[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.

BACKGROUND: In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. OBJECTIVE: Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. METHODS: In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. RESULTS: Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. CONCLUSIONS: The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

Transfer learning for medical image classification: a literature review.

BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. METHODS: 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. RESULTS: The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. CONCLUSION: The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review.

BACKGROUND: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. OBJECTIVE: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. METHODS: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. RESULTS: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. CONCLUSIONS: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

[Digital forms of service delivery for personalized crisis resolution and home treatment]. / Digitale Versorgungsformen zur Personalisierung der stationsäquivalenten Behandlung.

BACKGROUND: Ward-equivalent treatment (StäB), a form of crisis resolution and home treatment in Germany, has been introduced in 2018 as a new model of mental health service delivery for people with an indication for inpatient care. The rapid progress in the field of information and communication technology offers entirely new opportunities for innovative digital mental health care, such as telemedicine, eHealth, or mHealth interventions. OBJECTIVE: This review aims to provide a comprehensive overview of novel digital forms of service delivery that may contribute to a personalized delivery of StäB and improving clinical and social outcomes as well as reducing direct and indirect costs. METHOD: This work is based on a narrative review. RESULTS: Four primary digital forms of service delivery have been identified that can be used for personalized delivery of StäB: (1) communication, continuity of care, and flexibility through online chat and video call; (2) monitoring of symptoms and behavior in real-time through ecological momentary assessment (EMA); (3) use of multimodal EMA data to generate and offer personalized feedback on subjective experience and behavioral patterns as well as (4) adaptive ecological momentary interventions (EMI) tailored to the person, moment, and context in daily life. CONCLUSION: New digital forms of service delivery have considerable potential to increase the effectiveness and cost-effectiveness of crisis resolution, home treatment, and assertive outreach. An important next step is to model and initially evaluate these novel digital forms of service delivery in the context of StäB and carefully investigate their quality from the user perspective, safety, feasibility, initial process and outcome quality as well as barriers and facilitators of implementation.

Multiple breath washout (MBW) testing using sulfur hexafluoride: reference values and influence of anthropometric parameters.

BACKGROUND: Multiple breath washout (MBW) using sulfur hexafluoride (SF6) has the potential to reveal ventilation heterogeneity which is frequent in patients with obstructive lung disease and associated small airway dysfunction. However, reference data are scarce for this technique and mostly restricted to younger cohorts. We therefore set out to evaluate the influence of anthropometric parameters on SF6-MBW reference values in pulmonary healthy adults. METHODS: We evaluated cross-sectional data from 100 pulmonary healthy never-smokers and smokers (mean 51 (SD 20), range 20-88 years). Lung clearance index (LCI), acinar (Sacin) and conductive (Scond) ventilation heterogeneity were derived from triplicate SF6-MBW measurements. Global ventilation heterogeneity was calculated for the 2.5% (LCI2.5) and 5% (LCI5) stopping points. Upper limit of normal (ULN) was defined as the 95th percentile. RESULTS: Age was the only meaningful parameter influencing SF6-MBW parameters, explaining 47% (CI 33% to 59%) of the variance in LCI, 32% (CI 18% to 47%) in Sacin and 10% (CI 2% to 22%) in Scond. Mean LCI increases from 6.3 (ULN 7.4) to 8.8 (ULN 9.9) in subjects between 20 and 90 years. Smoking accounted for 2% (CI 0% to 8%) of the variability in LCI, 4% (CI 0% to 13%) in Sacin and 3% (CI 0% to 13%) in Scond. CONCLUSION: SF6-MBW outcome parameters showed an age-dependent increase from early adulthood to old age. The effect was most pronounced for global and acinar ventilation heterogeneity and smaller for conductive ventilation heterogeneity. No influence of height, weight and sex was seen. Reference values can now be provided for all important SF6-MBW outcome parameters over the whole age range. TRIAL REGISTRATION NUMBER: NCT04099225.

Prospective trial on telemonitoring of geriatric cancer patients using handheld devices.

