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

Abstract

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 ...