Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review

Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.

Should AI allocate livers for transplant? Public attitudes and ethical considerations

BACKGROUND

Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented.

METHODS

We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic.

FINDINGS

Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender.

CONCLUSIONS

There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.

Multiview Clustering to Identify Novel Kidney Donor Phenotypes for Assessing Graft Survival in Older Transplant Recipients

Key Points

An unsupervised machine learning clustering algorithm identified distinct deceased kidney donor phenotypes among older recipients. Recipients of certain donor phenotypes were at a relatively higher risk of all-cause graft loss even after accounting for recipient factors. The use of unsupervised clustering to support kidney allocation systems may be an important area for future study.

Background

Older transplant recipients are at a relatively increased risk of graft failure after transplantation, and some of this risk may relate to donor characteristics. Unsupervised clustering using machine learning may be a novel approach to identify donor phenotypes that may then be used to evaluate outcomes for older recipients. Using a cohort of older recipients, the purpose of this study was to (1 ) use unsupervised clustering to identify donor phenotypes and (2 ) determine the risk of death/graft failure for recipients of each donor phenotype.

Methods

We analyzed a nationally representative cohort of kidney transplant recipients aged 65 years or older captured using the Scientific Registry of Transplant Recipients between 2000 and 2017. Unsupervised clustering was used to generate phenotypes using donor characteristics inclusive of variables in the kidney donor risk index (KDRI). Cluster assignment was internally validated. Outcomes included all-cause graft failure (including mortality) and delayed graft function. Differences in the distribution of KDRI scores were also compared across the clusters. All-cause graft failure was compared for recipients of donor kidneys from each cluster using a multivariable Cox survival analysis.

Results

Overall, 23,558 donors were separated into five clusters. The area under the curve for internal validation of cluster assignment was 0.89. Recipients of donor kidneys from two clusters were found to be at high risk of all-cause graft failure relative to the lowest risk cluster (adjusted hazards ratio, 1.86; 95% confidence interval, 1.69 to 2.05 and 1.73; 95% confidence interval, 1.61 to 1.87). Only one of these high-risk clusters had high proportions of donors with established risk factors (i.e. , hypertension, diabetes). KDRI scores were similar for the highest and lowest risk clusters (1.40 [1.18–1.67] and 1.37 [1.15–1.65], respectively).

Conclusions

Unsupervised clustering can identify novel donor phenotypes comprising established donor characteristics that, in turn, may be associated with different risks of graft loss for older transplant recipients.

Deceased Donor Characteristics and Kidney Transplant Outcomes

Kidney transplantation is the therapy of choice for people living with kidney failure who are suitable for surgery. However, the disparity between supply versus demand for organs means many either die or are removed from the waiting-list before receiving a kidney allograft. Reducing unnecessary discard of deceased donor kidneys is important to maximize utilization of a scarce and valuable resource but requires nuanced decision-making. Accepting kidneys from deceased donors with heterogenous characteristics for waitlisted kidney transplant candidates, often in the context of time-pressured decision-making, requires an understanding of the association between donor characteristics and kidney transplant outcomes. Deceased donor clinical factors can impact patient and/or kidney allograft survival but risk-versus-benefit deliberation must be balanced against the morbidity and mortality associated with remaining on the waiting-list. In this article, the association between deceased kidney donor characteristics and post kidney transplant outcomes for the recipient are reviewed. While translating this evidence to individual kidney transplant candidates is a challenge, emerging strategies to improve this process will be discussed. Fundamentally, tools and guidelines to inform decision-making when considering deceased donor kidney offers will be valuable to both professionals and patients.