RD-MON - Building a Rare Disease Monitor to Enhance Awareness for Patients with Rare Diseases in Intensive Care.

Rare diseases are commonly defined by an incidence of less than 5/10000 inhabitants. There are some 8000 different rare diseases known. So even if a single rare disease is seldom, together they pose a relevant problem for diagnosis and treatment. This is especially true if a patient is treated for another common disease. University hospital of Gießen is part of the CORD-MI Project on rare diseases within the German Medical Informatics Initiative (MII) and a member of the MIRACUM consortium within the MII. As part of the ongoing Development for a clinical research study monitor within the use case 1 of MIRACUM, the study monitor has been configured to detect patients with rare diseases during their routine clinical encounters. The goal was to send a documentation request to the corresponding patient chart within the patient data management system for extended disease documentation to enhance clinical awareness for the patients' potential problems. The project was started in late 2022 and has so far been successfully tuned to detect patients with Mucoviscidosis and place notifications within the patient chart of the patient data management system (PDMS) on intensive care units.

Pulmonary Vascular Research Institute GoDeep: A meta-registry merging deep phenotyping datafrom international PH reference centers.

The Pulmonary Vascular Research Institute GoDeep meta-registry is a collaboration of pulmonary hypertension (PH) reference centers across the globe. Merging worldwide PH data in a central meta-registry to allow advanced analysis of the heterogeneity of PH and its groups/subgroups on a worldwide geographical, ethnical, and etiological landscape (ClinTrial. gov NCT05329714). Retrospective and prospective PH patient data (diagnosis based on catheterization; individuals with exclusion of PH are included as a comparator group) are mapped to a common clinical parameter set of more than 350 items, anonymized and electronically exported to a central server. Use and access is decided by the GoDeep steering board, where each center has one vote. As of April 2022, GoDeep comprised 15,742 individuals with 1.9 million data points from eight PH centers. Geographic distribution comprises 3990 enrollees (25%) from America and 11,752 (75%) from Europe. Eighty-nine perecent were diagnosed with PH and 11% were classified as not PH and provided a comparator group. The retrospective observation period is an average of 3.5 years (standard error of the mean 0.04), with 1159 PH patients followed for over 10 years. Pulmonary arterial hypertension represents the largest PH group (42.6%), followed by Group 2 (21.7%), Group 3 (17.3%), Group 4 (15.2%), and Group 5 (3.3%). The age distribution spans several decades, with patients 60 years or older comprising 60%. The majority of patients met an intermediate risk profile upon diagnosis. Data entry from a further six centers is ongoing, and negotiations with >10 centers worldwide have commenced. Using electronic interface-based automated retrospective and prospective data transfer, GoDeep aims to provide in-depth epidemiological and etiological understanding of PH and its various groups/subgroups on a global scale, offering insights for improved management.

Building a Comprehensive Clinical Data Repository Using FHIR, LOINC and SNOMED.

In 2018 the University Hospital of Giessen (UHG) moved its hospital information system from an in-house solution to commercial software. The introduction of MEONA and Synedra-AIM allowed for the successful migration of clinical documents. The large pool of structured clinical data has been addressed in a second step and is now consolidated in a HAPI-FHIR server and mapped to LOINC and SNOMED for semantic interoperability in multicenter research projects, especially the German Medical Informatics Initiative (MII) and the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium.

Challenges and Opportunities in Changing Data Structures of Clinical Document Archives from HL7-V2 to FHIR-Based Archive Solutions.

In 2018, a major replacement of clinical applications took place at the University Hospital of Giessen. One key part was the clinical document archive containing a vast collection of clinical data from the last 30 years. The aim of this sub-project was to move all data to a new system without any loss, while maintaining all functionality and all communication interfaces. This project successively resulted in a complete paradigm change in document storage. While the legacy clinical data repository (LCDR) was designed according to HL7-V2 principles, the replacement resulted in an HL7-FHIR implementation. The aim of this work is to discuss the differences between both approaches, the obstacles that appeared during migration, but also the opportunities resulting from the new philosophy, especially as far as the impact on the use of scientific data is concerned.

