Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

Establishing an objective quality assessment of an organ prior to transplantation can help prevent unnecessary discard of the organ and reduce the probability of functional failure. In this regard, normothermic machine perfusion (NMP) offers new possibilities for organ evaluation. However, to date, few studies have addressed the identification of markers and analytical tools to determine graft quality. In this study, function and injury markers were measured in blood and urine during NMP of 26 porcine kidneys and correlated with ex vivo inulin clearance behavior. Significant differentiation of kidneys according to their function could be achieved by oxygen consumption, oxygen delivery, renal blood flow, arterial pressure, intrarenal resistance, kidney temperature, relative urea concentration, and urine production. In addition, classifications were accomplished with supervised learning methods and histological analysis to predict renal function ex vivo. Classificators (support vector machines, k-nearest-neighbor, logistic regression and naive bayes) based on relevant markers in urine and blood achieved 75% and 83% accuracy in the validation and test set, respectively. A correlation between histological damage and function could not be detected. The measurement of blood and urine markers provides information of preoperative renal quality, which can used in future to establish an objective quality assessment.

Details

Original languageEnglish
Article number3055
Number of pages20
JournalBiomedicines
Volume10
Issue number12
Publication statusPublished - Dec 2022
Peer-reviewedYes

External IDs

Scopus 85144872044

Keywords

Research priority areas of TU Dresden

Subject groups, research areas, subject areas according to Destatis

Sustainable Development Goals

Keywords

  • supervised machine learning, Function assessment, normothermic machine perfusion, ex vivo kidney