Clinical and neuroimaging predictors of benzodiazepine response in catatonia: A machine learning approach.

Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated with benzodiazepine resistance remain unknown. We aimed to develop machine learning models using clinical and neuroimaging data to predict benzodiazepine response in catatonic patients. This study examined a cohort of catatonic patients who underwent standardized clinical evaluation, 3 T brain MRI, and benzodiazepine trial. Based on clinical response, patients were classified as benzodiazepine responders or non-responders. Cortical thickness and regional brain volumes were measured. Two machine learning models (linear model and gradient boosting tree model) were developed to identify predictors of treatment response using clinical, demographic, and neuroimaging data. The cohort included 65 catatonic patients, comprising 30 benzodiazepine responders and 35 non-responders. Using clinical data alone, the linear model achieved 63% precision, 51% recall, a specificity of 61%, and 58% AUC, while the gradient boosting tree (GBT) model attained 46% precision, 60% recall, a specificity of 62% and 64% AUC. Incorporating neuroimaging data improved model performance, with the linear model achieving 66% precision, 57% recall, a specificity of 67%, and 70% AUC, and the GBT model attaining 50% precision, 50% recall, a specificity of 62% and 70% AUC. The integration of imaging data with demographic and clinical information significantly enhanced the predictive performance of the models. The duration of the catatonic syndrome, along with the presence of mitgehen (passive obedience) and immobility/stupor, and the volume of the right medial orbito-frontal cortex emerged as important factors in predicting non-response to benzodiazepines.

Exploring seasonality in catatonia diagnosis: Evidence from a large-scale population study.

Catatonia is a severe psychomotor syndrome mainly associated with psychiatric disorders, such as mood disorders and schizophrenia. Seasonal patterns have been described for these psychiatric disorders, and a previous study conducted in South London showed for the first time a seasonal pattern in the onset of catatonia. In this study, we aim to extend those findings to a larger national sample of patients admitted to French metropolitan hospitals, between 2015 and 2022, and to perform subgroup analyses by the main associated psychiatric disorder. A total of 6225 patients diagnosed with catatonia were included. A seasonal pattern for catatonia diagnosis was described, using cosinor models. Two peaks of diagnoses for catatonic cases were described in March and around September-October. Depending on the associated psychiatric disorder, the seasonality of catatonia diagnosis differed. In patients suffering with mood disorders, peaks of catatonia diagnosis were found in March and July. For patients suffering with schizophrenia, no seasonal pattern was found.

Seasonality of presentation and birth in catatonia.

BACKGROUND: Catatonia is a neuropsychiatric syndrome associated with both psychiatric disorders and medical conditions. Understanding of the pathophysiology of catatonia remains limited, and the role of the environment is unclear. Although seasonal variations have been shown for many of the disorders underlying catatonia, the seasonality of this syndrome has not yet been adequately explored. METHODS: Clinical records were screened to identify a cohort of patients suffering from catatonia and a control group of psychiatric inpatients, from 2007 to 2016 in South London. In a cohort study, the seasonality of presentation was explored fitting regression models with harmonic terms, while the effect of season of birth on subsequent development of catatonia was analyzed using regression models for count data. In a case-control study, the association between month of birth and catatonia was studied fitting logistic regression models. RESULTS: In total, 955 patients suffering from catatonia and 23,409 controls were included. The number of catatonic episodes increased during winter, with a peak in February. Similarly, an increasing number of cases was observed during summer, with a second peak in August. However, no evidence for an association between month of birth and catatonia was found. CONCLUSIONS: The presentation of catatonia showed seasonal variation in accordance with patterns described for many of the disorders underlying catatonia, such as mood disorders and infections. We found no evidence for an association between season of birth and risk of developing catatonia. This may imply that recent triggers may underpin catatonia, rather than distal events.

The role of the electroencephalogram (EEG) in determining the aetiology of catatonia: a systematic review and meta-analysis of diagnostic test accuracy.

Background: Catatonia is a psychomotor syndrome that has a wide range of aetiologies. Determining whether catatonia is due to a medical or psychiatric cause is important for directing treatment but is clinically challenging. We aimed to ascertain the performance of the electroencephalogram (EEG) in determining whether catatonia has a medical or psychiatric cause, conventionally defined. Methods: In this systematic review and meta-analysis of diagnostic test accuracy (PROSPERO CRD42021239027), Medline, EMBASE, PsycInfo, and AMED were searched from inception to May 11, 2022 for articles published in peer-reviewed journals that reported EEG findings in catatonia of a medical or psychiatric origin and were reported in English, French, or Italian. Eligible study types were clinical trials, cohort studies, case-control studies, cross-sectional studies, case series, and case reports. The reference standard was the final clinical diagnosis. Data extraction was conducted using individual patient-level data, where available, by two authors. We prespecified two types of studies to overcome the limitations anticipated in the data: larger studies (n ≥ 5), which were suitable for formal meta-analytic methods but generally lacked detailed information about participants, and smaller studies (n < 5), which were unsuitable for formal meta-analytic methods but had detailed individual patient level data, enabling additional sensitivity analyses. Risk of bias and applicability were assessed with the QUADAS-2 tool for larger studies, and with a published tool designed for case reports and series for smaller studies. The primary outcomes were sensitivity and specificity, which were derived using a bivariate mixed-effects regression model. Findings: 355 studies were included, spanning 707 patients. Of the 12 larger studies (5 cohort studies and 7 case series), 308 patients were included with a mean age of 48.2 (SD = 8.9) years. 85 (52.8%) were reported as male and 99 had catatonia due to a general medical condition. In the larger studies, we found that an abnormal EEG predicted a medical cause of catatonia with a sensitivity of 0.82 (95% CI 0.67-0.91) and a specificity of 0.66 (95% CI 0.45-0.82) with an I 2 of 74% (95% CI 42-100%). The area under the summary ROC curve offered excellent discrimination (AUC = 0.83). The positive likelihood ratio was 2.4 (95% CI 1.4-4.1) and the negative likelihood ratio was 0.28 (95% CI 0.15-0.51). Only 5 studies had low concerns in terms of risk of bias and applicability, but a sensitivity analysis limited to these studies was similar to the main analysis. Among the 343 smaller studies, 399 patients were included, resulting in a sensitivity of 0.76 (95% CI 0.71-0.81), specificity of 0.67 (0.57-0.76) and AUC = 0.71 (95% CI 0.67-0.76). In multiple sensitivity analyses, the results were robust to the exclusion of reports of studies and individuals considered at high risk of bias. Features of limbic encephalitis, epileptiform discharges, focal abnormality, or status epilepticus were highly specific to medical catatonia, but features of encephalopathy had only moderate specificity and occurred in 23% of the cases of psychiatric catatonia in smaller studies. Interpretation: In cases of diagnostic uncertainty, the EEG should be used alongside other investigations to ascertain whether the underlying cause of catatonia is medical. The main limitation of this review is the differing thresholds for considering an EEG abnormal between studies. Funding: Wellcome Trust, NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust.

Feasibility and usefulness of brain imaging in catatonia.

Catatonia is a well characterized psychomotor syndrome that has recognizable motor, affective, behavioural and vegetative manifestations. Despite recent demonstration that catatonia is often associated with brain imaging abnormalities, there is currently no consensus or guidelines about the role of brain imaging. In this study, we assessed the feasibility of brain imaging in a series of patients with catatonia in a routine clinical setting and estimated the prevalence of clinically relevant radiological abnormalities. Sixty patients with catatonia were evaluated against sixty non-healthy controls subjects with headache. The MRI reports were reviewed, and MRI scans were also interpreted by neuroradiologists using a standardised MRI assessment. In this cohort, more than 85% of brain scans of patients with catatonia revealed abnormalities. The most frequently reported abnormalities in the catatonic group were white matter abnormalities (n = 44), followed by brain atrophy (n = 27). There was no evidence for significant differences in the frequency of abnormalities found in radiology reports and standardised neuroradiological assessments. The frequency of abnormalities was similar to that found in a population of non-healthy controls subjects with headache. This study shows that MRI is feasible in patients with catatonia and that brain imaging abnormalities are common findings in these patients. Most frequently, white matter abnormalities and diffuse brain atrophy are observed.

Clinical meaning of body temperatures in anorexia nervosa and bulimia nervosa.

OBJECTIVE: Bradycardia is one of the common cardiac abnormalities in patients with eating disorders. It ensues from hypometabolism, which results from reduced caloric intake and consequential weight loss. Hypothermia is another consequence of hypometabolism. While at-rest metabolism and body mass index (BMI) are typically used to assess hypometabolism and estimate potential bradycardia, we hypothesised that body temperature, which is easy to measure, could also capture the presence of this threatening cardiovascular condition. METHOD: We monitored heart rate continuously for 72 h, measured resting energy expenditure (REE) and assessed body temperature in 12 body parts for 58 patients with anorexia nervosa (AN) and 29 patients with bulimia nervosa (BN). RESULTS: Palm temperature reflects bradycardia in both AN and BN, explaining 18% of its variance (p < 0.001), capturing this aspect even more efficiently than BMI. We also observe different correlations between palm temperature, abdominal temperature, BMI, REE and levels of physical activity. CONCLUSION: Palm temperature could be used as a warning of bradycardia, a serious cardiovascular condition which can be difficult to detect in short visits with outpatients. Further studies are needed to determine how useful bradycardia and palm temperature could be to assess severity and prognosis of the disorder.

Compulsory treatments in eating disorders: a systematic review and meta-analysis.

INTRODUCTION: The aims of this systematic review and meta-analysis are to provide a summary of the current literature concerning compulsory treatments in patients with eating disorders (ED) and to understand whether compulsorily and involuntarily treated patients differ in terms of baseline characteristics and treatment outcomes. METHODS: Relevant articles were identified following the PRISMA guidelines by searching the following terms: "treatment refusal", "forced feeding", "compulsory/coercive/involuntary/forced treatment/admission", "eating disorders", "feeding and eating disorders", "anorexia nervosa", "bulimia nervosa". Research was restricted to articles concerning humans and published between 1975 and 2020 in English. RESULTS: Out of 905 articles retrieved, nine were included for the analyses allowing the comparisons between 242 compulsorily and 738 voluntarily treated patients. Mean body mass index (BMI) was slightly lower in patients compelled to treatments. Mean illness duration, BMI at discharge and BMI variation showed no significant differences between the two groups. Average length of hospitalization was 3 weeks longer among compulsory-treated patients, but this did not result in a higher increase in BMI. No significant risk difference on mortality was estimated (three studies). CONCLUSIONS: Compulsory treatments are usually intended for patients having worse baseline conditions than voluntary ones. Those patients are unlikely to engage in treatments without being compelled but, after the treatments, albeit with longer hospitalisations, they do achieve similar outcomes. Therefore, we can conclude that forcing patients to treatment is a conceivable option. LEVEL OF EVIDENCE: Level I, systematic review and meta-analysis.