We greatly appreciate Zhao and colleagues’ thoughtful comments on our publication.1, 2 Our goal was to share our clinical experience and describe the data collected, noting that no standardized algorithm was followed for medication reduction after Deep Brain Stimulation (DBS). Our study did not aim to detail the steps regarding when, how, or in whom to reduce or stop anticholinergic medications or botulinum toxin.2 Such decisions were (and are) based on clinical judgment, which is not easily captured by patients’ charts. Nonetheless, we fully recognize the importance of establishing clear guidelines to inform when and how dose reductions should be pursued. Future research is necessary to address the points raised, including expansion to additional variables not included in our initial study: patients with early complications, longer-term follow-up, and outcome analysis over time. We also concur that advancing beyond current data limitations requires incorporating patient-reported outcomes, cognitive assessments—particularly given the detrimental effect of anticholinergic medications—as well as formal cost-effectiveness analyses.3 We believe that true validation of such approaches would require not only multicenter studies within a country, but also international collaborations across centers with diverse healthcare infrastructures and patient populations. These future steps will inform the implementation of a standardized clinical guidance algorithm across centers; we are in strong agreement that this would be an extremely valuable initiative. It could also enable decentralization of care and allow for local follow-up for patients who do not require frequent DBS programming, thus improving access and reducing pressure on specialized centers. However, implementation is currently difficult to execute due to the shortage of professionals experienced in managing rare dystonic conditions that require DBS therapy. Regarding data science and artificial intelligence (AI), we agree that AI holds promise as a complementary tool in clinical decision-making. However, we remain cautious about its applicability in this specific context, where purely data-driven models may fail to accurately capture the complexity of individual patients, particularly in cases of generalized or complex dystonias. While AI-based systems may suggest medication adjustments based on retrospective data, our finding—and prior evidence—show that commonly used variables such as Total Electrical Energy Delivered, preoperative medication doses, and several rating scales scores, do not consistently correlate with post-surgical outcomes.4 As such, clinical judgment will remain essential, even as AI tools evolve.5 Finally, we respectfully disagree with the generalizability issues. First, DBS optimization in dystonia is known to take time and at least 1 year of stimulation seems the shortest timeframe to really detect a change in medications. Second, the Canadian healthcare system is public and highly centralized, so we are confident that our two centers captured the majority of the population referred for DBS regardless of socioeconomic background. Once again, we sincerely thank you for your insightful and constructive comments, which we believe significantly enrich the discussion and add valuable depth to our work. (1) Research project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the first draft, B. Review and Critique M.A.M.: 1A, 1B, 1C, 3A A.F.: 1A, 1B, 1C, 3B Ethical Compliance Statement: The authors confirm that the approval of an institutional review board was not required for this work. Informed patient consent was not necessary for this work. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Funding Sources and Conflict of Interest: This study was partly funded by the University Health Network and University of Toronto Chair in Neuromodulation to AF. The authors declare that there are no conflicts of interest relevant to this work. Financial Disclosures for the Previous 12 Months: MM has no financial disclosures. AF has stock ownership in Inbrain Pharma and has received payments as consultant and/or speaker from Abbvie, Abbott, Boston Scientific, Ceregate, Dompé Farmaceutici, Inbrain Neuroelectronics, Ipsen, Medtronic, Iota, Syneos Health, Merz, Sunovion, Paladin Labs, UCB, Sunovion. He has received research support from Abbvie, Boston Scientific, Medtronic, Praxis, ES and receives royalties from Springer. Data available on request from the authors.
Montiel et al. (Wed,) studied this question.