Personal prediction of graft survival in kidney transplant recipients before transplantation using machine learning methods
Kidney allograft failure is an important burden in kidney disease and contributes to the increasing number of people with ESKD, now exceeding 7 million worldwide as of 2020. Currently, more than 3000 Kazakhstanis patients urgently need kidney transplantation (www.egov.kz., accessed 28.03.2022). Despite improvements in transplant immune suppression techniques, unadjusted 5-year survival of ESKD patients on KRT was 41% in the USA, 48% in Europe, and 60% in Japan. Recent emerged studies reveal that artificial intelligence-assisted donor and recipient data analysis may improve the prediction of long-term graft survival.
Project goal:
We aim to develop an artificial intelligence approach to enhance risk stratification for kidney transplant recipients of living donors by generating continuously refined predictions of early allograft function impairment and survival before transplantation.
Project Objectives:
Kidney allograft failure is an important burden in kidney disease and contributes to the increasing number of people with ESKD,
now exceeding 7 million worldwide as of 2020. Currently, more than 3000 Kazakhstanis patients urgently need kidney transplantation
(www.egov.kz., accessed 28.03.2022). Despite improvements in transplant immune suppression techniques, unadjusted 5-year survival of ESKD patients on KRT was
41% in the USA, 48% in Europe, and 60% in Japan. Recent emerged studies reveal that artificial intelligence-assisted donor and recipient data analysis may
improve the prediction of long-term graft survival.
Supervisor of the project, Dr. Salybekov Amangeldi
Graduated from Astana Medical University (Kazakhstan) in 2012.
Worked as vascular surgeon at vascular and reconstructive surgery department in JSC ` National Center for Oncology and Transplantation`.
In 2019 received PhD degree in medicine from Tokai University School of Medicine and pursued joint postdoctoral followership in Brigham woman’ hospital,
Harvard University School of Medicine and Tokai University School of Medicine.
From 2020, Dr. Salybekov works as CEO of Qazaq Institute of Innovative Medicine and also in Shonan Kamakura General Hospital Kidney Disease and Transplant Center as visiting director.
Scopus: 57204169112
Web of Science: M-1130-2013
ORCID: 0000-0001-5490-9365
Our Team
Executor
Scopus: 57204640972
Web of Science: HKO-1511-2023
ORCID: 0000-0002-3486-227X
Executor
Scopus: 57670591400
Web of Science: AAB-2563-2020
ORCID: 0000-0003-3716-0895
Executor
Scopus: 57237958600
Web of Science: EGW-2175-2022
ORCID: 0000-0001-5949-6942
Timeline of the project
Data collection and preprocessing
Collecting and data preprocessing i.e.
Feature engineering and Predicting models
Feature engineering and Predicting models
Development of predicting models
Algorithms: Lineal regression, clustering, classification, and Apriori models for recipients and donors’ risks stratification and future graft function prediction
Convolutional neural networks classification on allograft biopsies
Three times cross-validation (more than 10000 patients, from USA transplant centers) and deployment on an external real-world cohort (101 patients, Japanese cohort) will be used for validation. The area under the receiver operating characteristic curve (AUROC) will be used as the main performance metric (the primary endpoint to assess CNN performance).
Kidney allograft function assessment and prediction models
Model development. As shown in Fig 2., we will use various models to find the best fit model for the prediction of eGFR.
Currently available prerelease of Kidney pretransplantation predictor app, you can download it by clicking: Predictor
Donor kidney function assessment and predicting models
This step will utilize the Apriori algorithm to define the possible mechanism of transplanted graft functional impairment.
Histological data and genetic data collection and interpretation
Histological and genetic data will be integrated to clinical data to increase eGFR predictive accuracy. Applying various data, we plan to increase sensitivity and specificity.
1st manuscript draft preparation
1st manuscript draft preparation. Reading and collection of literature. Data mining
1st manuscript revision, and submission to the journal
1st article submission to the journal that is included in the 1 (first) and (or) 2 (second) quartile by impact factor in the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 65 (sixty-five).
Cloud-based Web-application construction for data analysis
Cloud-based web application first test will start based on preliminary and obtained outcomes.
Data analysis
Final Histology data analysis and re-integration with clinical data.
Clinical samples characteristics
In this step, we use genetic data to define possible mechanism of early transplant functional mitigation via machine learning algorithms.
Published articles review and data collection, mining
The 2nd manuscript draft preparation, revision.
Final analyzed data evaluation
Analyzed data models will be verified and checked.
2nd manuscript final revision and submission
2nd article submission to the journal that is included in the 1 (first) and (or) 2 (second) quartile by impact factor in the Web of Science database and (or) having a CiteScore percentile in the Scopus database of at least 65 (sixty-five).
Final cloud-based web-application
Final cloud-based web-application.
Ask about project
General:
amansaab0@gmail.com, markus.wolfien@gmail.com
Biopsy image processing:
zhburibaev@gmail.com
Tabular data analysis:
aidyn.daulet@gmail.com, yerkosova@gmail.com