Publications by authors named "F Scheibe"
Objective: In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP).
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- - Myasthenia gravis (MG) is a rare autoimmune disease leading to muscle weakness and potentially life-threatening myasthenic crises (MC) that require intensive care, but there are currently no established lab tests to predict disease progression in MG patients
- - A study analyzed lab parameters related to inflammation in 58 MG patients, finding that 15 experienced at least one MC, with no significant differences based on antibody status or sex
- - Results suggest that increased counts of basophils, neutrophils, leukocytes, and platelets may indicate a higher risk of myasthenic crisis, providing a possible method for assessing risk in MG patients
Article Synopsis
- - Autoimmune diseases are a group of disorders where the immune system mistakenly attacks the body, causing inflammation and affecting various organs; they can be divided into connective tissue diseases and vasculitides.
- - Patients with autoimmune diseases often require admission to the ICU due to complications like flare-ups, infections, and organ failure, which can lead to high mortality rates; managing these cases is notably complex and requires a team-based approach.
- - There is limited data on how to treat autoimmune patients in the ICU, indicating a need for collaborative research to enhance understanding and treatment effectiveness, as highlighted in this narrative review on severe systemic autoimmune diseases.
Article Synopsis
- - The study aims to evaluate the effectiveness of a standard operating procedure for atrial fibrillation (AF) alarms in clinical settings and to assess how well automated monitoring detects AF in patients with ischemic strokes at two hospitals in Berlin.
- - Researchers analyzed ECG data from 109 selected stroke patients who had AF alarms, categorizing the data into AF, non-AF, or artifacts to validate the alarms against patient histories and treatment plans.
- - The primary outcome was to measure the rate of unrecognized AF cases that the monitoring system identified but clinical teams missed, while secondary outcomes focused on potential undiagnosed AF leading to anticoagulant treatment.
Background: Post-stroke heart rate (HR) and heart rate variability (HRV) changes have been proposed as outcome predictors after stroke. We used data lake-enabled continuous electrocardiograms to assess post-stroke HR and HRV, and to determine the utility of HR and HRV to improve machine learning-based predictions of stroke outcome.
Methods: In this observational cohort study, we included stroke patients admitted to two stroke units in Berlin, Germany, between October 2020 and December 2021 with final diagnosis of acute ischemic stroke or acute intracranial hemorrhage and collected continuous ECG data through data warehousing.