Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry

BACKGROUND Spontaneous episodic vertigo syndromes, namely vestibular migraine (VM) and Menière's disease (MD), are difficult to differentiate, even for an experienced clinician. In the presence of complex diagnostic information, automated systems can support human decision making. Recent developments in machine learning might facilitate bedside diagnosis of VM and MD. METHODS Data of this study originate from the prospective patient registry of the German Centre for Vertigo and Balance Disorders, a specialized tertiary treatment center at the University Hospital Munich. The classification task was to differentiate cases of VM, MD from other vestibular disease entities. Deep Neural Networks (DNN) and Boosted Decision Trees (BDT) were used for classification. RESULTS A total of 1357 patients were included (mean age 52.9, SD 15.9, 54.7% female), 9.9% with MD and 15.6% with VM. DNN models yielded an accuracy of 98.4 ± 0.5%, a precision of 96.3 ± 3.9%, and a sensitivity of 85.4 ± 3.9% for VM, and an accuracy of 98.0 ± 1.0%, a precision of 90.4 ± 6.2% and a sensitivity of 89.9 ± 4.6% for MD. BDT yielded an accuracy of 84.5 ± 0.5%, precision of 51.8 ± 6.1%, sensitivity of 16.9 ± 1.7% for VM, and an accuracy of 93.3 ± 0.7%, precision 76.0 ± 6.7%, sensitivity 41.7 ± 2.9% for MD. CONCLUSION The correct diagnosis of spontaneous episodic vestibular syndromes is challenging in clinical practice. Modern machine learning methods might be the basis for developing systems that assist practitioners and clinicians in their daily treatment decisions

In-depth analysis of cost structure for electroconvulsive therapy in a performance-based hospital budget

Objective New medical guideline recommendations for the treatment of major depressive disorders and regulative changes in the payment system of the German mental health care system warrant a revision of the framework in which electroconvulsive therapies (ECT) are offered. Methods A cost structure analysis of the clinical resources essential for the ECT procedure was conducted and economically validated, exemplified at a German inpatient ECT treatment center. Results The identification of directly attributable costs to the ECT intervention presupposes an accurate assessment of personnel engagement time and material consumption as well as an inclusion of overhead costs for the operational readiness of the hospital. Conclusion The increasing importance of ECT in the clinical portfolio of therapy options demands an adequate refunding to support the expansion of this highly effective treatment. For the calculation of an appropriate reimbursement for ECT and ascertaining an acceptable contribution, a detailed knowledge of personnel costs and infrastructure settings of the respective hospitals is required

Electroconvulsive therapy modulates grey matter increase in a hub of an affect processing network

A growing number of recent studies has suggested that the neuroplastic effects of electroconvulsive therapy (ECT) might be prominent enough to be detected through changes of regional gray matter volumes (GMV) during the course of the treatment. Given that ECT patients are difficult to recruit for imaging studies, most publications, however, report only on small samples. Addressing this challenge, we here report results of a structural imaging study on ECT patients that pooled patients from five German sites. Whole-brain voxel-based morphometry (VBM) analysis was performed to detect structural differences in 85 patients with unipolar depression before and after ECT, when compared to 86 healthy controls. Both task-independent and task-dependent physiological whole-brain functional connectivity patterns of these regions were modeled using additional data from healthy subjects. All emerging regions were additionally functionally characterized using the BrainMap database. Our VBM analysis detected a significant increase of GMV in the right hippocampus/amygdala region in patients after ECT compared to healthy controls. In healthy subjects this region was found to be enrolled in a network associated with emotional processing and memory. A region in the left fusiform gyrus was additionally found to have higher GMV in controls when compared with patients at baseline. This region showed minor changes after ECT. Our data points to a GMV increase in patients post ECT in regions that seem to constitute a hub of an emotion processing network. This appears as a plausible antidepressant mechanism and could explain the efficacy of ECT not only in the treatment of unipolar depression, but also of affective symptoms across heterogeneous disorders