PURPOSE AND OBJECTIVE: Randomized trials indicate that electronic or app-based assessment of patient-reported outcomes may improve outcomes in cancer patients. To analyze if an app-based follow-up would be accepted by elderly cancer patients, we conducted a single-center prospective feasibility study (NCT03196050). MATERIALS AND METHODS: Cancer patients (≥60 years) without concurrent uncontrolled severe medical conditions and a Karnofsky performance status (KPS) ≥70 were eligible if they were able to use the smartphone app. The primary endpoint was compliance over 1 year, calculated as patient-specific and study date-specific response rate to questions sent as push notifications; in this interim analysis, we report on 4­month data. Secondary outcomes included a comparison of a subjective health status item (SPHS) with the physician-rated KPS. RESULTS: Out of 225 patients screened, 54 patients agreed to participate and 29 activated the app and participated in the study. The mean age was 66 years (61-78). The individual compliance rate averaged at 58.3% (standard deviation SD = 35%). Daily compliance was 53.3% on average (SD = 10.8%) and declined over time. The average percentage of patients who sent answers at least weekly was 75.0% (SD = 14.8%) and declined from 100% in week 1 to 53.8% in week 17 post-enrollment. Secondary outcomes indicated that questionnaires such as the EORTC-QLQ-C30 are accepted via app and that there is a significant moderate correlation between the SPHS and KPS scores (r = 0.566; p < 0.001). CONCLUSION: Our data indicate that an app-based follow-up incorporating EORTC questionnaires might be possible in highly selected elderly cancer patients with modest compliance rates. Further trials should aim at an increased participation rate.

Digital Follow-Up and the Perspective of Patient-Centered Care in Oncology: What's the PROblem?

There is accumulating evidence from randomized trials suggesting that digital patient-centered care allows a more reliable detection of tumour-related symptoms and adverse events - with a direct impact on overall survival. Consequently, a variety of unsynchronized approaches were kicked off to (electronically) measure patient-reported outcomes (PROs). Despite increasing evidence that PRO data are highly relevant for patient care, the data generated in these initial projects lack standardized processing pathways in order to impact clinical routine; therefore, potential future routine PRO assessments require adequate analysis, storage and processing to allow a robust, reproducible and reliable incorporation into routine clinical decision-making. Here, we discuss relevant challenges of digital follow-up that need to be tackled to render PRO data as relevant to physicians as laboratory or biomarker data.

Towards a comprehensive and interoperable representation of consent-based data usage permissions in the German medical informatics initiative.

BACKGROUND: The aim of the German Medical Informatics Initiative is to establish a national infrastructure for integrating and sharing health data. To this, Data Integration Centers are set up at university medical centers, which address data harmonization, information security and data protection. To capture patient consent, a common informed consent template has been developed. It consists of different modules addressing permissions for using data and biosamples. On the technical level, a common digital representation of information from signed consent templates is needed. As the partners in the initiative are free to adopt different solutions for managing consent information (e.g. IHE BPPC or HL7 FHIR Consent Resources), we had to develop an interoperability layer. METHODS: First, we compiled an overview of data items required to reflect the information from the MII consent template as well as patient preferences and derived permissions. Next, we created entity-relationship diagrams to formally describe the conceptual data model underlying relevant items. We then compared this data model to conceptual models describing representations of consent information using different interoperability standards. We used the result of this comparison to derive an interoperable representation that can be mapped to common standards. RESULTS: The digital representation needs to capture the following information: (1) version of the consent, (2) consent status for each module, and (3) period of validity of the status. We found that there is no generally accepted solution to represent status information in a manner interoperable with all relevant standards. Hence, we developed a pragmatic solution, comprising codes which describe combinations of modules with a basic set of status labels. We propose to maintain these codes in a public registry called ART-DECOR. We present concrete technical implementations of our approach using HL7 FHIR and IHE BPPC which are also compatible with the open-source consent management software gICS. CONCLUSIONS: The proposed digital representation is (1) generic enough to capture relevant information from a wide range of consent documents and data use regulations and (2) interoperable with common technical standards. We plan to extend our model to include more fine-grained status codes and rules for automated access control.

Digital networks for laboratory data: potentials, barriers and current initiatives.

Medical care is increasingly delivered by multiple providers across healthcare sectors and specialties, leading to a fragmentation of the electronic patient record across organizations and vendor IT systems. The rapid uptake of wearables and connected diagnostic devices adds another source of densely collected data by the patients themselves. Integration of these data sources opens up several potentials: a longitudinal view of laboratory findings would close the gaps between individual provider visits and allow to more closely follow disease progression. Adding non-laboratory data (e.g. diagnoses, procedures) would add context and support clinical interpretation of findings. Case-based reasoning and disease-modelling approaches would allow to identify similar patient groups and classify endotypes. Realization of these potentials is, however, subject to several barriers, including legal and ethical prerequisites of data access, syntactic and semantic integration, comparability of items and user-centered presentation. The German Medical Informatics Initiative is presented as a current undertaking that strives to address these issues by establishing a national infrastructure for the secondary use of routine clinical data.

Indirect determination of hematology reference intervals in adult patients on Beckman Coulter UniCell DxH 800 and Abbott CELL-DYN Sapphire devices.

