Predictive Models for Motor Outcomes From Deep Brain Stimulation in Parkinson's Disease: A Systematic Review

Abstract

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease, but the extent of improvement in motor symptoms varies. A tool which accurately predicts patient outcomes based on data available pre‐surgery would be useful for clinical decision‐making and patient expectation management. Such data includes clinical and cognitive measures, neuroimaging, kinematics, functional connectivity and genetics. In this systematic review, we assess predictive models of motor outcomes from DBS. We searched the databases Web of Science, PubMed and Scopus for primary research articles that tested predictions from models based on pre‐surgical data and focused on motor outcomes from DBS for Parkinson's disease. We identified 19 studies fitting these criteria. The studies with high statistical power and generalisability use only clinical data and have limited accuracy. Studies including other types of data, such as magnetic resonance imaging, may have high accuracy but are under‐powered. Predictions are mostly limited to the first year after surgery, subthalamic nucleus‐targeted DBS and sum scores of motor performance. Following the results of the systematic search, we discuss candidate input and output variables and validation strategies to produce predictive models that are ready for translation to clinical practice. Ideally, models would predict scores for several motor domains, over a range of time after surgery, with confidence intervals. They should also be generalisable to clinics worldwide. To achieve this goal, models and datasets should be made publicly available to enable wider validation. These recommendations provide a framework to achieve predictive models for DBS outcomes that can be used clinically.