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. RESULTS: Across all countries, immunomodulatory imide drug (IMiD)-based regimens were prominent in the third-line setting. From 2016 to 2020, lenalidomide-dexamethasone was most common in Italy (18.0%) and Germany (12.7%). From 2019 to 2021, the most common regimen was ixazomib-lenalidomide-dexamethasone (67.5%) in the UK, pomalidomide-dexamethasone (17.1%) in France, and daratumumab-bortezomib-dexamethasone (15.0%) in Spain. In the historical data (2016-2018), third-line lenalidomide- and pomalidomide-dexamethasone doublet use across the UK (>47%), France (>46%), and Spain (>33%) was high. From historical to new, triplet use increased in Spain (>19% to >60%) as did anti-CD38 agent use in France (15.1% to 51.9%) and Spain (19.7% to 42.1%). CONCLUSIONS: From 2016 to 2021, third-line regimens were mostly IMiD based. The MCR data demonstrated evolving treatment choices from 2016 to 2018 and 2019 to 2021, providing insights into uptake of novel agents and current RRMM European clinical practice.

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. To utilise their full potential, we offer guidance by outlining available sources and approaches for the processing of secondary data. Furthermore, in addition to indicators for the regulatory and ethical evaluation of strategies for the best use of secondary data, we also propose criteria for assessing reusability. This overview supports more precise and effective policy decision making leading to earlier detection and better prevention of emerging health threats than is currently the case.

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. FHIR provides solutions by offering resource domains such as "Public Health & Research" and "Evidence-Based Medicine" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research. OBJECTIVE: The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions. METHODS: We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures. RESULTS: We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain "Public Health & Research." Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server. CONCLUSIONS: Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research.

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. Among 86 items to map, total mapping coverage was 94%: 50 (58%) of the items were available as standard resources in FHIR and 31 (36%) could be mapped using extensions. Five items (6%) could not be mapped to FHIR. Analyzing each MDS resource type, there was a total mapping coverage of 93% for studies and 95% for questionnaires and documents, with 61% of the MDS items available as standard resources in FHIR for studies, 57% for questionnaires and 52% for documents. Extensions in studies, questionnaires and documents were used in 32%, 38% and 43% of items, respectively. This work shows that FHIR can be used as a standardized format in registries for clinical, epidemiological and public health research. However, further adjustments to the initial MDS are recommended - and two additional items even needed when implementing FHIR. Developing a MDS based on the FHIR standard could be a future approach to reduce data ambiguity and foster interoperability.

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 resulting analysis showed that the majority of data items had either a complete or partial equivalent in SNOMED CT (complete equivalent: 141 items; partial equivalent: 63 items; no equivalent: 1 item). Intercoder reliability was moderate, possibly due to non-establishment of mapping rules and high percentage (74%) of different but similar concepts among the 86 non-equal chosen concepts. The study shows that SNOMED CT can be utilized for COVID-19 cohort browsing. However, further studies investigating mapping rules and further international terminologies are necessary.

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. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine. METHODS: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats. RESULTS: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined. CONCLUSION: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

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.g., diagnosis, prognosis or treatment) are studied. METHODS: Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010-2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. RESULTS: Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and "omics" data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). CONCLUSION: Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

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. As a first step towards a library of efficient implementations of common methods in health data sciences, we present a novel protocol for efficient time-to-event analysis. Our implementation utilizes a common technique called garbled circuits and was implemented using a widespread SMPC programming framework. We further describe optimizations that we have developed to reduce the execution times of our protocol. We experimentally evaluated our solution by computing Kaplan-Meier estimators over a vertically distributed dataset while measuring performance. By comparing the SMPC results with a conventional analysis on pooled data, we show that our approach is practical and scalable.

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. Here, we aim to address this gap and provide an overview of the main topics associated with FHIR in the scientific literature. For this, we screened all articles about FHIR on Web of Science and PubMed and identified the main topics discussed in these articles. We also explored the temporal trend and geography of publications and performed some basic text mining on article abstracts. We found that the topics most commonly discussed in the articles were related to data models, mobile and web applications as well as medical devices. Since its introduction, the number of publications about FHIR have steadily increased until 2017, indicating an increasing popularity of FHIR in healthcare (in 2018, publication numbers remained stable). In sum, our study provides an overview of the scientific literature about FHIR and its current use in digital health.

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. Here, we compare two value sets of infectious agents annotated with SNOMED CT (WHONET 2018 vs. pathogens reported under the German Protection against Infection Act). Our comparison revealed several inconsistencies, highlighting the importance of the consistent and coordinated use of standard terminologies.

