Reducing Opioid Prescriptions by Identifying Responders on Topical Analgesic Treatment Using an Individualized Medicine and Predictive Analytics Approach

Jeffrey Gudin; Seferina Mavroudi; Aigli Korfiati; Konstantinos Theofilatos; Derek Dietze; Peter Hurwitz

doi:10.2147/JPR.S246503

Reducing Opioid Prescriptions by Identifying Responders on Topical Analgesic Treatment Using an Individualized Medicine and Predictive Analytics Approach

J Pain Res. 2020 May 28:13:1255-1266. doi: 10.2147/JPR.S246503. eCollection 2020.

Authors

Jeffrey Gudin¹, Seferina Mavroudi^{2

3}, Aigli Korfiati³, Konstantinos Theofilatos³, Derek Dietze⁴, Peter Hurwitz⁵

Affiliations

¹ Rutgers New Jersey Medical School, Newark, NJ, USA.
² Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, Pátrai, Greece.
³ InSyBio Ltd, Winchester, UK.
⁴ Metrics for Learning LLC, Queen Creek, Arizona, USA.
⁵ Clarity Science LLC, Narragansett, Rhode Island, USA.

Abstract

Purpose: Chronic pain is a life changing condition, and non-opioid treatments have been lately introduced to overcome the addictive nature of opioid therapies and their side effects. In the present study, we explore the potential of machine learning methods to discriminate chronic pain patients into ones who will benefit from such a treatment and ones who will not, aiming to personalize their treatment.

Patients and methods: In the current study, data from the OPERA study were used, with 631 chronic pain patients answering the Brief Pain Inventory (BPI) validated questionnaire along with supplemental questions before and after a follow-up period. A novel machine learning approach combining multi-objective optimization and support vector regression was used to build prediction models which can predict, using responses in the baseline, the four different outcomes of the study: total drugs change, total interference change, total severity change, and total complaints change. Data were split to training (504 patients) and testing (127 patients) sets and all results are measured on the independent test set.

Results: The machine learning models extracted in the present study significantly overcame other state of the art machine learning methods which were deployed for comparative purposes. The experimental results indicated that the machine learning models can predict the outcomes of this study with considerably high accuracy (AUC 73.8-87.2%) and this allowed their incorporation in a decision support system for the selection of the treatment of chronic pain patients.

Conclusion: Results of this study revealed the potential of machine learning for an individualized medicine application for chronic pain therapies. Topical analgesics treatment were proven to be, in general, beneficial but carefully selecting with the suggested individualized medicine decision support system was able to decrease by approximately 10% the patients which would have been subscribed with topical analgesics without having benefits from it.

Keywords: individualized medicine; machine learning; multi-objective optimization; non-opioid treatment; pain therapy; predictive analytics; regression; support vector regression.