Long-Term Observation of Patients With Cancer - an Entity-Independent Registry for Healthcare and Translational Research at the University Medicine Dresden (Cancer-Reg-VT). / Langzeitbeobachtung von Patienten mit Krebserkrankungen ­ ein entitätsunabhängiges Register für Versorgungsforschung und translationale Forschung an der Universitätsmedizin Dresden (Cancer-Reg-VT).

INTRODUCTION: Translational research is important, especially in medicine where decisions affect people's lives. Clinical registries and the studies embedded in them allow the depiction of actual care practice under routine conditions. Translating the findings of health services research back into clinical research through prospective cohort studies has the potential to drive medical innovations faster, more effectively and, above all, in a more targeted manner. These must therefore be a central component of cutting-edge oncological research. OBJECTIVE: The aim of the registry is the establishment of clinical cohorts and the provision of a comprehensive, high-quality data set for oncological diseases. METHODS/DESIGN: The registry will prospectively record all patients treated for cancer at Dresden University Hospital (UKD). In addition to the data from the hospital information systems (ORBIS, TDS, GEPADO, etc.), monitoring of health-related quality of life (HRQOL) is to be carried out at regular intervals at the beginning and during the course of treatment. In addition, individual linkage with data from clinical cancer registries and health insurance companies (including AOK PLUS) is planned for a period of five years before and after inclusion. All these data will be merged in a registry database. The selection of variables and measurement time points is closely based on the guidelines for colorectal carcinoma of the international initiative ICHOM (International Consortium for Health Outcomes Measurement). The study management software (STeVe) separates personal identification characteristics (IDAT) and medical data (MDAT) at an early stage. The independent trust centre of the TU Dresden (Treuhandstelle) ensures that no personal data enter the registry database. It is thereby also ensured that the data owners involved (UKD, biobank, health insurance company, cancer registry, patient) only receive the personal data they need for allocation. The MOSAIC software tools recommended by the TMF (Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V.) are used to manage the pseudonyms. DISCUSSION/CONCLUSION: With the registry, previously missing evidence on the effectiveness, safety and costs of diagnostic and therapeutic measures can be made, taking into account long-term and patient-reported outcomes of routine care. The data potentially allow for the identification of barriers to and facilitators of innovative promising cancer diagnostics and therapies. They also enable generation of scientifically relevant hypotheses in the field of translational and outcomes research.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.