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. In this article, we argue that interoperability is a prerequisite for the digital innovations envisioned for future medicine. We focus on four areas where interoperable data and IT systems are particularly important: (1) artificial intelligence and big data; (2) medical communication; (3) research; and (4) international cooperation. We discuss how interoperability can facilitate digital transformation in these areas to improve the health and well-being of patients worldwide.

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. RESULTS: Among 258 patients with HSCT, direct costs were €290,125/patient (pediatric ALL), €246,266/patient (adult ALL), €230,399/patient (DLBCL/FL allogeneic) and €107,457/patient (DLBCL/FL autologous). Indirect costs with HSCT were €52,939/patient (adult ALL), €20,285/patient (DLBCL/FL allogeneic) and €29,881/patient (DLBCL/FL autologous). CONCLUSION: Direct and indirect costs associated with HSCT are substantial for patients with ALL, DLBCL and FL. Novel therapies that reduce HSCT use could reduce medical costs.

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). Although no precise real world data are available, published evidence suggests a lower risk for PCO development for hydrophobic acrylic IOLs compared to hydrophilic acrylic IOLs. Therefore, in the present study we assessed real world data on the impact of different IOL material types on the incidence of post-operative PCO treatment. SUBJECT AND METHODS: In this retrospective study, we included 3,025 patients who underwent cataract extraction and implantation of either an acrylic hydrophobic or hydrophilic IOL in 2010. We assessed clinical outcomes and direct costs in a 4-year follow-up period after cataract surgery from a statutory health insurance (SHI) perspective in Germany. RESULTS: PCO that required capsulotomies occurred significantly (p < 0.0001) less frequent in patients who had received a hydrophobic IOL (31.57% of 2,078 patients) compared to the group with hydrophilic IOL implants (56.6% of 947 patients) and costs per patient for postoperative treatment in a 4-year follow-up were 50.03 € vs. 87.81 € (i.e. 75% higher in the latter group, p < 0.0001). CONCLUSION: Considering the high prevalence of cataract, the economic burden associated with adverse effects of cataract extraction is of great relevance for the German SHI. Hydrophobic lenses seem to be superior regarding both medical and economic results.

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. In a retrospective observational study based on claims data from a German statutory health insurance company, average costs for medication, hospital treatment, ambulatory treatment, and non-medical remedies during the first 3 years after birth were analyzed for early preterm, late preterm, and full-term births. Costs associated with preterm births were generally higher than for full-term births, with the highest costs for the hospital treatment of early preterm births. Cost differences tended to decrease in the second and third year after birth except for ambulatory treatment costs, which decreased for late preterm and full-term births but not for early preterm births. The study shows that preterm birth is associated with increased health care costs, particularly during the first year after birth, indicating that the implementation of adequate programs and policies for preventing preterm birth is not only desirable from a medical but also from a health economic perspective.

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., novels, movies, etc.) is suspense. Suspense appears to build on basic aspects of human cognition such as processes of expectation, anticipation, and prediction. However, the neural processes underlying emotional experiences of suspense have not been previously investigated. We acquired functional magnetic resonance imaging (fMRI) data while participants read a suspenseful literary text (E.T.A. Hoffmann's "The Sandman") subdivided into short text passages. Individual ratings of experienced suspense obtained after each text passage were found to be related to activation in the medial frontal cortex, bilateral frontal regions (along the inferior frontal sulcus), lateral premotor cortex, as well as posterior temporal and temporo-parietal areas. The results indicate that the emotional experience of suspense depends on brain areas associated with social cognition and predictive inference.

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). The omnipresence of tension and suspense suggests that they build on very basic cognitive and affective mechanisms. However, the psychological underpinnings of tension experiences remain largely unexplained, and tension and suspense are rarely discussed from a general, domain-independent perspective. In this paper, we argue that tension experiences in different contexts (e.g., musical tension or suspense in a movie) build on the same underlying psychological processes. We discuss key components of tension experiences and propose a domain-independent model of tension and suspense. According to this model, tension experiences originate from states of conflict, instability, dissonance, or uncertainty that trigger predictive processes directed at future events of emotional significance. We also discuss possible neural mechanisms underlying tension and suspense. The model provides a theoretical framework that can inform future empirical research on tension phenomena.

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. We acquired continuous ratings of musical tension for four piano pieces. In a subsequent functional magnetic resonance imaging experiment, we identified blood oxygen level-dependent signal increases related to musical tension in the left lateral orbitofrontal cortex (pars orbitalis of the inferior frontal gyrus). In addition, a region of interest analysis in bilateral amygdala showed activation in the right superficial amygdala during periods of increasing tension (compared with decreasing tension). This is the first neuroimaging study investigating the time-varying changes of the emotional experience of musical tension, revealing brain activity in key areas of affective processing.

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. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.