Background Conventional establishment of reference intervals for hematological analytes is challenging due to the need to recruit healthy persons. Indirect methods address this by deriving reference intervals from clinical laboratory databases which contain large datasets of both physiological and pathological test results. Methods We used the "Reference Limit Estimator" (RLE) to establish reference intervals for common hematology analytes in adults aged 18-60 years. One hundred and ninety-five samples from 44,519 patients, measured on two different devices in a tertiary care center were analyzed. We examined the influence of patient cohorts with an increasing proportion of abnormal test results, compared sample selection strategies, explored inter-device differences, and analyzed the stability of reference intervals in simulated datasets with varying overlap of pathological and physiological test results. Results Reference intervals for hemoglobin, hematocrit, red cell count and platelet count remained stable, even if large numbers of pathological samples were included. Reference intervals for red cell indices, red cell distribution width and leukocyte count were sufficiently stable, if patient cohorts with the highest fraction of pathological samples were excluded. In simulated datasets, estimated reference limits shifted, if the pathological dataset contributed more than 15%-20% of total samples and approximated the physiological distribution. Advanced sample selection techniques did not improve the algorithm's performance. Inter-device differences were small except for red cell distribution width. Conclusions The RLE is well-suited to create reference intervals from clinical laboratory databases even in the challenging setting of a adult tertiary care center. The procedure can be used as a complement for reference interval determination where conventional approaches are limited.

Feasibility analysis of conducting observational studies with the electronic health record.

BACKGROUND: The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes. METHODS: After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions "completeness", "correctness", "currency" and "granularity". RESULTS: Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n = 411, Study 2 n = 423, Study 3 n = 13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions. CONCLUSIONS: While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.

Computerized patient identification for the EMBRACA clinical trial using real-time data from the PRAEGNANT network for metastatic breast cancer patients.

As breast cancer is a diverse disease, clinical trials are becoming increasingly diversified and are consequently being conducted in very small subgroups of patients, making study recruitment increasingly difficult. The aim of this study was to assess the use of data from a remote data entry system that serves a large national registry for metastatic breast cancer. The PRAEGNANT network is a real-time registry with an integrated biomaterials bank that was designed as a scientific study and as a means of identifying patients who are eligible for clinical trials, based on clinical and molecular information. Here, we report on the automated use of the clinical data documented to identify patients for a clinical trial (EMBRACA) for patients with metastatic breast cancer. The patients' charts were assessed by two independent physicians involved in the clinical trial and also by a computer program that tested patients for eligibility using a structured query language script. In all, 326 patients from two study sites in the PRAEGNANT network were included in the analysis. Using expert assessment, 120 of the 326 patients (37 %) appeared to be eligible for inclusion in the EMBRACA study; with the computer algorithm assessment, a total of 129 appeared to be eligible. The sensitivity of the computer algorithm was 0.87 and its specificity was 0.88. Using computer-based identification of patients for clinical trials appears feasible. With the instrument's high specificity, its application in a large cohort of patients appears to be feasible, and the workload for reassessing the patients is limited.

Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting.

BACKGROUND: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems. METHODS: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project. RESULTS: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records. CONCLUSIONS: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Concept and implementation of a study dashboard module for a continuous monitoring of trial recruitment and documentation.

BACKGROUND: The difficulty of managing patient recruitment and documentation for clinical trials prompts a demand for instruments for closely monitoring these critical but unpredictable processes. Increasingly adopted Electronic Data Capture (EDC) applications provide novel opportunities to reutilize stored information for an efficient management of traceable trial workflows. In related clinical and administrative settings, so-called digital dashboards that continuously visualize time-dependent parameters have recently met a growing acceptance. To investigate the technical feasibility of a study dashboard for monitoring the progress of patient recruitment and trial documentation, we set out to develop a propositional prototype in the form of a separate software module. METHODS: After narrowing down functional requirements in semi-structured interviews with study coordinators, we analyzed available interfaces of a locally deployed EDC application, and designed the prototypical study dashboard based on previous findings. The module thereby leveraged a standardized export format in order to extract and import relevant trial data into a clinical data warehouse. Web-based reporting tools then facilitated the definition of diverse views, including diagrams of the progress of patient accrual and form completion at different granularity levels. To estimate the utility of the dashboard and its compatibility with current workflows, we interviewed study coordinators after a demonstration of sample outputs from ongoing trials. RESULTS: The employed tools promoted a rapid development. Displays of the implemented dashboard are organized around an entry page that integrates key metrics for available studies, and which links to more detailed information such as study-specific enrollment per center. The interviewed experts commented that the included graphical summaries appeared suitable for detecting that something was generally amiss, although practical remedies would mostly depend on additional information such as access to the original patient-specific data. The dependency on a separate application was seen as a downside. Interestingly, the prospective users warned that in some situations knowledge of specific accrual statistics might undermine blinding in a subtle yet intricate fashion, so ignorance of certain patient features was seen as sometimes preferable for reproducibility. DISCUSSION: Our proposed study dashboard graphically recaps key progress indicators of patient accrual and trial documentation. The modular implementation illustrates the technical feasibility of the approach. The use of a study dashboard might introduce certain technical requirements as well as subtle interpretative complexities, which may have to be weighed against potential efficiency gains.

