Machine Learning Applied to Clinical Laboratory Data Predicts Patient-Specific, Near-Term Relapse in Patients in Medication for Opioid Use Disorder Treatment

Abstract

We have developed a data-driven, algorithmic method for identifying patients in an outpatient buprenorphine program at high risk for relapse in the following seven days. This method uses data already available in clinical laboratory data, can be made available in a timely matter, and is easily understandable and actionable by clinicians. Use of this method could significantly reduce the rate of relapse in addiction treatment programs by targeting interventions at those patients most at risk for near term relapse.

Using Machine Learning to Predict Treatment Adherence in Patients on Medication for Opioid Use Disorder

“…Therefore, it is difficult to compare our model performance directly with that of other models. The study most similar to ours used urine drug data combined with basic demographic data from 139 OUD patients in a hospital-based outpatient buprenorphine program to predict relapse risk after 4 weeks of urine-validated opioid abstinence 20 . That model achieved overall accuracy of 89.9%, sensitivity of 0.65, specificity of 0.94, and a positive predictive value of 0.65.…”

“…The study most similar to ours used urine drug data combined with basic demographic data from 139 OUD patients in a hospital-based outpatient buprenorphine program to predict relapse risk after 4 weeks of urine-validated opioid abstinence. 20 That model achieved overall accuracy of 89.9%, sensitivity of 0.65, specificity of 0.94, and a positive predictive value of 0.65. Our models were lower in specificity and slightly lower in accuracy, but higher in sensitivity and positive predictive value.…”

“…Risk-prediction models have been widely implemented in other health fields (eg, cardiovascular disease prognostic and diagnostic models: Khera et al 16 and Wessler et al 17 ); however, to our knowledge, only 3 risk models have been developed to predict MOUD outcomes. [18][19][20] Furthermore, these models focus only on predicting return to use or treatment discharge/retention in specific subpopulations (eg, patients with a prolonged period of opioid abstinence, first MOUD treatment episode, or patients of Hispanic/Latino ethnicity). The modal pattern of MOUD outcomes, at least in the first few months of MOUD, is neither abstinence or full-blown return to use, but rather an intermediate state of sporadic use, which may or may not be a harbinger of unwanted outcomes.…”

“…One logical way to forecast a patient's future status is by developing a statistical or mathematical risk-prediction model. Risk-prediction models have been widely implemented in other health fields (eg, cardiovascular disease prognostic and diagnostic models: Khera et al 16 and Wessler et al 17 ); however, to our knowledge, only 3 risk models have been developed to predict MOUD outcomes 18–20 . Furthermore, these models focus only on predicting return to use or treatment discharge/retention in specific subpopulations (eg, patients with a prolonged period of opioid abstinence, first MOUD treatment episode, or patients of Hispanic/Latino ethnicity).…”