Prediction of multisite pain incidence in adolescence using a machine learning approach

ImportanceMultisite pain is a major adverse health outcome in the adolescent population, affecting the daily lives of up to every third adolescent and their families. ObjectiveTo 1) predict multisite pain incidence in the whole body and in the musculoskeletal locations in adolescents, and 2) explore the sex-specific predictors of multisite pain incidence with a novel machine learning approach. DesignA 2-year observational study (2013-2015). Three different baseline data sets were utilized to predict multisite pain incidence during the follow-up. SettingPopulation-based sample of Finnish adolescents. ParticipantsApparently healthy adolescents. ExposuresThe first data set included 48 selected baseline variables relevant for adolescents health and wellbeing. Data included information on students self-reported, objectively measured, and device-based demographics, physical and psychosocial characteristics, and lifestyle factors. The second data set included nine physical fitness variables related to the Finnish national Move! monitoring and surveillance system for health-related fitness. The third data set included all available baseline data (392 variables). Main Outcome and MeasuresOnset of multisite pain (=weekly pain during the past three months manifesting in at least three sites and not related to any known disease or injury) during the 2-year follow-up in the whole body or musculoskeletal locations. Musculoskeletal pain sites included the neck/shoulder, upper extremities, chest, upper back, low back, buttocks, and lower extremities. Whole body pain sites also the head and abdominal areas. A machine learning algorithm random forest was utilized. ResultsAmong 410 participants (57% girls) aged on average 12.5-years (SD 1.2), 16 % of boys and 28 % of girls developed multisite pain in the whole body and 10 % and 15 % in the musculoskeletal area during follow-up. The prediction ability of the machine learning approach with 48 predictive variables reached an AUC 0.65 at highest. With ML, a broad variety of predictors were identified, with up to 33 variables showing predictive power in girls and 13 in boys. Conclusions and RelevanceFindings highlight that rather than any isolated variable, a variety of factors pose a risk for future multisite pain. More emphasis on holistic and multidisciplinary approaches is recommended to prevent multisite pain in adolescence. Key points QuestionWhat is the ability of machine learning approach to predict multisite pain incidence during adolescence? FindingsWith a random forest machine learning method, a broad variety of predictive physical, lifestyle and psychosocial factors were identified. Prediction ability reached AUC 0.65. MeaningThe findings highlight that predictors of multisite pain incidence in adolescence are multifaceted, although the prediction ability of machine learning remained under clinical relevance (AUC <0.7). These findings support the adoption of holistic and multidisciplinary prevention approaches for multisite pain in adolescence in the future.