Opioid disorder treatment: first three weeks forecast success
NEW YORK, NY--A newly developed prediction model may be able to calculate the risk of opioid relapse among individuals in the early stages of medication treatment—as early as three weeks into therapy.
“Medication treatment for opioid use disorder, contrary to popular belief, is very effective and likely to succeed if patients achieve early treatment success,” says Sean X. Luo, MD, PhD, assistant professor of psychiatry at Columbia University Vagelos College of Physicians and Surgeons, who developed the model with Daniel Feaster, PhD, of the University of Miami.
The model, based on data from 2,199 adults enrolled in clinical trials of opioid use medications, estimates in the first few weeks of treatment the likelihood that a patient will return to using opioids before the end of a 12-week treatment program.
More specifically, for patients prescribed buprenorphine, increasing dose of an oral formulation or switching to an extended-release injection formulation could be considered quickly. Physicians should also evaluate patients with high risk of relapse for other factors that may need attention, such as co-occurring psychiatric disorders.
To disseminate the tools developed from this project, the team built a web portal (www.oudriskscore.org) that allows clinicians to estimate the risk of relapse of their patients.
What’s next
Although physicians may modify treatment for patients with a high risk of relapse, no studies have been conducted to determine the optimal strategies. “We need new clinical trials that test different treatment modifications among high-risk individuals,” Luo says.
More long-term follow-up data also are needed to estimate timing and probability of relapse beyond the 12-week treatment phase.
The need
The medications methadone, buprenorphine, and extended-release injection naltrexone are effective for many patients, but many patients return to opioid use during the 12-week treatment program.
When treating chronic conditions, physicians often use risk scores to predict the likelihood of future health events and use those scores to guide treatment. But no such risk score is available for opioid use disorder treatment.
“Once a patient relapses and drops out of medication treatment, they are in danger, including risk of overdose, and can be hard to locate and re-engage,” says Columbia psychiatrist Edward Nunes, MD, who co-led the new study. “If physicians know in the first few weeks of treatment who is in danger, they can respond early and hopefully head off trouble.”
Using machine learning to build a predictor
To build a predictor that estimates a patient’s risk of returning to drug use, the researchers applied machine learning techniques to data from previous clinical trials that tested three medications for opioid use disorder. A method called LASSO automatically constructed predictive models using the most informative patient characteristics. The models were then tested on an independent validation subset of the harmonized dataset to assess for model performance.
Using patient data available at the start of treatment, the best model had a performance of around 70%. Model performance improved substantially when results of urine drug tests in the first three weeks of treatment were incorporated.
When the urine drug tests results are included, the model predicts that patients with no positive or missed drug tests in the first three weeks have a 13% risk of returning to use, whereas those with three positive or missed tests have an 85% risk of return to use.
Why it matters
“Our model now gives clinicians a way to quantify a patient’s risk of relapse early on in treatment and treatment modifications can be considered,” Luo says. “Medication doses can be increased and more frequent monitoring and psychotherapy options introduced for higher risk patients.”
More information
The study, titled "Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment," was published in JAMA Psychiatry on October 4.
Sean X. Luo, MD, PhD, is assistant professor of psychiatry at Columbia University Vagelos College of Physicians and Surgeons Luo and treats patients with addictions in his private practice.
All study authors: Sean X. Luo (Columbia), Daniel J. Feaster (University of Miami), Ying Liu (Columbia), Raymond R. Balise (Miami), Mei-Chen Hu (Columbia), Layla Bouzoubaa (Miami), Gabriel J. Odom (Florida International University), Laura Brandt (City College of New York), Yue Pan (Miami), Yih-Ing Hser (University of California, Los Angeles), Paul VanVeldhuisen (Emmes Company), Felipe Castillo (Columbia), Anna R. Calderon (Miami), John Rotrosen (New York University), Andrew J. Saxon (University of Washington), Roger D. Weiss (Harvard University), Melanie Wall (Columbia), and Edward V. Nunes (Columbia).
