title: A statistical model predicting the seizure threshold for right unilateral ECT in 106 patients.
abstract: Titration of the electroconvulsive therapy (ECT) stimulus to the patient's convulsive threshold is the only way to directly assess the patient's seizure threshold. This technique is presently practiced by 39% of ECT providers, according to a recent survey. Because multiple variables influence the seizure threshold in patients, multivariate statistical methods may provide a useful strategy to determine which variables exert the most influence on convulsive threshold. A multivariate ordinal logistic model of seizure threshold was developed on an experimental group of 66 consecutive patients undergoing titrated right unilateral (RUL) ECT for major depression. The accuracy of the model was cross-validated on a second group of 40 patients undergoing similar RUL ECT procedures. The final multivariate ordinal logistic regression model for the seizure threshold level (STL) was significant (Likelihood ratio chi 2 = 54.115; p < 0.0001:R2 = 0.313). Increasing age, African-American race, and longer inion-nasion distances (p < 0.06) predicted higher STL. Female gender was associated with a lower STL. The ability of the final model to accurately predict STL for the validation group was fair (pairwise correlation was 0.576; p < 0.001). The model did well for predicting lower STL, but fared poorly for higher STL. In conclusion, modeling STL may help establish the relative contribution of variables thought to be important to seizure threshold. However, STL models remain impractical for clinical applications in estimating seizure threshold at this time, and empirical stimulus titration should be used.
authors.affiliation: Department of Psychiatry and Behavioral Medicine, Bowman Gray School of Medicine, Winston-Salem, North Carolina 27517, USA.
authors.forename: C C
authors.initials: CC
authors.lastname: Colenda
authors.forename: W V
authors.initials: WV
authors.lastname: McCall
mesh_terms: D000328:Adult; D003866:Depressive Disorder / Q000628:therapy; D004565:Electroconvulsive Therapy; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D012044:Regression Analysis; D012640:Seizures / Q000503:physiopathology*