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Bu sayfa, 26. Dünya Psikiyatri Kongresi'nde sunulan çalışmanın yapay zekâ desteğiyle hazırlanmış Türkçe özetidir. Çalışmanın sahibi sunan yazarlardır; atıf için orijinal kaynağa başvurun.

EV0895

How can the course of ECT treatments over time be predicted: a modeling approach

HOW TO PREDICT THE TIME COURSE OF ECT TREATMENTS - A MODELLING APPROACH

A numerical model assuming that seizure resistance accumulates exponentially with previous stimulations provides a significant fit at the individual level for approximately 60% of ECT courses and summarizes the entire course using four parameters.

The aim is to develop a numerical model describing the trajectory of seizure intensity over the course of electroconvulsive therapy (ECT) and apply it to a large database; although seizure intensity is associated with treatment outcome, no model had quantitatively explained how it varies with treatment history and stimulus intensity. The method consists of three components: using the total integral of power spectral density as a measure of seizure intensity that incorporates duration, intensity, and synchronization; constructing a model based on the assumption that seizure resistance accumulates as an exponential sum of previous stimulations and estimating the parameters k, alpha1, alpha2, and threshold T0 through nonlinear optimization; and applying the model to an ECT-EEG database collected between 2002 and 2020. Based on data completeness and treatment duration criteria, 452 treatment courses from 368 patients were included in the analysis. Across these courses, an average of 13.6 ± 5.2 stimulations were administered over 34.1 ± 15 days, with the mean dose increasing from 124 mC to 309 mC. The model provided a significant fit at the individual level for approximately 60% of treatment courses; the mean k value corresponded to a half-life of 5.3 days, and the mean threshold T0 was 174 mC. Both threshold T0 and sensitivity alpha2 were significantly associated with age; older patients required higher doses and were more sensitive to the cumulative effect of previous stimulations. In conclusion, an entire ECT course can be expressed using four parameters, enabling simulation of the treatment trajectory, simplification of the data into a form that can be related to clinical variables, and patient-specific dose planning; parameter distributions and clinical utility should be tested in large, well-controlled datasets.

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