MIRACUM: Medical Informatics in Research and Care in University Medicine.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. OBJECTIVES: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. GOVERNANCE AND POLICIES: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. ARCHITECTURAL FRAMEWORK AND METHODOLOGY: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. USE CASES: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. RESULTS: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. DISCUSSION: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

Realizing a Realtime Shared Patient Chart using a Universal Message Forwarding Architecture.

The goal of this paper is to describe the clinical needs and the informational methodology which led to the realization of a realtime shared patient chart. It is an integral part of the communications infrastructure of the Patient Data Management System (PDMS) ICUData which is in routine use at the intensive care unit (ICU) of the Department for Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. ICUData utilizes a four tier system architecture consisting of modular clients, message forwarders, application servers and a relational database management system. All layers communicate with health level seven messages. The innovative aspect of this architecture consists of the interposition of a message forwarder layer which allows for instant exchange of patient data between the clients without delays caused by database access. This works even in situations with high workload as in patient monitoring. Therefore a system with many workstations acts a blackboard for patient data allowing shared access under realtime conditions. Realized first as an experimental feature, it has been embraced by the clinical users and served well during the documentation of more than 18000 patient stays.

A data model for managing drug therapy within a patient data management system for intensive care units.

The major intent of this article was to describe the design principles of the drug-therapy documentation module of the Patient Data Management System (PDMS) ICUData, in routine use at the intensive care unit (ICU) of the Department of Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. The new drug management system has been in routine use since March 2000. Until 8 January 2001, 1140 patients have been documented using this approach. It could be demonstrated that it was possible to transform the formerly unstructured text-based documentation into a detailed and structured model. The mediated benefit resulted in the automatic calculation of fluid balance. Further, detailed statistical analyses of therapeutic behavior in drug administration are now possible.

Discriminative power on mortality of a modified Sequential Organ Failure Assessment score for complete automatic computation in an operative intensive care unit.

OBJECTIVE: To evaluate the discriminative power on mortality of a modified Sequential Organ Failure Assessment (SOFA) score and derived measures (maximum SOFA, total maximum SOFA, and delta SOFA) for complete automatic computation in an operative intensive care unit (ICU). DESIGN: Retrospective study. SETTING: Operative ICU of the Department of Anesthesiology and Intensive Care Medicine. PATIENTS: Patients admitted to the ICU from April 1, 1999, to March 31, 2000 (n = 524). Data from patients under the age of 18 yrs and patients who stayed <24 hrs were excluded. In the case of patient readmittance, only data from the patient's last stay was included in the study. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main outcome measure was survival status at ICU discharge. Based on Structured Query Language (SQL) scripts, a modified SOFA score for all patients who stayed in the ICU in 1 yr was calculated for each day in the ICU. Only routine data were used, which were supplied by the patient data management system. Score evaluation was modified in registering unavailable data as being not pathologic and in using a surrogate of the Glasgow Coma Scale. During the first 24 hrs, 459 survivors had an average SOFA score of 4.5 +/- 2.1, whereas the 65 deceased patients averaged 7.6 +/- 2.9 points. The area under the receiver operating characteristic (ROC) curve was 0.799 and significantly >0.5 (p <.01). A confidence interval (CI) of 95% covers the area (0.739-0.858). The maximum SOFA presented an area under the ROC of 0.922 (CI: 0.879-0.966), the total maximum SOFA of 0.921 (CI: 0.882-0.960), and the delta SOFA of 0.828 (CI: 0.763-0.893). CONCLUSION: Despite a number of differences between completely automated data sampling of SOFA score values and manual evaluation, the technique used in this study seems to be suitable for prognosis of the mortality rate during a patient's stay at an operative ICU.