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Supplementary Material for: Multicenter Validation of Individual Preoperative Motor Outcome Prediction for Deep Brain Stimulation in Parkinson’s Disease

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posted on 25.11.2021, 10:09 by Habets J.G.V., Herff C., Fasano A.A., Beudel M., Kocabicak E., Schnitzler A., Snineh M.A., Kalia S.K., Ramirez-Gómez C., Hodaie M., Munhoz R.P., Rouleau E., Yildiz O., Linetsky E., Schuurman R., Hartmann C.J., Lozano A.M., DeBie R.M.A., Temel Y., Janssen M.L.F.
Background: Subthalamic nucleus deep brain stimulation (STN DBS) is an established therapy for Parkinson’s disease (PD) patients suffering from motor response fluctuations despite optimal medical treatment, or severe dopaminergic side effects. Despite careful clinical selection and surgical procedures, some patients do not benefit from STN DBS. Preoperative prediction models are suggested to better predict individual motor response after STN DBS. We validate a preregistered model, DBS-PREDICT, in an external multicenter validation cohort. Methods: DBS-PREDICT considered eleven, solely preoperative, clinical characteristics and applied a logistic regression to differentiate between weak and strong motor responders. Weak motor response was defined as no clinically relevant improvement on the Unified Parkinson’s Disease Rating Scale (UPDRS) II, III, or IV, 1 year after surgery, defined as, respectively, 3, 5, and 3 points or more. Lower UPDRS III and IV scores and higher age at disease onset contributed most to weak response predictions. Individual predictions were compared with actual clinical outcomes. Results: 322 PD patients treated with STN DBS from 6 different centers were included. DBS-PREDICT differentiated between weak and strong motor responders with an area under the receiver operator curve of 0.76 and an accuracy up to 77%. Conclusion: Proving generalizability and feasibility of preoperative STN DBS outcome prediction in an external multicenter cohort is an important step in creating clinical impact in DBS with data-driven tools. Future prospective studies are required to overcome several inherent practical and statistical limitations of including clinical decision support systems in DBS care.

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