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Zusammenfassung Hintergrund Für die Behandlung von Menschen mit Schizophrenien wird vorrangig antipsychotische Monotherapie empfohlen. Antipsychotische Polypharmazie (APP) wird dennoch oft eingesetzt und dient als Qualitätsindikator. Ziel der Arbeit Das Auftreten von APP an 9 psychiatrischen Kliniken des LVR zu untersuchen und die Nutzbarkeit als Qualitätsindikator zu diskutieren. Material und Methoden Es wurden Behandlungsdaten von stationären und teilstationären Fällen mit der Hauptdiagnose ICD-10 F20.x im Zeitraum 6/2016 bis 6/2017 hinsichtlich der APP-Rate analysiert. Ergebnisse Bei 6788 Behandlungsfällen wurde eine APP-Rate von 55,5 % ermittelt. Männliches Geschlecht, längere Verweildauer, keine Zwangsunterbringung und der Standort der Klinik waren mit APP assoziiert. Diskussion Die APP-Rate ist im Vergleich bisherigen Ergebnissen hoch und weist erhebliche Unterschiede zwischen den Kliniken auf. Die Verwendung des Qualitätsindikators APP wird diskutiert.

Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. Methods We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. Results Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. Conclusion Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies.

OBJECTIVE Hypothermia is a potentially lethal adverse reaction to typical and atypical antipsychotic drugs (APD). Among predisposing factors are advanced age and comorbid somatic diseases. The aim of this study was to assess the incidence of hypothermia and quantify risk factors. METHOD Charts of N = 3002 psychogeriatric inpatients were screened for incidence of hypothermia (body core temperature

Introduction Everyday clinical routine is frequently challenged by difficulty to choose among differential diagnostic options, since many psychiatric disorders share similar phenotypes. E.g., borderline personality disorder (BPD) and schizophrenia (SZ) can both be associated with psychotic syndromes. Objectives Our objective was to evaluate the effectiveness of combining sMRI data and pattern classification methods to differentiate between BPD and SZ. Aims We aim to introduce objective diagnostic measures to improve the reliability of clinical evaluations. Methods sMRI data of 114 female patients were used to train a multivariate disease classifier. MR images were processed using voxel-based morphometry and high-dimensional registration to the MNI template. Grey matter volume maps were fed into a machine learning pipeline consisting of adjustment for possible age effects, PCA for dimensionality reduction and linear ν-support vector classification. Diagnostic performance of the classifier was determined by repeated nested 10-fold cross-validation. Results We were able to correctly classify unseen test subjects’ diagnosis with 74% accuracy. Classification sensitivity and specificity was 74%. Volume reductions in SZ vs. BPD were predominantly located in the left peri- and intrasylvian regions, orbitofrontal regions, the nucleus caudatus and the right cerebellum. Volume reductions in BPD compared to SZ were found predominantly in the left cerebellum, in limbic areas and the left inferior occipital gyrus. Conclusions Our results suggest that SZ can be differentiated from BPD at the single-subject level using sMRI and pattern classification methods. In future, this method might enhance clinical evaluations and improve accuracy and reliability of differential diagnosis.