Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy

Glanz, Jason M.; Narwaney, Komal J.; Mueller, Shane R.; Gardner, Edward M.; Calcaterra, Susan L.; Xu, Stanley; Breslin, Kristin; Binswanger, Ingrid A.

doi:10.1007/s11606-017-4288-3

Cited by 77 publications

(84 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of the chronic conditions in Table 3, e.g. heart disease (circulatory system), psoriasis (integumentary system), and BMI/obesity (nutrition) also appear in the CDC opioid prescribing guidelines [9] or have extensive literature linking them to increased risk for long-term pain, either intrinsic to the condition or due to needed medical procedures that are more likely to expose patients to opioids [14]. For example, numerous papers show a link between increased body-mass index (BMI) and increased pain intensity and duration (with anti-correlations between BMI and pain recovery) [33], and obesity has also been associated with higher initial opioid doses [25].…”

Section: Interpretation Of the Opioid Enhanced Effect Subgroupmentioning

confidence: 99%

“…Another expected finding was that individuals with psychological comorbidities (mental health conditions) also have high probability of belonging to the enhanced response group, with individuals participating in psychotherapy having reduced risk of addiction (psychotherapy was among the lowest-scored features and hence not shown in the table). Substantial research has already linked mental illness with opioid misuse [14,39,47]. Although adjustment reaction (psychology) appears with a lower score in Table 3, it encompasses reactions to trauma, episodic emotional disorders, and chronic anxiety that have been shown to be comorbid with many of the chronic diagnoses and pain conditions discussed above [14,39,47] and have also been linked with increased opioid dosages [16].…”

Section: Interpretation Of the Opioid Enhanced Effect Subgroupmentioning

confidence: 99%

“…Substantial research has already linked mental illness with opioid misuse [14,39,47]. Although adjustment reaction (psychology) appears with a lower score in Table 3, it encompasses reactions to trauma, episodic emotional disorders, and chronic anxiety that have been shown to be comorbid with many of the chronic diagnoses and pain conditions discussed above [14,39,47] and have also been linked with increased opioid dosages [16]. Similarly, nondependent abuse of drugs also has a lower score but it is well known that opioid dependence and addiction are associated with polysubstance use and abuse [38,44].…”

Section: Interpretation Of the Opioid Enhanced Effect Subgroupmentioning

confidence: 99%

“…As is standard in EM, let us define Q(Θ, Θ l ) as the expected value of the complete-data log-likelihood (14) with respect to the conditional distribution of the latent variable given the current parameters Θ l :…”

Section: E-stepmentioning

confidence: 99%

See 3 more Smart Citations

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

Nagpal

Wei²,

Vinzamuri³

et al. 2020

Proceedings of the ACM Conference on Health, Inference, and Learning

View full text Add to dashboard Cite

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision support. KEYWORDScausal treatment effect, heterogeneous treatment effect, Bayesian networks, decision support ACM Reference Format:

show abstract

Section: Interpretation Of the Opioid Enhanced Effect Subgroupmentioning

confidence: 99%

Section: Interpretation Of the Opioid Enhanced Effect Subgroupmentioning

confidence: 99%

Section: Interpretation Of the Opioid Enhanced Effect Subgroupmentioning

confidence: 99%

Section: E-stepmentioning

confidence: 99%

See 2 more Smart Citations

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

Nagpal

Wei²,

Vinzamuri³

et al. 2020

Proceedings of the ACM Conference on Health, Inference, and Learning

View full text Add to dashboard Cite

show abstract

“…1 Without question, the study and its findings should be considered foundational for any future efforts aiming to refine a clinically actionable prediction model. However, whether the study's findings on their own should drive changes in treatment recommendations and policy, particularly with regard to naloxone distribution, is less clear.…”

mentioning

confidence: 99%

Important Steps Towards a Clinically Actionable Predictive Model of Opioid Overdose Among Patients on Long-term Opioid Therapy

Becker

2018

J GEN INTERN MED

View full text Add to dashboard Cite

G lanz et al. took on a very ambitious and important project-developing and validating a prediction model for opioid overdose among patients on long-term opioid therapy (LTOT)-and conducted a rigorous study that offers provocative results.1 Without question, the study and its findings should be considered foundational for any future efforts aiming to refine a clinically actionable prediction model. However, whether the study's findings on their own should drive changes in treatment recommendations and policy, particularly with regard to naloxone distribution, is less clear.First, the authors conducted a well-designed, well-executed, and clearly described study. The rationale for the use of predictive modeling in clinical risk scenarios was compelling, and the clear justifications and explanations for various design decisions are exemplary and worth emulating by others in the field. The development of a model that has five components that are typically readily available in a clinical encounter is also a welcome advance.The potential for immediate clinical application is not as certain. Some of the model's operating characteristics should give readers pause; to their credit, the authors highlight these issues. For example, while sensitivity of the model (i.e., proportion of those who overdosed that was predicted by the model) was 82%, the positive predictive value (PPV)-was low, ranging from 0.56 to 1.8% in the derivation and validation cohorts, respectively. This is especially concerning because PPV is more well-aligned with how predictive models are best used in clinical practice. In this case, for every 1000 patients that the model predicted were at high risk (i.e., had a positive test), only about 10 experienced an overdose. Positive predictive value varies as a function of prevalence, and the low PPV is in part due to the fact that overdoses, even in this epidemic era, are thankfully relatively rare events.The authors predicated the importance of a clinically actionable predictive model on its value in helping LTOT prescribers decide who should receive the overdose reversal drug naloxone, an opioid receptor antagonist. There is no cost effectiveness data (or randomized controlled trial data) to date on the use of naloxone in primary care populations prescribed LTOT. It may be an over-reach to recommend applying this predictive model to determine whether to prescribe a $150-$4500 treatment that will not be needed~99% of the time and can be administered by emergency medical responders who should be called in any case. That said, the larger issue here is not the authors' potentially over-valuing naloxone among patients on LTOT for chronic pain; it is the unethically exorbitant cost of naloxone.2 In systems where naloxone costs are controlled (and dramatically lower), it is an easier clinical and policy decision to give naloxone's effectiveness data in this population the benefit of the doubt. 3 The predictive model may be at least as (or more) valuable in helping LTOT prescribers determine, for example, in whom ...

show abstract

Suggested Paths to Fixing the Opioid Crisis

Samet

Kertesz

2018

JAMA Netw Open

View full text Add to dashboard Cite

Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy

Cited by 77 publications

References 39 publications

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

Important Steps Towards a Clinically Actionable Predictive Model of Opioid Overdose Among Patients on Long-term Opioid Therapy

Suggested Paths to Fixing the Opioid Crisis

Contact Info

Product

Resources

About