Dataset Information

Electroconvulsive Therapy and the Risk of Suicide in Hospitalized Patients With Major Depressive Disorder.

ABSTRACT:

Importance

Electroconvulsive therapy (ECT) is indicated for severe depression, including depression with psychosis, catatonia, and/or an elevated suicide risk. However, the association of ECT with suicide risk is uncertain.

Objective

To determine the association between ECT and the risk of suicide in patients with unipolar major depressive disorder.

Design, setting, and participants

This registry-based cohort study used patient data from Swedish national registers. Patients with a record of inpatient care between January 1, 2012, and October 31, 2018, for moderate depression, severe depression, or severe depression with psychosis were included in the study. Propensity score matching (1:1) was used to balance risk factors for suicide at baseline between patients treated with and without ECT during the inpatient episode.

Exposures

Data from the Swedish National Quality Register for ECT and the Swedish National Inpatient Register were combined to identify patients who had received ECT during the inpatient episode. National registers were used to identify risk factors for suicide.

Main outcomes and measures

Suicide within 3 and 12 months of admission to inpatient care was analyzed. Cox regression analyses were used to adjust for confounders.

Results

The study included 28 557 patients (mean [SD] age, ECT group, 55.9 [18.4] years; non-ECT group, 45.2 [19.2] years; 15 856 women [55.5%]). In the matched sample of 5525 patients in each group, 62 patients (1.1%) in the ECT group and 90 patients (1.6%) in the non-ECT group died of suicide within 12 months (hazard ratio [HR], 0.72; 95% CI, 0.52-0.99). Electroconvulsive therapy was significantly associated with a decreased risk of suicide in patients with psychotic features (HR, 0.20; 95% CI, 0.08-0.54) and those aged 45 to 64 years (HR, 0.54; 95% CI, 0.30-0.99) or 65 years or older (HR, 0.30; 95% CI, 0.15-0.59), but not in patients aged 44 years or younger (HR, 1.22; 95% CI, 0.68-2.16).

Conclusions and relevance

The results of this cohort study support the continued use of ECT to reduce suicide risk in hospitalized patients who are severely depressed, especially those who are older than 45 years and those with a psychotic subtype.

SUBMITTER: Ronnqvist I

PROVIDER: S-EPMC8295734 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWe aimed to compare the degree of symptom relief to psychosocial functional (abbreviated as "functional") improvement and explore the relationships between symptom relief and functional improvement during acute electroconvulsive therapy for patients with major depressive disorder.MethodsMajor depressive disorder inpatients (n=130) requiring electroconvulsive therapy were recruited. Electroconvulsive therapy was generally performed for a maximum of 12 treatments. Symptom severity, using the 17-item Hamilton Depression Rating Scale, and psychosocial functioning (abbreviated as "functioning"), using the Modified Work and Social Adjustment Scale, were assessed before electroconvulsive therapy, after every 3 electroconvulsive therapy treatments, and after the final electroconvulsive therapy. Both 17-item Hamilton Depression Rating Scale and Modified Work and Social Adjustment Scale scores were converted to T-score units to compare the degrees of changes between depressive symptoms and functioning after electroconvulsive therapy. Structural equation modeling was used to test the relationships between 17-item Hamilton Depression Rating Scale and Modified Work and Social Adjustment Scale during acute electroconvulsive therapy.ResultsOne hundred sixteen patients who completed at least the first 3 electroconvulsive therapy treatments entered the analysis. Reduction of 17-item Hamilton Depression Rating Scale T-scores was significantly greater than that of Modified Work and Social Adjustment Scale T-scores at assessments 2, 3, 4, and 5. The model analyzed by structural equation modeling satisfied all indices of goodness-of-fit (chi-square = 32.882, P =.107, TLI = 0.92, CFI = 0.984, RMSEA = 0.057). The 17-item Hamilton Depression Rating Scale change did not predict subsequent Modified Work and Social Adjustment Scale change.ConclusionsFunctioning improved less than depressive symptoms during acute electroconvulsive therapy. Symptom reduction did not predict subsequent functional improvement. Depressive symptoms and functional impairment are distinct domains and should be assessed independently to accurately reflect the effectiveness of electroconvulsive therapy.

Project description:BackgroundPrevious studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD).MethodsParticipants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI).ResultsDSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm.LimitationsA limited sample size and failure to include sufficient suicide risk factors in the predictive model.ConclusionThis study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.

Project description:BackgroundAlthough electroconvulsive therapy (ECT) is an effective treatment for depression, ECT cognitive impairment remains a major concern. The neurobiological underpinnings and mechanisms underlying ECT antidepressant and cognitive impairment effects remain unknown. This investigation aims to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks and assesses whether they are associated with the ECT-induced electric field (E-field) with an optimal pulse amplitude estimation.MethodsA single site clinical trial focused on amplitude (600, 700, and 800 mA) included longitudinal multimodal imaging and clinical and cognitive assessments completed before and immediately after the ECT series (n = 54) for late-life depression. Another two independent validation cohorts (n = 84, n = 260) were included. Symptom and cognition were used as references to supervise fMRI and sMRI fusion to identify ECT antidepressant-response and cognitive-impairment multimodal brain networks. Correlations between ECT-induced E-field within these two networks and clinical and cognitive outcomes were calculated. An optimal pulse amplitude was estimated based on E-field within antidepressant-response and cognitive-impairment networks.ResultsDecreased function in the superior orbitofrontal cortex and caudate accompanied with increased volume in medial temporal cortex showed covarying functional and structural alterations in both antidepressant-response and cognitive-impairment networks. Volume increases in the hippocampal complex and thalamus were antidepressant-response specific, and functional decreases in the amygdala and hippocampal complex were cognitive-impairment specific, which were validated in two independent datasets. The E-field within these two networks showed an inverse relationship with HDRS reduction and cognitive impairment. The optimal E-filed range as [92.7-113.9] V/m was estimated to maximize antidepressant outcomes without compromising cognitive safety.ConclusionsThe large degree of overlap between antidepressant-response and cognitive-impairment networks challenges parameter development focused on precise E-field dosing with new electrode placements. The determination of the optimal individualized ECT amplitude within the antidepressant and cognitive networks may improve the treatment benefit-risk ratio.Trial registrationClinicalTrials.gov Identifier: NCT02999269.