Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

EHR data from all adult patients who had been in contact with the Psychiatric Services of the Central Denmark Region between 2013 and 2021 were retrieved. We derived 694 patient predictors (covering e.g. diagnoses, medication, and coercive measures) and 1134 predictors from free text using term frequency-inverse document frequency and sentence transformers. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the 2 years prior, we predicted involuntary admission 180 days ahead. XGBoost and elastic net models were trained on 85% of the dataset. The models with the highest area under the receiver operating characteristic curve (AUROC) were tested on the remaining 15% of the data.

The model was trained on 50 634 voluntary inpatient discharges among 17 968 patients. The cohort comprised of 1672 voluntary inpatient discharges followed by an involuntary admission. The best XGBoost and elastic net model from the training phase obtained an AUROC of 0.84 and 0.83, respectively, in the test phase.

A machine learning model using routine clinical EHR data can accurately predict involuntary admission. If implemented as a clinical decision support tool, this model may guide interventions aimed at reducing the risk of involuntary admission.

This study aimed to investigate changes in mRNA expression of the kynurenine pathway (KP) enzymes tryptophan 2, 3-dioxygenase (TDO), indoleamine 2, 3-dioxygenase 1 and 2 (IDO1, IDO2), kynurenine aminotransferase 1 and 2 (KAT1, KAT2), kynurenine monooxygenase (KMO) and kynureninase (KYNU) in medicated patients with depression (n = 74) compared to age- and sex-matched healthy controls (n = 55) and in patients with depression after electroconvulsive therapy (ECT). Associations with mood score (24-item Hamilton Depression Rating Scale, HAM-D24), plasma KP metabolites and selected glucocorticoid and inflammatory immune markers known to regulate KP enzyme expression were also explored.

HAM-D24 was used to evaluate depression severity. Whole blood mRNA expression was assessed using quantitative real-time polymerase chain reaction.

KAT1, KYNU and IDO2 were significantly reduced in patient samples compared to control samples, though results did not survive statistical adjustment for covariates or multiple comparisons. ECT did not alter KP enzyme mRNA expression. Changes in IDO1 and KMO and change in HAM-D24 score post-ECT were negatively correlated in subgroups of patients with unipolar depression (IDO1 only), psychotic depression and ECT responders and remitters. Further exploratory correlative analyses revealed altered association patterns between KP enzyme expression, KP metabolites, NR3C1 and IL-6 in depressed patients pre- and post-ECT.

Further studies are warranted to determine if KP measures have sufficient sensitivity, specificity and predictive value to be integrated into stress and immune associated biomarker panels to aid patient stratification at diagnosis and in predicting treatment response to antidepressant therapy.

Despite strong evidence of efficacy of electroconvulsive therapy (ECT) in the treatment of depression, no sensitive and specific predictors of ECT response have been identified. Previous meta-analyses have suggested some pre-treatment associations with response at a population level.

Using 10 years (2009–2018) of routinely collected Scottish data of people with moderate to severe depression (n = 2074) receiving ECT we tested two hypotheses: (a) that there were significant group-level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (b) that it was possible to develop a method for predicting illness remission for individual patients using machine learning.

Data were analysed on a group level using descriptive statistics and association analyses as well as using individual patient prediction with machine learning methodologies, including cross-validation.

ECT is highly effective for moderate to severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, necessity for urgent intervention, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for necessity for urgent intervention and, in addition, history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.

Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could inform shared decision-making, prevent the unnecessary use of ECT when it is unlikely to be beneficial and ensure prompt use of ECT when it is likely to be effective.

Most patients show temporary impairments in clinical orientation after electroconvulsive therapy (ECT)-induced seizures. It is unclear how postictal reorientation relates to electroencephalography (EEG) restoration. This relationship may provide additional measures to quantify postictal recovery and shed light on neurophysiological aspects of reorientation after ECT.

We analyzed prospectively collected clinical and continuous ictal and postictal EEG data from ECT patients. Postictal EEG restoration up to 1 h was estimated by the evolution of the normalized alpha–delta ratio (ADR). Times to reorientation in the cognitive domains of person, place, and time were assessed postictally. In each cognitive domain, a linear mixed model was fitted to investigate the relationships between time to reorientation and postictal EEG restoration.

