week for 30 minutes each time. I am going to re-assess my goals in two months to consider if I should modify my goal. This is important to me because I want to be stronger to keep up with my kids, who are very into ninja warrior right now! I also have non-health related goals, such as writing a children’s book, which I have broken down into several steps needed to work towards that goal.
 
Dr. Aupperle has initiated research projects at LIBR investigating neurocognitive and behavioral predictors of treatment response to behavioral activation therapy for depression and exposure therapy for anxiety.  In addition, she is taking the lead in LIBR projects investigating predictors of success for females enrolled in a criminal diversion program and factors related to mental health resiliency in college students.