Real-world treatment patterns in patients initiating third-line therapy for relapsed or refractory multiple myeloma in Germany, Italy, the United Kingdom, France, and Spain.

Objectives: To retrospectively analyze real-world treatment patterns in patients with relapsed/refractory multiple myeloma (RRMM) who initiated third-line treatment in Europe.

Methods: German and Italian administrative claims data were sourced from the German AOK PLUS health insurance fund and Italian local health units (2016-2020). Data for the United Kingdom (UK), France, and Spain were sourced from medical chart reviews (MCRs) from 2016 to 2018 (historical) and 2019 to 2021 (new) using electronic case report forms.

Secondary data for global health digitalisation.

Substantial opportunities for global health intelligence and research arise from the combined and optimised use of secondary data within data ecosystems. Secondary data are information being used for purposes other than those intended when they were collected. These data can be gathered from sources on the verge of widespread use such as the internet, wearables, mobile phone apps, electronic health records, or genome sequencing.

Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review.

Background: The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important.

Fast Healthcare Interoperability Resources (FHIR) in a FAIR Metadata Registry for COVID-19 Research.

Adopting international standards within health research communities can elevate data FAIRness and widen analysis possibilities. The purpose of this study was to evaluate the mapping feasibility against HL7® Fast Healthcare Interoperability Resources® (FHIR)® of a generic metadata schema (MDS) created for a central search hub gathering COVID-19 health research (studies, questionnaires, documents = MDS resource types). Mapping results were rated by calculating the percentage of FHIR coverage.

Evaluating Suitability of SNOMED CT in Structured Searches for COVID-19 Studies.

Studies investigating the suitability of SNOMED CT in COVID-19 datasets are still scarce. The purpose of this study was to evaluate the suitability of SNOMED CT for structured searches of COVID-19 studies, using the German Corona Consensus Dataset (GECCO) as example. Suitability of the international standard SNOMED CT was measured with the scoring system ISO/TS 21564, and intercoder reliability of two independent mapping specialists was evaluated.

The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond.

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed.

A Secure Multi-Party Computation Protocol for Time-To-Event Analyses.

The cryptographic method Secure Multi-Party Computation (SMPC) could facilitate data sharing between health institutions by making it possible to perform analyses on a "virtual data pool", providing an integrated view of data that is actually distributed - without any of the participants having to disclose their private data. One drawback of SMPC is that specific cryptographic protocols have to be developed for every type of analysis that is to be performed. Moreover, these protocols have to be optimized to provide acceptable execution times.

The use of machine learning in rare diseases: a scoping review.

Background: Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.

The Use of FHIR in Digital Health - A Review of the Scientific Literature.

Fast Healthcare Interoperability Resources (FHIR), an international standard for exchanging digital health data, is increasingly used in health information technology. FHIR promises to facilitate the use of electronic health records (EHRs), enable mobile technologies and make health data accessible to large-scale analytics. Until now, there is no comprehensive review of scientific articles about FHIR and its use in digital health.

Why digital medicine depends on interoperability.

Digital data are anticipated to transform medicine. However, most of today's medical data lack interoperability: hidden in isolated databases, incompatible systems and proprietary software, the data are difficult to exchange, analyze, and interpret. This slows down medical progress, as technologies that rely on these data - artificial intelligence, big data or mobile applications - cannot be used to their full potential.

Comparing Two Standardized Value Sets of Infectious Agents: Implications for Semantic Interoperability.

Infections are a global public health concern. For coordinated actions against infectious diseases, semantic interoperability between infection control systems is crucial. This requires a consistent use of standard terminologies such as SNOMED CT.

Cost associated with hematopoietic stem cell transplantation: a retrospective claims data analysis in Germany.

Aim: Quantify hematopoietic stem cell transplantation (HSCT) costs in German patients with acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL).

Methods: The primary outcome was direct and indirect costs in patients with ALL/DLBCL/FL who received HSCT between 2010 and 2014. Costs were evaluated two to four quarters before to eight quarters after HSCT.

German claims data analysis to assess impact of different intraocular lenses on posterior capsule opacification and related healthcare costs.

Aim: Cataract extraction is one of the most frequent surgeries in Germany. In most cases, the clouded natural lens is replaced by a hydrophobic or hydrophilic acrylic intraocular lens (IOL) implant. The most common long-term complication after cataract surgery is the development of a posterior capsule opacification (PCO).

Cost effects of preterm birth: a comparison of health care costs associated with early preterm, late preterm, and full-term birth in the first 3 years after birth.

Preterm birth is one of the main causes for infant morbidity and mortality. Apart from negative health outcomes, preterm birth also produces significant health care expenditures. This study evaluates the costs associated with preterm birth in different health sectors during the first 3 years of infants' lives.

Reading a suspenseful literary text activates brain areas related to social cognition and predictive inference.

Stories can elicit powerful emotions. A key emotional response to narrative plots (e.g.

Toward a general psychological model of tension and suspense.

Tension and suspense are powerful emotional experiences that occur in a wide variety of contexts (e.g., in music, film, literature, and everyday life).

Tension-related activity in the orbitofrontal cortex and amygdala: an fMRI study with music.

Tonal music is characterized by a continuous flow of tension and resolution. This flow of tension and resolution is closely related to processes of expectancy and prediction and is a key mediator of music-evoked emotions. However, the neural correlates of subjectively experienced tension and resolution have not yet been investigated.

A Dry EEG-System for Scientific Research and Brain-Computer Interfaces.

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients.