Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression.

Autor:	McInnes AN; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Olsen ST; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Sullivan CRP; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Cooper DC; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Wilson S; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Sonmez AI; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Albott CS; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Olson SC; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Peterson CB; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Rittberg BR; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Herman A; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Bajzer M; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Nahas Z; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA., Widge AS; Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA.
Jazyk:	angličtina
Zdroj:	Personalized medicine in psychiatry [Pers Med Psychiatry] 2024 Nov-Dec; Vol. 47-48. Date of Electronic Publication: 2024 Aug 22.
DOI:	10.1016/j.pmip.2024.100135
Abstrakt:	Background: Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. Methods: We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. Results: LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52 - 0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. Conclusions: In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.
Databáze:	MEDLINE
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