Smart hospital: achieving interoperability and raw data collection from medical devices in clinical routine.

Introduction: Today, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures-Especially in Germany-is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine. Methods: The CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces. Results: In the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations. Discussion: Fully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR.

Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge.

Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset. A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics. In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i.e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https://adam.isi.uu.nl/.

Imaging-Based 4D Aortic Pressure Mapping in Marfan Syndrome Patients: A Matched Case-Control Study.

BACKGROUND: Marfan syndrome predisposes to aortic aneurysm, dissection, and rupture. We sought to investigate aortic 4-dimensional (4D) relative pressure maps derived from 4D flow cardiovascular magnetic resonance to identify disease characteristic alterations of the intraaortic pressure field in Marfan patients with aortic root dilation compared with age- and sex-matched healthy controls. METHODS: This prospective case-control study included 11 Marfan patients with aortic root dilation (31 ± 5 years, 5 female) and 11 age- and sex-matched healthy controls (31 ± 8 years, 5 female) undergoing 4D flow cardiovascular magnetic resonance of the thoracic aorta. 4D relative pressure maps were computed and compared between groups for 8 aortic regions. RESULTS: Aortic root diameters were significantly larger in patients compared with controls (43 vs 31 mm, P < .001), but not in the proximal descending aorta (23 vs 21 mm, P = .19). Regional pressure gradients over the cardiac cycle were significantly altered in Marfan patients with significantly higher minimum pressure gradients in the proximal ascending aorta (-44.3 vs -97.0 mm Hg/m, P < .001) and significantly lower maximum pressure gradients in the proximal descending aorta (55.1 vs 82.3 mm Hg/m, P < .01). The latter finding was associated with pathologic vortical flow patterns. Regional pressure gradient at mid systole significantly correlated with aortic diameter (proximal ascending aorta: r = 0.73, P < .001; proximal descending aorta: r = -0.59, P = .004). CONCLUSIONS: Noninvasive 4D pressure mapping derived from 4D flow cardiovascular magnetic resonance revealed significant alterations of spatiotemporal pressure characteristics in the thoracic aorta of Marfan patients. These alterations were most pronounced in the proximal ascending aorta and the proximal descending aorta, corresponding to the regions where aortic dissections often originate in Marfan patients.