Predicting placebo analgesia in patients with chronic pain using natural language processing: A preliminary validation study

Abstract

Patients with chronic pain show large placebo effects in clinical trials, and inert pills can lead to clinically meaningful analgesia that can last from days to weeks. Whether the placebo response can be predicted reliably, and how to best predict it, is still unknown. We have shown previously that placebo responders can be identified through the language content of patients because they speak about their life, and their pain, after a placebo treatment. In this study, we examine whether these language properties are present before placebo treatment and are thus predictive of placebo response and whether a placebo prediction model can also dissociate between placebo and drug responders. We report the fine-tuning of a language model built based on a longitudinal treatment study where patients with chronic back pain received a placebo (study 1) and its validation on an independent study where patients received a placebo or drug (study 2). A model built on language features from an exit interview from study 1 was able to predict, a priori, the placebo response of patients in study 2 (area under the curve = 0.71). Furthermore, the model predicted as placebo responders exhibited an average of 30% pain relief from an inert pill, compared with 3% for those predicted as nonresponders. The model was not able to predict who responded to naproxen nor spontaneous recovery in a no-treatment arm, suggesting specificity of the prediction to placebo. Taken together, our initial findings suggest that placebo response is predictable using ecological and quick measures such as language use.