There are about 6915 clinical studies being (or have been) conducted in Austria. The country of the clinical trial is determined by the location of where the clinical research is being studied. Most studies are often held in multiple locations & countries.
This is an online survey in Austria and Germany directed at parents with children born since the start of the first lockdown of the COVID-19 pandemic (birthdate beginning with 16.03.2020). The survey includes questions about: - current stress levels and depressive symptoms, - resilience during the pandemic, - social support, - retrospective birth risk factors, pregnancy distress and pregnancy experience, - demographic factors and - other questions related to parenting and the COVID-19 pandemic.
contextflow DETECT Lung CT is a Artificial Intelligence (AI)-based computed-aided detection (CADe) system, intended to support radiologists in the detection of lung nodules in chest computed tomography (CT) scans. System is intended to be used as a second-reader, therefore results provided by the software are meant to complement the radiologist's findings and decisions. Proposed study will be multi-reader, multi case (MRMC) retrospective reader study. The goal of the study is to evaluate the influence of CADe on the effectiveness of lung nodule detection. During the study, 10 radiologists will analyze 350 chest CT scans of adult patients, with and without the assistance of CADe. The study will be conducted remotely. CT scans will be uploaded to a web-based image submission and annotation platform, in which every participant of the study will be provided with individual account and assigned task list. The primary objective of the study determine if the diagnostic accuracy of radiologists with CADe assistance is superior to the diagnostic accuracy of radiologists without CADe assistance in localizing the pulmonary nodules with enhanced area under the free-response operating characteristic curve (AUC of FROC). The study will target approximately 350 asymptomatic adult patients, whose CT scans were acquired during routine CT examination. The patient population will include patients with and without lung nodules.
Since May 2019, psychocardiological rehabilitation has been carried out at the Rehabilitation Center Felbring (RFE) in the form of a pilot project. The background is the mutual relationship of psychological and physical morbidity, which is of particular importance in cardiological rehabilitation. The present outcome evaluation study is designed as a quantitative longitudinal study with 4 repeated measures, in which at least 75 rehabilitation patients will be included. Three assessments are conducted at admission and discharge to/from inpatient rehabilitation, and an additional survey will be conducted by mail 6 months after the end of rehabilitation. Effects that become apparent as a result of rehabilitation will be recorded from a patient-centered perspective by means of "patient-reported outcomes". In this way, primarily psychological and work-related changes, but also changes in the physical quality of life are to be mapped, which can be determined immediately after completion of rehabilitation and continue in the medium term up to 6 months later.
In the emergency department, the urgency for treating patients is determined according to the Manchester Triage System. The parameters collected in this process are deterministically translated into a treatment priority. The Manchester Triage System (MTS), which has been in use for at least 20 years, is a widely used, validated and standardized procedure for initial assessment in the emergency department - this initial assessment (triage) is done to prioritize medical assistance at a central point. Especially in emergency situations, critically endangered patients often require the deployment of a large part of the available staff at the same time - the medically correct triage of patients according to objective criteria in order to enable an adequate allocation of the available resources at the right time is the main objective. In the optimal case, each patient is treated by medical professionals within the time frame that is adequate for his/her health condition. Using artificial intelligence methods, it may be possible to increase the accuracy of treatment priority assignment. In the best case, incorrect prioritization of patients can be prevented and medical care can be ensured for those patients who actually need it most urgently. However, initial assessment, even if standardized and validated, still runs under limited resource conditions - time, space, material and personnel. Last but not least, the very idea of conducting an initial assessment limits its validity, and the results of the allocation fluctuate according to current research, although the determinants of this are currently unknown.
A large number of different organ functions are recorded in real time for patients who are monitored in an intensive care unit. On the one hand, the measured values collected in this way are used for continuous monitoring of vital parameters, but they are also evaluated several times a day in order to be able to make decisions regarding further diagnostics and therapy. In the first case, threshold values can be defined, and if these are exceeded or fallen short of, the treatment team is automatically alerted. If these limits are set too liberally, then the alert will only indicate an acute risk to the patient, where extensive pathophysiological changes have already occurred. If the limits are chosen too restrictively, then there are frequent false alarms, since the limits are exceeded in most cases due to natural fluctuation, without this having any pathological value. The consequence is a so-called "alarm fatigue", which in the worst case leads to ignoring correct alarms and thus endangers the patients. By design, all of these readings only show the status quo of a patient. It is the task of the treatment team to predict from the course of these readings whether a threatening situation is developing for the patient. For daily clinical practice, it would be better if dangerous changes in vital signs could be predicted. In this case, it would be possible to intervene therapeutically not only when a dangerous situation has arisen, but to try to avert this situation through adequate measures by changing the therapy strategy. In such a case, the treatment team would no longer be confronted with emergency alarms, but could counteract an impending deterioration with a long lead time. The first approaches for detecting a drop in blood pressure, for example, which are based on simple models, are already in clinical use.
