Data Mining-based Survival Analysis and Simulation Modeling for Lung Transplant

The objective of this research is to develop a decision support methodology for the lung transplant procedure by investigating the UNOS nation-wide dataset via data mining-based survival analysis and simulation-based optimization. Traditional statistical techniques have various limitations which hinder the exploration of the information hidden under the voluminous data. The deployment of the structural equation modeling integrated with decision trees provides a more effective matching between the donor organ and the recipient. Such an integration preceded by powerful data mining models to determine which variables to include for survival analysis is validated via the simulation-based optimization.The suggested data mining-based survival analysis was superior to the conventional statistical methods in predicting the lung graft survivability and in determining the critical variables to include in organ matching and allocation. The proposed matching index derived via structural equation model-based decision trees was validated to be a more effective priority-ranking mechanism than the current lung allocation scoring system. This validation was established by a simulation-based optimization model. It was demonstrated that with this novel matching index, a substantial improvement was achieved in the survival rate while only a short delay was caused in the average waiting time of candidate patients on the list. Furthermore, via the response surface methodology-based simulation optimization the optimal weighting scheme for the components of the novel matching index was determined by jointly optimizing the lung transplant performance measures, namely, the justice principle in terms of the waiting time and the utility principle in terms of the survival rate. The study presents uniqueness in that it provides a means to integrate the data mining modeling as well as simulation optimization with the survival analysis so that more useful information hidden in the large amount of data can be discovered. The developed methodology improves the modeling of matching and allocation system in terms of both interpretability and predictability. This will be beneficial to medical professionals at a great deal.Industrial Engineering & Managemen

Sistema multiagente como apoyo de procesos de trasplante de órganos

This article is a review that leads to build a state of knowledge about Multi-Agent Systems based on Selection and Search (ISSA) applied in the search and selection of organ and tissue transplant recipients, emphasizing as a case study the heart, using Geo-location. In particular, this research analyzes technical, scientific and normative aspects of ISSA, between 2007 and 2017, in Europe (Spain) and Latin America (Colombia). A base line of systems based on Artificial Intelligence is thus obtained for the selection and search of transplant recipients, before a possible demand of the List of Persons Waiting for Donation (LED). From the above, solutions can be implemented reducing time in the allocation of organs taking into account their characteristics and compatibility: blood group, size, location criteria, among others, from one to several possible recipients. Finally, a technological solution model for Colombia is proposed.El presente artículo es una revisión que conduce a construir un estado de conocimiento sobre Sistemas Multi Agentes basados en Selección y Búsqueda (AISB) aplicados en la búsqueda y selección de receptores de trasplante de órganos y tejidos, enfatizando como caso de estudio el corazón, utilizando Geo- localización. Particularmente, esta investigación analiza aspectos técnicos, científicos y normativos de AISB, entre el 2007 y 2017, en Europa (España) y Latino América (Colombia). Se obtiene en consecuencia una línea de base de sistemas basados en Inteligencia Artificial para la selección y búsqueda de receptores de trasplante, ante una posible demanda de Lista de Personas en Espera de Donación (LED). De lo anterior, se pueden implementar soluciones reduciendo tiempos en la asignación de los órganos teniendo en cuenta sus características y la compatibilidad: de grupo sanguíneo, tamaño, criterios de ubicación, entre otros, de uno a varios posibles receptores. Finalmente se propone un modelo de solución tecnológica para Colombia

Determinants of Switching From Peritoneal to Hemodialysis in Preserving Residual Renal Function

