Electroconvulsive therapy (ECT) is one of the most effective treatments for depression, but worries about cognitive side effects remain. This retrospective study evaluated cognitive outcomes and the antidepressant efficacy of ECT in a real-life sample of patients with treatment-resistant uni- or bipolar depression.

We included 90 depressed inpatients aged 49 ± 13.8 (SD) years who underwent 10 ± 2.1 (SD) unilateral or bitemporal ECT treatments and completed an extensive pre- and post-treatment psychological test battery. The Hamilton Depression Rating Scale (HAMD) and the Mini-Mental State Examination (MMSE) were evaluated as main outcomes pre-/post-ECT treatment.

There was no significant change in MMSE scores between pre-/post-treatment assessments (β = 0.10, 95% confidence interval [CI] [−0.44, 0.25], p = 0.58), indicating no negative effect on global cognition. A minority of patients (N = 3) experienced a reduction of ≥5 points in the MMSE. Most cognitive tests showed no difference; however, some domains revealed statistically significant improvements (visual learning and motoric reaction time), whereas one domain showed a significant decline (verbal learning). Higher age and higher stimulus doses predicted worse outcomes in some cognitive domains. While ECT significantly reduced depressive symptoms measured by HAMD (β = −5.51, 95% CI [−7.08, −3.94], p < 0.001), depressive symptoms were not associated with cognitive outcomes.

No major cognitive changes were observed. While test results indicated deterioration in verbal learning and improvement in visual learning and motoric reaction time, effect sizes were small, and other cognitive tests showed no significant changes. The main limitation is the absence of retrograde memory assessment.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.