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

Paris Hosseini, Rebecca Whincup, Karrish Devan, Dory Anthony Ghanem, Jack B. Fanshawe, Aman Saini, Benjamin Cross, Apoorva Vijay, Tomas Mastellari, Umesh Vivekananda, Steven White, Franz Brunnhuber, Michael S. Zandi, Anthony S. David , Ben Carter, Dominic Oliver, Glyn Lewis, Charles Fry, Puja R. Mehta, Biba StantonJonathan P/ Rogers

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4 Citations (Scopus)


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.

Original languageEnglish
Article number101808
Early online date5 Jan 2023
Publication statusPublished - Feb 2023


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