In total, 272 pairs of ictal-postictal EEG and reorientation times of 32 patients were included. In all domains, longer time to reorientation was associated with slower postictal EEG recovery. Longer seizure duration and postictal administration of midazolam were related to longer time to reorientation in all domains. At 1-hour post-seizure, most patients were clinically reoriented, while their EEG had only partly restored.

We show a relationship between postictal EEG restoration and clinical reorientation after ECT-induced seizures. EEG was more sensitive than reorientation time in all domains to detect postictal recovery beyond 1-hour post-seizure. Our findings indicate that clinical reorientation probably depends on gradual cortical synaptic recovery, with longer seizure duration leading to longer postsynaptic suppression after ECT seizures.

Electroconvulsive therapy (ECT) is an established treatment for depression, but more data on effectiveness and safety in clinical practice is needed. The aim of this register-based study was to investigate short-term effectiveness and cognitive safety after ECT, evaluated by clinicians and patients. Secondary, we investigated predictors for remission and cognitive decline.

The study included 392 patients from the Regional Register for Neurostimulation Treatment in Western Norway. Depressive symptoms and cognitive function were assessed with Montgomery-Åsberg Depression Rating Scale and Mini-Mental State Examination (clinician-rated) and Beck Depression Inventory and Everyday Memory Questionnaire (patient-rated). Assessments were done prior to ECT-series and a mean of 1.7 days after (range 6 days before and 12 days after) end of ECT-series. Paired samples t-tests were extended by detailed, clinically relevant subgroups. Predictors were examined using logistic regression.

Clinician- and patient-rated remission rates were 49.5 and 41.0%, respectively. There was a large reduction in depressive symptoms and a small improvement in cognition after ECT, but we also identified subgroups with non-response of ECT in combination with cognitive decline (4.6% clinician-rated, 15.7% patient-rated). Positive predictors for patient- and clinician-rated remission were increasing age, shorter duration of depressive episode, and psychotic features. Antipsychotic medication at the commencement of treatment and previous ECT-treatment gave higher odds of clinician-rated remission, whereas higher pretreatment subjective depression level was associated with lower odds for patient-rated remission. Clinician-rated cognitive decline was predicted by higher pretreatment MMSE scores, whereas psychotic features, increasing age, and greater pretreatment subjective memory concerns were associated with lower odds for patient-rated cognitive decline.

Our study supports ECT as an effective and safe treatment, although subgroups have a less favorable outcome. ECT should be considered at an early stage for older patients suffering from depression with psychotic features. Providing comprehensive and balanced information from clinicians and patients perspectives on effects and side effects, may assist in a joint consent process.

The cause of cognitive side effects after electroconvulsive therapy (ECT) is largely unknown. Alterations in the blood–brain barrier (BBB) have been considered in several recent ECT studies. We therefore found it worthwhile to perform a systematic review of the literature to examine if electrically induced seizures affect the permeability of the BBB.

PubMed/MEDLINE and Embase were searched 16 November 2022. Studies with a direct measurement of BBB permeability in animals treated with modified electroconvulsive stimulation (ECS) and in humans treated with ECT were included. Synthesis of results was narrative due to the low number of studies and differences in study designs.

Four animal and two human (31 participants) studies were included. In animals, two studies found increased BBB permeability to some smaller molecules after modified ECS, while the two other studies found marginally increased or unchanged permeability to albumin after treatment. In contrast, the human studies did not find increased BBB permeability to smaller molecules or albumin after ECT.

Animal but not human studies support increased BBB permeability to some smaller molecules after electrically induced seizures. However, this conclusion is confined by the low number of studies and the lack of studies applying state-of-the-art methods. More studies using modern approaches to measuring of BBB permeability are warranted.

The study was founded by Mental Health Services in the Capital Region of Denmark (grant number 61151-05) and was registered on PROSPERO before data extraction was initiated (CRD42022331385).

Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome.

In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t0), after ECT (t1) and 2 years later (t2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed.

Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t0 to t1 and decreases from t1 to t2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t0 to t1 correlate with immediate and delayed symptom increase, while volume decreases from t1 to t2 correlate with long-term depressive outcome (all p ⩽ 0.049).

Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes.

This study aims to systematically review the literature on using electroconvulsive therapy (ECT) in patients with dementia/major NCD (Neuro cognitive disorder) presenting with behavioral symptoms.

We conducted a PRISMA-guided systematic review of the literature. We searched five major databases, including PubMed, Medline, Embase, Cochrane, and registry (ClinicalTrials.gov), collaborating with “ECT” and “dementia/major NCD” as our search terms.

Out of 445 published papers and four clinical trials, only 43 papers and three clinical trials met the criteria. There were 22 case reports, 14 case series, 4 retrospective chart reviews, 1 retrospective case–control study, 1 randomized controlled trial, and 2 ongoing trials. We evaluated existing evidence for using ECT in dementia/major NCD patients with depressive symptoms, agitation and aggression, psychotic symptoms, catatonia, Lewy body dementia/major NCD, manic symptoms, and a combination of these symptoms.

The studies were conducted in the in-patient setting.

Seven hundred and ninety total patients over the age of 60 years were added.

All reviewed studies reported symptomatic benefits in treating behavioral symptoms in individuals with dementia/major NCD. While transient confusion, short-term memory loss, and cognitive impairment were common side effects, most studies found no serious side effects from ECT use.

Current evidence from a systematic review of 46 studies indicates that ECT benefits specific individuals with dementia/major NCD and behavioral symptoms, but sometimes adverse events may limit its use in these vulnerable individuals.

Schizophrenia is a severe mental illness and a common indication for electroconvulsive therapy (ECT). Research is lacking on the factors that influence response to acute ECT treatment in schizophrenia patients.

This study examined the response rate and associated factors in patients with schizophrenia undergoing bilateral ECT.

Demographic data, clinical characteristics, ECT data and treatment response were respectively reviewed in patients with schizophrenia undergoing bilateral ECT from January 2013 to June 2022.

Forty-six patients were included. Nine responded after the first three sessions, 17 after six sessions, 20 after nine sessions, 25 after 12 sessions and 28 after the last ECT session, cumulatively. The mean of the baseline Brief Psychiatric Rating Scale psychotic symptom subscale score was significantly higher in responders (17.0) than non-responders (10.9) (P < 0.05). The mean of duration of electroencephalogram seizure was significantly longer in responders (53.9) than in non-responders (42.7). There was no association between demographic and ECT data and treatment response. Among 28 responders, 20 responded to ECT after nine sessions (faster responders) and eight responded later (slower responders). The number of failed antipsychotics prior to ECT was 2.8 for faster responders and 4.4 for slower responders (P = 0.02). Nominal logistic regression showed that the number of failed antipsychotics prior to ECT was associated with speed of response to ECT (P = 0.037, odds ratio = 1.77).

ECT is an effective treatment for schizophrenia and may be influenced by the number of failed antipsychotics prior to ECT.

To evoke a therapeutically effective seizure, electrical stimulation in electroconvulsive therapy (ECT) has to overcome the combined resistivity of scalp, skull and other tissues. Static impedances are measured prior to stimulation using high-frequency electrical alternating pulses, dynamic impedances during passage of the stimulation current. Static impedance can partially be influenced by skin preparation techniques. Earlier studies showed a correlation between dynamic and static impedance in bitemporal and right unilateral ECT.

This study aims at assessing the correlation of dynamic and static impedance with patient characteristics and seizure quality criteria in bifrontal ECT

We performed a cross-sectional single-centre retrospective analysis of ECT treatments at the Psychiatric University Hospital Zurich between May 2012 and March 2020 and used linear mixed-effects regression models in 78 patients with a total of 1757 ECT sessions.

Dynamic and static impedance were strongly correlated. Dynamic impedance was significantly correlated with age and higher in women. Energy set and factors positively (caffeine) and negatively (propofol) affecting seizure at the neuronal level were not associated with dynamic impedance. For secondary outcomes, dynamic impedance was significantly related to Maximum Sustained Power and Average Seizure Energy Index. Other seizure quality criteria showed no significant correlation with dynamic impedance.

Aiming for low static impedance might reduce dynamic impedance, which is correlated with positive seizure quality parameters. Therefore, good skin preparation to achieve low static impedance is recommended.