Long-term management and outcome of lung transplantation in Japan

The long-term survival after lung transplantation (LT) is favorable in Japan. However, longterm survivors after LT are subject to late complications, including chronic lung allograft dysfunction (CLAD), malignancy, infection, and chronic kidney disease (CKD) because of the need for lifelong immunosuppression. The rates of single cadaveric LT (CLT) and living-donor lobar LT (LDLLT) are higher than that of bilateral CLT in Japan. Here, we will describe the management of late complications and long-term outcome after LT in Japan. Attention should be paid to not only the phenotype of CLAD but also the difference in CLAD after CLT and after LDLLT as well as the timing of lung re-transplantation for advanced CLAD, especially after single CLT. Since post-transplant lymphoproliferative disorder is the most common malignancy after LT, infection monitoring for infection-related malignancies and appropriate screening are keys to the early diagnosis and treatment of malignancy after LT. The long-term management of infection after LT is also important, especially with regard to community-acquired pathogens, Aspergillus, and cytomegalovirus. When providing long-term care after LT, physicians should be aware of CKD and the timing of renal replacement therapy in cases with severe CKD. The widespread use of computed tomography and dialysis in Japan are beneficial for long-term survivors of LT. The similar survival outcomes of single CLT and LDLLT, compared with bilateral CLT, might contribute to improved long-term survival in Japan. Pulmonologists are encouraged to become further involved in long-term management after LT in Japan

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

Deep Learning Applications for Biomedical Data and Natural Language Processing

The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. Development of deep learning is essentially a quest to mimic how the human brain, at least partially, operates.In this thesis, I will use machine learning techniques to tackle two different domain of problems. The first is a problem in natural language processing. We improved classification of relations within images, using text associated with the pictures. The second domain is regarding heart transplant. We created models for pre- and post-transplant survival and simulated a whole transplantation queue, to be able to asses the impact of different allocation policies. We used deep learning models to solve these problems.As introduction to these problems, I will present the basic concepts of machine learning, how to represent data, how to evaluate prediction results, and how to create different models to predict values from data. Following that, I will also introduce the field of heart transplant and some information about simulation

Comparing Performances of Logistic Regression, Classification & Regression Trees and Artificial Neural Networks for Predicting Albuminuria in Type 2 Diabetes Mellitus

In this study, performances of classification methods were compared in order to predict the presence of albuminuria in type 2 diabetes mellitus patients. A retrospective analysis was performed in 266 subjects. We compared performances of logistic regression (LR), classification and regression trees (C&RT) and two artificial neural networks algorithms. Predictor variables were gender, urine creatinine, weight, blood urea, serum albumin, age, creatinine clearance, fasting plasma glucose, post-prandial plasma glucose, and HbA1c. For validation set, the best classification accuracy (84.85%), sensitivity (68.0%) and the highest Youden index (0.63) was found in the MLP model but the specificity was 95.12%. Additionally, the specificity of all the models was close to each other. For whole data set the results were found as 84.21%, 53.95%, 0.50 and 96.32% respectively. Consequently, the model had the highest predictive capability to predict the presence of albuminuria was MLP. According to this model, blood urea and serum albumin were the most important variables for predicting the albuminuria. On the basis of these considerations, we suggest that data should be better explored and processed by high performance modeling methods. Researchers should avoid assessment of data by using only one method in future studies focusing on albuminuria in type 2 diabetes mellitus patients or any other clinical condition

The Rule of Rescue in the Era of Precision Medicine, HLA Eplet Matching, and Organ Allocation

Precision medicine can put clinicians in a position where they must act more as resource allocators than their traditional role as patient advocates. In the allocation of transplantable organs and tissues, the use of eplet matching will enhance precision medicine but, in doing so, generate a tension with the present reliance on rule of rescue and justice-based factors for allocations. Matching donor and recipient human leukocyte antigens (HLA) is shown to benefit virtually all types of solid organ transplants yet, until recently, HLA-matching has not been practical and was shown to contribute to ethnic/racial disparities in organ allocation. Recent advances using eplets from the HLA molecule has renewed the promise of such matching for predicting patient outcomes. The rule of rescue in organ allocation reflects a combination of ethical, policy, and legal imperatives. However, the rule of rescue can impede the allocation strategies adopted by professional medical associations and the optimal use of scarce transplant resources. While eplet-matching seeks to improve outcomes, it may potentially frustrate current ethics-motivated initiatives, established patient-practitioner relationships, and functional conventions in the allocation of medical resources such as organ and tissue transplants. Eplet-matching allocation schemes need to be carefully and collaboratively designed with clear, fair and equitable guidelines that complement functional conventions and maintain public trust

