Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation

Abstract

Purpose of Review Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients. Recent Findings Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival. Summary AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.

The Independent Effects of Procurement Biopsy Findings on 10-Year Outcomes of Extended Criteria Donor Kidney Transplants

“…2 Should the European model of limited reliance on biopsies 15 be universally adopted, or should transplant systems aim to standardize and improve on both the criteria for performing a biopsy 23 and techniques used to obtain, interpret, and share biopsy information? [91][92][93][94][95][96] The goal of both camps is the same: to improve outcomes for patients with end-stage renal failure through timely and successful transplantation.…”

Langfristige körperliche und psychische Folgen chronischer Nierenerkrankungen

“…Barrieren zwischen den verschiedenen Behandlungsebenen müssen im Rahmen eines Konsensusprozesses wissenschaftlich begründet und Möglichkeiten zu deren Überwindung erarbeitet werden und zusätzlich müssen epidemiologische Aspekte, verfügbare Personalressourcen sowie Kosten und Strukturen des Gesundheitssystems berücksichtigt werden [ 11 ]. Tele-Health, Telenephrologie und Telepsychologie können helfen, die Situation graduell zu verbessern [ 103 , 104 ]. Aber es bedarf fester organisatorischer, struktureller und finanzieller Voraussetzungen zur optimalen Betreuung der Patient:innen mit chronischer Nierenerkrankung [ 105 , 106 ].…”

Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States

“…Artificial intelligence has been increasingly applied in the medical field for different sectors. Its application in the form of machine learning (ML) has allowed it to develop tools that improve clinical decision making and individualize patient care [ 13 , 14 , 15 , 16 , 17 , 18 ]. Unsupervised consensus clustering is a type of ML that can elucidate novel data patterns and distinct subtypes from large data [ 19 , 20 , 21 ].…”