Authors: Jeffrey Gudin, Seferina Mavroudi, Aigli Korfiati, Nikos Iliopoulos, Derek Dietze, Peter Hurwitz.
Purpose: Topical analgesics have gained acceptance in guidelines for the treatment of pain. The Kailo Pain Patch® is a topically applied analgesic adhesive patch, with a recent study showing reduced pain severity and interference scales in comparison to a control group. However, as with any analgesic modality, treatment response is variable. Advances in technology, such as pharmacogenomic evaluation and machine learning (artificial intelligence) have emerged as tools to assist clinicians with selecting the most suitable treatments for a variety of disease states. There is limited data on the use of these technologies for pain management; only limited studies have applied machine learning to personalize the treatment of chronic pain patients. This report analyzed the PREVENT Study using an existing modified interpretable machine learning method to personalize the selection of the most suitable protocol for use of the Kailo Pain Patch® and other topical analgesics.
Patients and methods: Data from the IRB-approved observational PREVENT study were used in the present analysis of 128 (89 females,39 males) chronic pain patients and 20 controls answering the Brief Pain (BPI) questionnaire along with additional questions in the baseline and after 30 days of treatment with the Kailo Pain Patch®. An interpretable machine-learning model was used to build pain outcome prediction models. This method is a multi-objective ensemble classification/regression technique, which combines multi-objective evolutionary algorithms with Support Vector Machines, Random Forests, and feature filtering techniques to optimize the classification model and minimize the utilized feature subset. Three basic endpoints were examined as outputs to the prediction models including Total BPI Severity, Total BPI Interference, and Total medication changes in the follow- up period. Both classification and regression models were constructed for these endpoints and a “leave-one-out” cross-validation strategy was used to evaluate the generalization ability, classification, and regression performance of the deployed models.
Results: Experimental results showed that the trained models with the proposed machine learning method were able to predict endpoints with extremely high accuracy, with the AUC exceeding 90% and Spearman correlation metric exceeding 0.4 for all endpoints, overcoming the classification and regression performances of other benchmark models, including the recently introduced XGBoost. The interpretable machine learning method was able to reduce the number of significant features to 15 and was able to identify some of the most important characteristics of responders and non-responders allowing for a personalized approach to creating an individualized pain treatment approach. Applying the trained model in a previous IRB-approved Observational Study (OPERA) dataset (631 chronic pain patients) demonstrated that most of the participants (>70%) who did not benefit from other topical analgesics therapies, as well as more than 50% of responders to OPERA study medications, would have noted improvement from the pain patch studied in PREVENT.
Conclusions: Artificial intelligence and machine learning technologies are advancing multiple areas in fields of medicine, including pain management. A model has been developed which continues to be refined; here we show use of that model for predicting response to topical analgesic therapies. We will continue to refine these tools and make them available to front-line clinicians through a user-friendly web interface (https://kailo.insybio.com/) that can be used to support analgesic clinical decision making [15 questions].