Jha Rohan, Wadhwa Aryan, Chua Melissa M J, Cosgrove G Rees, Rolston John D
Harvard Medical School, Boston, Massachusetts, USA.
Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA.
Mov Disord Clin Pract. 2024 Dec;11(12):1542-1549. doi: 10.1002/mdc3.14237. Epub 2024 Oct 25.
Imbalance is the most commonly reported side effect following focused ultrasound (FUS) thalamotomy for essential tremor (ET). It remains unknown which patients are more likely to develop imbalance following FUS treatment.
To identify preoperative and treatment-related sonication parameters that are predictive of imbalance following FUS treatment.
We retrospectively collected demographic data, preoperative Fahn-Tolosa-Marin Clinical Rating Scale for Tremor (FTM) scores and FUS treatment parameters in patients undergoing FUS thalamotomy for treatment of ET. The presence of imbalance was evaluated at several discrete time-points with up to 4 years of follow-up. Multiple machine learning classifiers were built and evaluated, aiming to maximize accuracy while minimizing feature set.
Of the 297 patients identified, the presence of imbalance peaked at 1 week following operation at 79%. This declined rapidly with 29% reporting imbalance at 3 months, and only 15% at 4 years. At 1 week, total preoperative FTM scores and Maximum Energy delivered in FUS could predict the presence of imbalance at 92.8% accuracy. At 3 months, the total preoperative FTM scores and maximum power delivered could predict the presence of imbalance with 90.6% accuracy. Post-operative lesion size and extent into thalamic nuclei, internal capsule, and subthalamic regions were identified as likely key underlying drivers of these predictors.
A machine learning model based on preoperative tremor scores and maximum energy/power delivered predicted the development of short-term imbalance and long-term imbalance following FUS thalamotomy.
失衡是聚焦超声(FUS)丘脑切开术治疗特发性震颤(ET)后最常报告的副作用。目前尚不清楚哪些患者在接受FUS治疗后更容易出现失衡。
确定术前和与治疗相关的超声参数,这些参数可预测FUS治疗后失衡的发生。
我们回顾性收集了接受FUS丘脑切开术治疗ET患者的人口统计学数据、术前震颤的Fahn-Tolosa-Marin临床评分量表(FTM)得分和FUS治疗参数。在长达4年的随访期间的几个离散时间点评估失衡的存在情况。构建并评估了多个机器学习分类器,旨在在最小化特征集的同时最大化准确性。
在确定的297例患者中,失衡的发生率在术后1周达到峰值,为79%。此后迅速下降,3个月时报告失衡的患者为29%,4年时仅为15%。在术后1周,术前FTM总分和FUS传递的最大能量可预测失衡的存在,准确率为92.8%。在3个月时,术前FTM总分和传递的最大功率可预测失衡的存在,准确率为90.6%。术后病变大小以及向丘脑核、内囊和丘脑底区域的扩展被确定为这些预测指标可能的关键潜在驱动因素。
基于术前震颤评分和传递的最大能量/功率的机器学习模型可预测FUS丘脑切开术后短期和长期失衡的发生。