High-dose subconjunctival cyclosporine a implants do not affect corneal neovascularization after high-risk keratoplasty.

PURPOSE: To test whether subconjunctival cyclosporine A (CsA) implants affect the incidence and the degree of corneal neovascularization occurring after penetrating keratoplasty. DESIGN: Prospective, randomized, multicenter, controlled phase 2/3 clinical trial. The study comprised 43 trial sites in Germany, India, and the United States. PARTICIPANTS: Enrolled patients (n = 97) were randomized to 1 of 3 groups: treatment group A (n = 36), treatment group B (n = 40), and the control group (n = 21). METHODS: Patients from each group received either of 2 doses of subconjunctival CsA (group A, low-dose CsA; group B, high-dose CsA) or placebo (carrier only) implants at the time of high-risk penetrating keratoplasty. MAIN OUTCOME MEASURES: The incidence and degree of corneal neovascularization occurring after penetrating keratoplasty were evaluated in a substudy (LX201-01 study: NCT00447187). A web-based image upload system was developed. Standardized digital slit-lamp pictures were quantitatively and objectively evaluated using CellˆF morphometry software. RESULTS: No statistically significant difference in incidence and degree of corneal neovascularization developing after penetrating keratoplasty was found between treatment groups and placebo group. Mean corneal neovascularization area at week 52 (visit 12) was 2.32±1.79% in treatment group A versus placebo (2.79±2.11%; P = 0.45) and 2.74±2.22% in treatment group B versus placebo (2.79±2.11%; P = 0.94). CONCLUSIONS: High-dose subconjunctival CsA implants do not significantly affect corneal neovascularization after high-risk penetrating keratoplasty. This suggests that local CsA has negligible antiangiogenic effects in the human cornea, at least in the transplant setting.

Data Visualization Support for Tumor Boards and Clinical Oncology: Protocol for a Scoping Review.

BACKGROUND: Complex and expanding data sets in clinical oncology applications require flexible and interactive visualization of patient data to provide the maximum amount of information to physicians and other medical practitioners. Interdisciplinary tumor conferences in particular profit from customized tools to integrate, link, and visualize relevant data from all professions involved. OBJECTIVE: The scoping review proposed in this protocol aims to identify and present currently available data visualization tools for tumor boards and related areas. The objective of the review will be to provide not only an overview of digital tools currently used in tumor board settings, but also the data included, the respective visualization solutions, and their integration into hospital processes. METHODS: The planned scoping review process is based on the Arksey and O'Malley scoping study framework. The following electronic databases will be searched for articles published in English: PubMed, Web of Knowledge, and SCOPUS. Eligible articles will first undergo a deduplication step, followed by the screening of titles and abstracts. Second, a full-text screening will be used to reach the final decision about article selection. At least 2 reviewers will independently screen titles, abstracts, and full-text reports. Conflicting inclusion decisions will be resolved by a third reviewer. The remaining literature will be analyzed using a data extraction template proposed in this protocol. The template includes a variety of meta information as well as specific questions aiming to answer the research question: "What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?" The findings will be compiled, charted, and presented as specified in the scoping study framework. Data for included tools may be supplemented with additional manual literature searches. The entire review process will be documented in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. RESULTS: The results of this scoping review will be reported per the expanded PRISMA-ScR guidelines. A preliminary search using PubMed, Web of Knowledge, and Scopus resulted in 1320 articles after deduplication that will be included in the further review process. We expect the results to be published during the second quarter of 2024. CONCLUSIONS: Visualization is a key process in leveraging a data set's potentially available information and enabling its use in an interdisciplinary setting. The scoping review described in this protocol aims to present the status quo of visualization solutions for tumor board and clinical oncology applications and their integration into hospital processes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53627.

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model.

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions.

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence.

BACKGROUND: Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials. METHODS: Each participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group. RESULTS: 351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were 'age' (89%), 'gender' (89%), 'addictive behaviour' (74%), 'disease, symptom and sign' (64%) and 'organ or tissue status' (61%). No data was available for 6 semantic groups. CONCLUSIONS: There exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.