History of clinical transplantation

How transplantation came to be a clinical discipline can be pieced together by perusing two volumes of reminiscences collected by Paul I. Terasaki in 1991-1992 from many of the persons who were directly involved. One volume was devoted to the discovery of the major histocompatibility complex (MHC), with particular reference to the human leukocyte antigens (HLAs) that are widely used today for tissue matching.1 The other focused on milestones in the development of clinical transplantation.2 All the contributions described in both volumes can be traced back in one way or other to the demonstration in the mid-1940s by Peter Brian Medawar that the rejection of allografts is an immunological phenomenon.3,4 © 2008 Springer New York

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance

Clinical and Preclinical Lung Transplantation in the aspects of improving outcome

Lung transplantation (LTx) is an established therapeutic option for end-stage pulmonary disease. However, it remains restricted by donor lung scarcity. Donor's lungs are rejected frequently due to severe lung damage caused by aspiration or neurogenic pulmonary oedema that can all lead to acute lung injury (ALI), and more severe acute respiratory distress syndrome (ARDS). Lung transplant patients face poor survival rates in comparison with other solid organ transplantations. This is primarily due to a high incidence of postoperative complications, such as primary graft dysfunction (PGD) and chronic lung allograft dysfunction (CLAD), especially bronchiolitis obliterans syndrome (BOS). The aim of this thesis was to expand the availability of a donor's lungs for transplantation. We sought to increase the chances of a lifesaving opportunity for recipients who may otherwise have remained on the transplant waiting list for years. We did this preclinically by utilising a variety of techniques to regain lung function in discarded lungs, thus increasing the donor pool. We investigated the role of cytokine adsorption during ex vivo lung perfusion (EVLP), and extracorporeal haemofiltration post-transplant as a means of treating and restoring the ARDS-damaged lungs and reducing the incidence of PGD post-transplantation. The lungs were evaluated regarding the development of primary graft dysfunction (PGD) in which cytokines seem to be an essential target given the outcome of significantly less PGD in the group receiving cytokine adsorption. We suggest this treatment method will increase the availability of the donor's lungs and increase the tolerability of the donor's lungs in the recipient. The results of this study formed the basis for our idea to investigate the effect of mesenchymal stromal cell (MSC) therapy to restore gastric content aspirations damaged lungs and reduce the incidence of PGD at 72 hours’ post-transplantation. Furthermore, we explored pulmonary function, survival, and the incidence of CLAD between patients receiving marginal lungs after ex vivo lung perfusion (EVLP) reconditioning and patients receiving clinically standard lungs (conventional lungs) at our centre. These patients were followed for over 10 years. We did not find any difference in pulmonary function, survival, or incidence of CLAD, indicating that EVLP is safe to use and does not increase mortality. We also explored the impact of allograft ischaemic time (IT) in lung transplantation survival rate which showed superior outcomes for IT between 120 and 240 minutes. Every 2-hour increase in IT was equivalent to an increased mortality of up to 24% within 5 years. This indicates that IT has a key role in improving LTx outcomes. We explored the role of plasma biomarkers in the largest subgroup of CLAD, patients with BOS. Plasma from lung- transplanted patients with different BOS grades was analysed for protein biomarkers using Olink proteomics. A selective number of biomarkers were then validated using an enzyme-linked immunosorbent assay (ELISA) at baseline and after 1 year. Corticotropin-releasing hormone (CRH) levels were found to be related to different stages of BOS which identified CRH as a potential marker in a novel diagnostic tool to detect BOS. In conclusion, using EVLP is a safe effective platform for cytokine adsorption therapy and MSC therapy which can restore pulmonary function in damaged donor lungs, thus increasing the donor pool. CRH is a novel potential biomarker in the progression of post-transplantation BOS grades