Predicting placebo analgesia in patients with chronic pain using natural language processing: a preliminary...

Area of Science:

• Neuroscience
• Psychology
• Medical Informatics

Background:

• Chronic pain patients exhibit significant placebo effects in clinical trials.
• Predicting placebo response remains a challenge in pain management.
• Previous research suggests patient language post-treatment correlates with placebo response.

Purpose of the Study:

• To determine if pre-treatment language characteristics predict placebo response.
• To develop and validate a language-based model for placebo response prediction.
• To assess if the model can differentiate placebo responders from drug responders.

Main Methods:

• A language model was fine-tuned on data from a longitudinal study (Study 1) of chronic back pain patients receiving placebo.
• The model's predictive capability was validated on an independent dataset (Study 2) with placebo or drug treatments.
• Language features from exit interviews were analyzed to build the predictive model.

Main Results:

• The language model predicted placebo response in Study 2 with an area under the curve of 0.71.
• Predicted placebo responders reported 30% pain relief from inert pills, versus 3% for non-responders.
• The model did not predict responses to naproxen or spontaneous recovery, indicating specificity to placebo effects.

Conclusions:

• Placebo response in chronic pain is predictable using accessible measures like language use.
• Language-based prediction models offer a novel, non-invasive approach to identifying placebo responders.
• These findings have implications for optimizing clinical trial design and personalized pain treatment strategies.