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
People
ARTICLE TITLE
Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment
COI STATEMENT
Edward Nunes has served as a consultant without compensation for Indivior, Camurus, and Pear Therapeutics and has received in-kind donations of medication or digital therapeutics from Alkermes, Braeburn-Camurus, Indivior, Pear Therapeutics, and CHESS Health. No other disclosures from Columbia authors were reported.
Stigma felt by opioid-dependent moms impacts the health care received by their babies
Findings from University of Missouri study can help emphasize messages of compassion and support toward opioid-dependent mothers
IMAGE:
PREGNANT WOMAN
view moreCREDIT: UNIVERSITY OF MISSOURI
COLUMBIA, Mo. -- The rate of opioid use among pregnant women in the United States quadrupled between 1999 and 2014 and continues to rise — an alarming trend that researchers from the University of Missouri and University of Iowa say has exposed the stigma felt by opioid-dependent mothers and how their shame has negatively impacted the health care received by their infants.
Jamie Morton led a study, which was a metasynthesis of existing literature on the topic, as a doctoral student at the MU Sinclair School of Nursing. She said the findings can help ensure health care providers, family members, friends and community members emphasize messages of support and compassion toward opioid-dependent mothers during the perinatal stage, which in this study was defined as one year prior to conception, pregnancy, and up to 18-24 months postpartum.
“We found that because the mothers were made to feel badly about themselves, they would withdraw themselves from receiving health care,” Morton said.
Morton added that since the mothers were often not getting any emotional or social support from health care providers, family, friends or community members, they felt self-blame and would internalize the stigma.
“What was surprising was the stigma was also transferred to the baby, a term known as associative stigma. The moms felt their infants were not given the same level of care or were treated differently,” Morton said. “The moms would just withdraw from even receiving health care in the first place in an effort to protect their child from being stigmatized, so they were less likely to take their baby to the pediatrician and less likely to take advantage of developmental services for their baby.”
Morton explained that this sometimes led to the mothers being referred to as “noncompliant” or “bad mothers.”
The researchers analyzed and synthesized 18 qualitative studies involving women of childbearing age in the U.S. who expressed feeling stigmatized due to their opioid dependence during the perinatal stage, as well as how the stigma impacted the health care they received for themselves and their babies.
“How often do we hear the phrase ‘the apple doesn’t fall far from the tree’?” Morton said. “Personally, I think everyone should be given a chance and treated with the same kindness and compassion as anyone else.”
Morton formerly worked in a newborn nursery and remembers mothers that would be very withdrawn.
“I made it a point to just treat them as any other mom, because all moms deserve our support as nurses and health care providers,” Morton said. “We just need to promote the opportunity for them to still be at the center of their health care decisions regarding both them and their babies as well as promoting their role as a mom.”
The research could potentially lead to more formalized education that could include trauma-informed care, topics such as trust, active listening, unconscious bias, and not judging based off assumptions. This could result in opioid-dependent mothers feeling more comfortable accessing care for themselves, taking their baby to the pediatrician or taking advantage of developmental services for their baby. This would ultimately improve both the mom’s and the baby’s long-term health outcomes.
“This expands way beyond nurses and health care providers, who, in general, do a great job of showing support, kindness, and compassion to the patients they serve,” Morton said. “This expands to the importance of family, friends, community members and the general public showing support, kindness and compassion. It is an honor and privilege to elevate the voices of these vulnerable women because their voices are not typically heard, but they need to be so we can meet their health care needs.”
“Stigma experienced by perinatal women with opioid dependency in the United States: A qualitative metasynthesis” was recently published in the Western Journal of Nursing Research. Funding was provided by the National Institutes of Health through a training grant awarded to the MU Sinclair School of Nursing.
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JOURNAL
Western Journal of Nursing Research
METHOD OF RESEARCH
Literature review
SUBJECT OF RESEARCH
People
ARTICLE TITLE
Stigma experienced by perinatal women with opioid dependency in the United States: A qualitative metasynthesis
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