This clinical trial will investigate the effectiveness and safety of a new active ingredient (LEO 138559) in the treatment of moderate to severe atopic dermatitis (AD). It is given by subcutaneous injection. Some people in the trial will instead receive Dupixent® which is an approved treatment for moderate to severe AD. Dupixent® is also given by subcutaneous injection. The main aim of this clinical trial is to investigate which changes in biomarkers in the skin are caused by LEO 138559 and Dupixent®. The trial includes a screening phase of up to 4 weeks, followed by a treatment period of 16 weeks, and a safety follow-up period of 16 weeks.
Currently, about 350000 red blood cell concentrates are produced from blood donations in Austria every year. In addition to the main effect of replacing lost blood, red blood cell concentrates also have many undesirable effects - from blood group compatibilities, which are easily avoidable due to care, to storage-related side effects, to mostly intensive care problems as a result of massive transfusions, to system-wide effects such as TRALI, TACO and TRIM. Before being administered to patients, red blood cell concentrates undergo an extensive quality assurance process in which a large number of parameters are collected. Prior to use on patients, for example, bedside tests and tests for further incompatibilities with a blood sample from the intended patient are performed. With the implementation of Patient Blood Management (PBM) in recent years, the use of red cell concentrates has become more targeted - the number of transfusions is decreasing in most developed countries. However, it is still possible to suffer transfusion-related adverse events (TRAE). Thus, active research activity to reduce these TRAEs continues to be called for. To date, however, it is not known which patients experience transfusion-related adverse events. Despite the broad measures of hemovigilance and pre-transfusion testing, it is still not possible to predict which individual patient will respond to a transfusion with a typical adverse event such as hypotension, hemolysis, renal failure, or TRALI. It seems understandable that characteristics of the patient as well as characteristics of the administered unit could play a role for this. In particular, it is conceivable that a combination of characteristics of the blood unit and characteristics of the patient could determine a complication in the course of administration. For this reason, it seems attractive to use artificial intelligence and machine learning methods to predict any complications.
Intrahospital cardiovascular arrest is one of the most common causes of death in hospitalized patients. In contrast to extramural cases of cardiovascular arrest, hospitalized patients often have severe medical conditions that can affect the outcome of resuscitation. Nevertheless, survival rates from resuscitation are better in hospitals than outside, because there is often a rapid start of resuscitation measures and predefined resuscitation standards. Regular CPR training and the availability of defibrillators in all bedside units can also positively influence outcome. Despite these many efforts, survival rates, especially of patients with good neurological outcome, remained stable at low levels even within hospitals in recent years and did not improve. Most outcome parameters are nowadays well known. (e.g., initial rhythm, age, early defibrillation, etc.) Nevertheless, we still do not know today how relevant the corresponding factors actually are, especially in relation to each other. One approach to this might be machine learning methods such as "random forest", which might be able to create a predictive model. However, this has not been attempted to date. The hypothesis of this work is to find out if it is possible to accurately predict the probability of surviving an in-hospital resuscitation using the machine learning method "random forest" and if particularly relevant outcome parameters can be identified. Design: retrospective data analysis of all data sets recorded in the resuscitation register of Kepler University Hospital. Measures and Procedure: Review of the registry for missing data as well as false alarms of the CPR team and, if necessary, exclusion of these data sets; evaluation of the data sets using the machine learning method random forest.
This study will evaluate the safety, tolerability, and immunogenicity of a pneumococcal 21-valent conjugate vaccine (V116) in pneumococcal vaccine-naïve adults 18 to 49 years of age. The primary study hypothesis is that all 3 lots of V116 are equivalent as assessed by the serotype-specific opsonophagocytic activity (OPA) Geometric Mean Titers (GMTs) at 30 days postvaccination for all serotypes included in V116.
Ocular surface photography is significantly limited in standardization and reproducibility. This reduces its applicability for clinical monitoring of acute or chronic disease. The innovative lens and illumination design of the CDL system aims to yield standardized high resolution photographs of the cornea and conjunctiva as required for clinical documentation, posing a significant clinical benefit of health care providers in the field of ophthalmology. Primary objectives: The primary objective of this study is to test the safety and feasibility of the CDL imaging system in a clinical routine setting. This will include the comparison of subjective contrast sensitivity testing post imaging, and the measurement of examination duration per imaging session, and the comparison of image lightness in mesopic versus photopic imaging. Secondary objectives: The secondary objective of this study is to compare the image quality of the device and repeatability of lateral resolution, dynamic range, hue, saturation, lightness, and image position between colour photographs from a state-of the art slit lamp camera and the CDL system. This is a monocentric, prospective, observational study. Patients with ocular surface disease of variable aetiology routinely assigned to ocular surface photography, following informed consent, will be imaged using state-of-the-art colour photography and the CDL imaging system. Pictures of each patient will be taken under several standardized conditions with both methods, subsequently analysed and compared by a Medical Image Processing Specialist.