Moritz Lehne

Background: Sickle cell disease (SCD) is a rare inherited hemoglobinopathy associated with substantial morbidity and high healthcare needs. In Germany, population-based evidence on SCD epidemiology and real-world care patterns remains limited, despite increasing relevance in the context of migration and the introduction of nationwide newborn screening in 2021. Methods: We conducted a retrospective, non-interventional study using anonymized German statutory health insurance claims data from the InGef database (2015–2024). Patients with SCD were identified using ICD-10-GM codes D57.0, D57.1, D57.2, and D57.8 based on inpatient and confirmed outpatient diagnoses. Administrative prevalence was estimated using a carry-forward cohort approach, whereby SCD patients diagnosed in the current year and alive/observable in subsequent years were counted as prevalent. Administrative incidence was assessed in children aged < 6 years using the first recorded SCD diagnosis in claims data as the date of incidence. Analyses of patient characteristics, clinical outcomes, treatments and healthcare resource use (HCRU) were conducted cross-sectionally by calendar year, stratified by age group and sex. Results: Across 2015–2024, 1,019 individuals with SCD were identified. As might be expected using a carry forward cohort approach, annual prevalent counts increased from 225 (2015) to 864 (2024), and nationally extrapolated prevalence increased from 2.75 per 100,000 in 2015 to 10.50 per 100,000 in 2024. Median time to recorded diagnosis in children < 6 years decreased from 31 months (2015) to 11 months from 2021 onwards (2021–2024). In 2024, infections were the most frequently documented clinical outcome (34.5% [298/864]), followed by vaso‑occlusive crises (19.2% [166/864]) and mental health comorbidities (depression 19.1% [165/864]; anxiety 9.1% [79/864]). The proportions of patients with inpatient or outpatient visits, and with medication prescriptions (e.g. for antibiotics, antirheumatics, hydroxyurea), decreased over time. Conclusion: This claims-based analysis indicates increasing documented SCD prevalence in Germany. However, the extent to which prevalence increased due to a true increase in the general population, due to our carry-forward cohort approach, or due to other factors such as changes in coding practices or healthcare seeking behaviors, cannot be disentangled.We observed earlier recorded diagnosis timing in young children over time, alongside substantial age-dependent burden reflected in complications, mental health comorbidity, and healthcare utilization. Possible explanations for lower proportions of patients with inpatient/outpatient visits and medication prescriptions over time may include changes in disease awareness, patient management and/or healthcare seeking behaviours over time, and/or broader capture of patients with milder disease. Overall, our findings can inform healthcare planning and structured life-course–oriented SCD care pathways in Germany.

Aim: This study aimed to compare the safety and effectiveness of direct oral anticoagulants (DOACs) with low-molecular-weight heparin (LMWH) in patients with venous thromboembolism (VTE) and active cancer in a real-world setting in Sweden, Norway, Finland, the UK and Germany. Materials & methods: This observational cohort study used datasets from Sweden, Norway, Finland (National Patient and Prescription Registers) the UK (Clinical Practice Research Datalink and Hospital Episode Statistics) and Germany (AOK Plus and GWQ) from 2013 to 2020. We identified treatment-naive adult patients with cancer-related VTE treated with a DOAC or LMWH. We employed inverse probability of treatment weighting for each DOAC-LMWH comparison and assessed recurrent VTE and bleeding risk (overall and by site: gastrointestinal [GI], intracranial hemorrhage [ICH] or other) within 6 months of treatment initiation. Fine–Gray models were fitted to estimate adjusted hazard ratios and country level estimates were meta-analyzed. Results: After inverse probability of treatment weighting, 30,002 and 2892 patients were included in the LMWH-apixaban comparison, and 29,976 and 4321 in the LMWH-rivaroxaban comparison, respectively. Recurrent VTE could not be assessed comparatively because of low numbers. At 6 months, apixaban was associated with a lower risk of overall and other (HR: 0.67 [95% CI: 0.53,0.86]; 0.64 [0.41,0.998]) bleeding compared with LMWH, with similar risks for GI and ICH bleeding (0.89 [0.61,1.29]; 0.67 [0.31,1.44], respectively). Rivaroxaban had a similar risk of overall, GI, and other bleeding (0.98 [0.83, 1.15]; 0.86 [0.47,1.58]; 0.89 [0.74,1.08]) compared with LMWH, but a lower risk of ICH (0.32 [0.13, 0.82]). Conclusion: These findings suggest that apixaban may offer a safer bleeding profile than LMWH for patients with cancer-associated VTE, particularly in reducing overall and other types of bleeding. Rivaroxaban also appears to be a viable alternative to LMWH, with a notably lower risk of ICH. These results provide valuable real-world insights in an area where evidence remains very limited and support the role of DOACs as a safe alternative to LMWH in patients with VTE and cancer.

Purpose To assess the epidemiology, treatment patterns, and Bacillus Calmette–Guérin (BCG) instillations in Non-Muscle-Invasive Bladder Cancer (NMIBC) patients to evaluate guidelines compliance and effectiveness of adherence on real-world outcomes. Methods NMIBC patients with BCG-treatment were identified in a retrospective cohort study using German health claims data. The first BCG prescription was defined as index date. Crude and standardized annual incidence were calculated. Subsequent treatment regimens were described based on prescriptions to assess adherence. BCG-maintenance adherents were compared with non-adherents using a landmark time approach and Cox proportional hazard model. Results The annual standardized incidence per 100,000 for intermediate- and high-risk NMIBC patients ranged from 7.6 to 10.4 in males and 1.2 to 1.9 in females from 2017 to 2021. The main cohort consisted of 817 patients (mean age: 72.5; male: 78%). Most patients (98%) received transurethral resection of bladder tumor (TURBT) as initial treatment followed by BCG, with a median number of 13 BCG instillations per patient (IQR: 8–21). 28% continued BCG in the maintenance phase at 24 months post-induction and 62% received at least one Re-TURBT post-index. BCG maintenance-adherent patients showed lower risk compared with non-adherent patients: adjusted hazard ratio (aHR): 0.92 [0.73–1.18] for all-cause death; aHR: 0.49 [0.36–0.67] for advanced treatment; aHR: 0.65 [0.53–0.80] for disease progression. Conclusion Real-world compliance with clinical guidelines may require improvement based on BCG maintenance duration and adherence to treatment schedules. The benefits of BCG adherence in improving oncology-relevant outcomes support guidelines recommendations for sufficient BCG maintenance.

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.

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.

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.

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.

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 segmentation 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. 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, anamnesis, 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.

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.