There are more than 2 million end stage renal disease (ESRD) patients in the world. ESRD is becoming more manageable with the advent of competent therapies such as peritoneal dialysis (PD) and hemodialysis (HD). While recent evidence suggests that switching from PD to HD may preserve residual renal function longer than either PD or HD alone as an alternative approach, little is known about the optimal timing and the long-term efficacy of switching dialysis modes. The purpose of this quantitative retrospective study, based on the bio-psychosocial model, was to investigate the optimal timing and determinants of the effectiveness of switching dialysis modes from PD to HD. Data were extracted from a national database of ESRD dialysis patients. The Kaplan-Meier survival curve and the log-rank test were used to determine the effect of optimal dialysis time for switching from PD to HD on ESRD patient\u27s survival and mortality. The results showed the optimal duration for switching dialysis modalities was 9 months where patients had a 90% survival rate after switching. ESRD patients taking more than 24 months to switch modes had the highest loss of renal function. Also, patients between 40 and 80 years of age were at a significantly higher hazard of renal function loss than patients younger than 40 years of age. It was concluded that timely switching of dialysis mode from PD to HD increases survival in ESRD patients. Younger patients have better survival rates in peritoneal dialysis modality than older patients. Moreover, females switching from PD to HD have better survival rates than males. The positive social change implications of this study may help raise awareness to the importance of optimal timing when switching dialysis modalities for improved survival and quality of life

The Process of Organ Donation from Non-Living Donors: A Case-Based Journey from Potential Donor Identification to Organ Procurement

Each year, thousands of people worldwide succumb to end-organ failure while awaiting life-saving transplantation procedures. The shortage of organs continues with no signs of easing in the foreseeable future. The availability of organs from living donors continues to be constrained. At the same time, the cumulative knowledge of organ preservation is advancing steadily resulting in an enhanced ability to utilize a growing number of previously unsuitable tissue and organ gifts. Our ability to procure and preserve more organs is accompanied by the increasing use of so-called “expanded criteria” donors, or those whose organs may not have been suitable without modern advances in organ preservation science. Within the overall context of organ donation from non-living donors, the importance of physiologic and end-organ optimization cannot be understated. This chapter discusses our current state of understanding of optimized organ procurement approaches derived from practical experiences and “lessons learned” at a high-performing, community-based tertiary referral hospital

Organ Transplant Crisis: Should the Deficit be Eliminated Through Inter Vivos Sales?

In response to what has been characterized as the last remaining obstacle to transplantation, Senator Warren Hatch introduced a bill on October 20, 1983 which would establish a task force to investigate and make recommendations to Congress about the problem. Hearings and debate on the bill are scheduled to resume with the next Congress. Its future is bleak with the administration opposing it and the budget-cutting axe being resharpened. Regardless of the bill\u27s outcome, the problem of supplying anatomical organs will continue to present a host of moral, political, and most importantly, legal issues which must be resolved if society is to realize the full benefit of transplant science. This article will attempt to address some of these questions, exploring possibilities and obstacles presented by each

Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes

Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation

Clinical datasets are commonly limited in size, thus restraining applications of Machine Learning (ML)techniques for predictive modelling in clinical research and organ transplantation. We explored thepotential of Decision Tree (DT) and Random Forest (RF) classification models, in the context of smalldataset of 80 samples, for outcome prediction in high-risk kidney transplantation. The DT and RF modelsidentified the key risk factors associated with acute rejection: the levels of the donor specific IgG anti-bodies, the levels of IgG4 subclass and the number of human leucocyte antigen mismatches betweenthe donor and recipient. Furthermore, the DT model determined dangerous levels of donor-specific IgGsubclass antibodies, thus demonstrating the potential of discovering new properties in the data whentraditional statistical tools are unable to capture them. The DT and RF classifiers developed in this workpredicted early transplant rejection with accuracy of 85%, thus offering an accurate decision supporttool for doctors tasked with predicting outcomes of kidney transplantation in advance of the clinicalintervention

The EBMT Handbook

This Open Access edition of the European Society for Blood and Marrow Transplantation (EBMT) handbook addresses the latest developments and innovations in hematopoietic stem cell transplantation and cellular therapy. Consisting of 93 chapters, it has been written by 175 leading experts in the field. Discussing all types of stem cell and bone marrow transplantation, including haplo-identical stem cell and cord blood transplantation, it also covers the indications for transplantation, the management of early and late complications as well as the new and rapidly evolving field of cellular therapies. This book provides an unparalleled description of current practices to enhance readers’ knowledge and practice skills