NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study.

BACKGROUND: Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. OBJECTIVE: The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. METHODS: To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. RESULTS: We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. CONCLUSIONS: For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48892.

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients.

TBase is an electronic health record (EHR) for kidney transplant recipients (KTR) combining automated data entry of key clinical data (e.g., laboratory values, medical reports, radiology and pathology data) via standardized interfaces with manual data entry during routine treatment (e.g., clinical notes, medication list, and transplantation data). By this means, a comprehensive database for KTR is created with benefits for routine clinical care and research. It enables both easy everyday clinical use and quick access for research questions with highest data quality. This is achieved by the concept of data validation in clinical routine in which clinical users and patients have to rely on correct data for treatment and medication plans and thereby validate and correct the clinical data in their daily practice. This EHR is tailored for the needs of transplant outpatient care and proved its clinical utility for more than 20 years at Charité - Universitätsmedizin Berlin. It facilitates efficient routine work with well-structured, comprehensive long-term data and allows their easy use for clinical research. To this point, its functionality covers automated transmission of routine data via standardized interfaces from different hospital information systems, availability of transplant-specific data, a medication list with an integrated check for drug-drug interactions, and semi-automated generation of medical reports among others. Key elements of the latest reengineering are a robust privacy-by-design concept, modularity, and hence portability into other clinical contexts as well as usability and platform independence enabled by HTML5 (Hypertext Markup Language) based responsive web design. This allows fast and easy scalability into other disease areas and other university hospitals. The comprehensive long-term datasets are the basis for the investigation of Machine Learning algorithms, and the modular structure allows to rapidly implement these into clinical care. Patient reported data and telemedicine services are integrated into TBase in order to meet future needs of the patients. These novel features aim to improve clinical care as well as to create new research options and therapeutic interventions.

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform.

The MACCS (Medical Assistant for Chronic Care Service) platform enables secure sharing of key medical information between patients after kidney transplantation and physicians. Patients provide information such as vital signs, well-being, and medication intake via smartphone apps. The information is transferred directly into a database and electronic health record at the kidney transplant center, which is used for routine patient care and research. Physicians can send an updated medication plan and laboratory data directly to the patient app via this secure platform. Other features of the app are medical messages and video consultations. Consequently, the patient is better-informed, and self-management is facilitated. In addition, the transplant center and the patient's local nephrologist automatically exchange notes, medical reports, laboratory values, and medication data via the platform. A telemedicine team reviews all incoming data on a dashboard and takes action, if necessary. Tools to identify patients at risk for complications are under development. The platform exchanges data via a standardized secure interface (Health Level 7 (HL7), Fast Healthcare Interoperability Resources (FHIR)). The standardized data exchange based on HL7 FHIR guarantees interoperability with other eHealth solutions and allows rapid scalability to other chronic diseases. The underlying data protection concept is in concordance with the latest European General Data Protection Regulation. Enrollment started in February 2020, and 131 kidney transplant recipients are actively participating as of July 2020. Two large German health insurance companies are currently funding the telemedicine services of the project. The deployment for other chronic kidney diseases and solid organ transplant recipients is planned. In conclusion, the platform is designed to enable home monitoring and automatic data exchange, empower patients, reduce hospitalizations, and improve adherence, and outcomes after kidney transplantation.

The visibility of scientific misconduct: A review of the literature on retracted journal articles.

Retractions of scientific articles are becoming the most relevant institution for making sense of scientific misconduct. An increasing number of retracted articles, mainly attributed to misconduct, is currently providing a new empirical basis for research about scientific misconduct. This article reviews the relevant research literature from an interdisciplinary context. Furthermore, the results from these studies are contextualized sociologically by asking how scientific misconduct is made visible through retractions. This study treats retractions as an emerging institution that renders scientific misconduct visible, thus, following up on the sociology of deviance and its focus on visibility. The article shows that retractions, by highlighting individual cases of misconduct and general policies for preventing misconduct while obscuring the actors and processes through which retractions are effected, produce highly fragmented patterns of visibility. These patterns resemble the bifurcation in current justice systems.

Le retrait d'articles scientifiques après publication est devenu le principal instrument pour mesurer l'ampleur de la fraude scientifique. L'augmentation des cas de retrait d'article, essentiellement pour des raisons de fraude, fournit une nouvelle base empirique pour analyser la fraude scientifique. Cet article se propose de passer en revue la littérature scientifique traitant ce sujet dans un contexte interdisciplinaire. Il contextualise les résultats de cette étude dans le champ sociologique en s'interrogeant sur le mécanisme de dévoilement de la fraude. Il considère les retraits d'article comme un nouvel instrument de révélation de la fraude qui insiste sur la notion de visibilité dans une perspective sociologique de la déviance. En mettant l'accent sur les cas individuels et les politiques de prévention des fraudes tout en faisant l'impasse sur les acteurs et les procédures de retrait des articles, ce processus produit un espace fragmenté de visibilité. En cela, il s'apparente à la séparation des questions judiciaires (bifurcation) dans les décisions de justice.

Las retracciones de artículos científicos se están convirtiendo en la institución más relevante para dar sentido a la mala conducta científica. Un número creciente de artículos retractados, mayormente debido a la mala conducta, está proporcionando una nueva base empírica para la investigación sobre la mala conducta científica. Este artículo revisa la literatura de investigación relevante desde un contexto interdisciplinario. Además, los resultados de estos estudios se contextualizan sociológicamente preguntando cómo la mala conducta científica se hace visible a través de retracciones. Estamos tratando a retracciones como institución emergente que vuelve visible a la mala conducta científica, por lo tanto, seguimos a la sociología de la desviación y su enfoque en la visibilidad. Mostramos que las retracciones, al iluminar los casos individuales de mala conducta y las políticas generales para evitarla, oscurecen los actores y los procesos mediante los cuales se efectúan las retracciones, produciendo patrones altamente fragmentadas de visibilidad. Estos patrones se asemejan a la bifurcación en los sistemas de justicia actuales.

3D printing for sizing left atrial appendage closure device: head-to-head comparison with computed tomography and transoesophageal echocardiography.

AIMS: Device sizing for LAA closure using transoesophageal echocardiography (TEE) can be challenging due to complex LAA anatomy. We investigated whether the use of 3D-printed left atrial appendage (LAA) models based on preprocedural computed tomography (CT) permits accurate device sizing. METHODS AND RESULTS: Twenty-two (22) patients (73±8 years, 55% male) with atrial fibrillation requiring anticoagulation at high bleeding risk underwent LAA closure (WATCHMAN device). Preprocedurally, LAA was sized by TEE and third-generation dual-source CT. Based on CT, 3D printing models of LAA anatomy were created for simulation of device implantation. Device compression was assessed in a CT scan of the 3D model with the implanted device. Implantation was successful in all patients. Mean LAA ostium diameter based on TEE was 22±4 mm and based on CT 25±3 mm (p=0.014). Predicted device size based on simulated implantation in the 3D model was equal to the device finally implanted in 21/22 patients (95%). TEE would have undersized the device in 10/22 patients (45%). Device compression determined in the 3D-CT model corresponded closely with compression upon implantation (16±3% vs. 18±5%, r=0.622, p=0.003). CONCLUSIONS: Patient-specific CT-based 3D printing models may assist device selection and prediction of device compression in the context of interventional LAA closure.