Early symptom improvement at 10 sessions as a predictor of rTMS treatment outcome in major depression

Publication date: Available online 19 October 2017
Source:Brain Stimulation
Author(s): Kfir Feffer, Hyewon Helen Lee, Farrokh Mansouri, Peter Giacobbe, Fidel Vila-Rodriguez, Sidney H. Kennedy, Zafiris J. Daskalakis, Daniel M. Blumberger, Jonathan Downar
BackgroundPredicting rTMS nonresponse could be helpful in sparing patients from futile treatment, and in improving use of limited rTMS resources. While several predictive biomarkers have been proposed, few are accurate for individual-level prediction; none have entered routine use. An alternative approach in pharmacotherapy predicts outcome from early response; patients showing minimal (e.g., ≤20%) improvement at 2 weeks can be predicted as nonresponders with negative predictive values (NPV) > 80–90%. This approach has recently been extended to ECT, but never before to rTMS.ObjectiveTo assess the accuracy of 2-week clinical response in predicting rTMS treatment outcome.MethodsWe reviewed clinical symptom scores for 101 patients who underwent 20 sessions of dorsomedial prefrontal rTMS for unipolar major depression in a naturalistic retrospective case series, defining nonresponders both at the conventional <50% improvement criterion and at a more stringent <35% criterion.ResultsPatients achieving <20% improvement at session 10 were correctly predicted as nonresponders with NPVs of 88.2% by the conventional and 80.4% by the stringent criterion. Achieving <10% improvement at session 10 predicted nonresponse with NPVs of 89.5% and 86.8% by conventional and stringent criteria, respectively. Using the least-depressed score of either session 5 or 10, <20% improvement predicted nonresponse with NPVs of 91.3% and 82.6%, and <10% improvement predicted nonresponse with NPVs of 93.5% and 93.5%, by conventional and stringent criteria.ConclusionFor DMPFC-rTMS, a '<20% improvement at 2 weeks' rule concurred with previous pharmacotherapy and ECT studies on predicting nonresponse, and could prove useful for treatment decision-making in clinical settings.



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