Evidence map›Paper›PMID 38133915›Full record

ArticleJMIR research protocols2023

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

Matthieu-P Schapranow, Mozhgan Bayat, Aadil Rasheed, Marcel Naik, Verena Graf, Danilo Schmidt, Klemens Budde, Héloïse Cardinal, Ruth Sapir-Pichhadze, Franz Fenninger and 10 more

Abstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Matthieu-P SchapranowHasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.ORCID https://orcid.org/0000-0001-6601-2942
Mozhgan BayatHasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.ORCID https://orcid.org/0000-0002-8784-1157
Aadil RasheedHasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.ORCID https://orcid.org/0009-0005-1083-3043
Marcel NaikDepartment of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID https://orcid.org/0000-0002-6063-2806
Verena GrafGeschäftsbereich IT, Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID https://orcid.org/0000-0001-7178-7160
Danilo SchmidtGeschäftsbereich IT, Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID https://orcid.org/0000-0001-5236-2749
Klemens BuddeDepartment of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID https://orcid.org/0000-0002-7929-5942
Héloïse CardinalResearch Centre, Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada.ORCID https://orcid.org/0000-0002-9413-651X
Ruth Sapir-PichhadzeDivision of Nephrology and Multi-Organ Transplant Program, Department of Medicine and Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, QC, Canada.ORCID https://orcid.org/0000-0003-0745-004X
Franz FenningerDivision of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-3859-6626
Karen SherwoodDivision of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-3191-7263
Paul KeownDivision of Nephrology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0001-6466-8251
Oliver P GüntherGünther Analytics, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0003-1900-715X
Konstantin D PandlDepartment of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID https://orcid.org/0000-0002-9360-3817
Florian LeiserDepartment of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID https://orcid.org/0000-0001-5347-0493
Scott ThiebesDepartment of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID https://orcid.org/0000-0002-6917-1831
Ali SunyaevDepartment of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.ORCID https://orcid.org/0000-0002-4353-8519
Matthias NiemannPIRCHE AG, Research and Development, Berlin, Germany.ORCID https://orcid.org/0000-0002-3514-9812
Andreas SchimanskiPIRCHE AG, Research and Development, Berlin, Germany.ORCID https://orcid.org/0009-0007-8374-8223
Thomas KleinPIRCHE AG, Berlin, Germany.ORCID https://orcid.org/0009-0007-1687-0691

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent 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.

objectiveThe 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.

methodsTo 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.

resultsWe 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.

conclusionsFor 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.

Indexed as

clinical prediction modeldonor-recipient matchingfederated learning infrastructurekidney transplantationmultinational transplant data setposttransplant risks

Identifiers

PMID38133915
PMCPMC10770792

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.