Abstract
The use of machine learning to predict postoperative pain and opioid use has likely been catalyzed by the availability of complex patient-level data, computational and statistical advancements, the prevalence and impact of chronic postsurgical pain, and the persistence of the opioid crisis. The objectives of this narrative review were to identify and characterize methodological aspects of studies that have developed and/or tested machine learning algorithms to predict acute, subacute, or chronic pain or opioid use after any surgery and to propose considerations for future machine learning studies. Pairs of independent reviewers screened titles and abstracts of 280 PubMed-indexed articles and ultimately extracted data from 61 studies that met entry criteria. We observed a marked increase in the number of relevant publications over time. Studies most commonly focused on machine learning algorithms to predict chronic postsurgical pain or opioid use, using real-world data from patients undergoing orthopedic surgery. We identified variability in sample size, number and type of predictors, and how outcome variables were defined. Patient-reported predictors were highlighted as particularly informative and important to include in such machine learning algorithms, where possible. We hope that findings from this review might inform future applications of machine learning that improve the performance and clinical utility of resultant machine learning algorithms.