Prediction of Kidney Allograft Discard Before Procurement: The Kidney Discard Risk Index

Price, Mellanie; Yan, Gaibo; Joshi, Manasi; Zhang, Theodore; Hickner, Brian; O’Mahony, Christine A.; Goss, J.; Galván, Nhu Thao Nguyen; Cotton, Ronald T.; Rana, Abbas

doi:10.6002/ect.2020.0340

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Other researchers have concentrated on predicting allograft or donor discards. Pettit et al [57] showed the efficacy of the XGBoost model at predicting organ use, while Barah et al [58] observed random forest's proficiency in identifying kidneys at risk of discard, with Price et al [59] having developed a kidney discard risk index, identifying 21 factors predictive of organ discard.…”

Section: Addressing the Organ Shortagementioning

confidence: 99%

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

Ramalhete,

Almeida,

Ferreira

et al. 2024

BioMedInformatics

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This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can enhance the transplantation process, from donor selection to postoperative patient care. Our methodology involved a comprehensive review of current research, focusing on the application of AI and ML in various stages of KT. This included an analysis of donor–recipient matching, predictive modeling, and the improvement in postoperative care. The results indicated that AI and ML significantly improve the efficiency and success rates of KT. They aid in better donor–recipient matching, reduce organ rejection, and enhance postoperative monitoring and patient care. Predictive modeling, based on extensive data analysis, has been particularly effective in identifying suitable organ matches and anticipating postoperative complications. In conclusion, this review discusses the transformative impact of AI and ML in KT, offering more precise, personalized, and effective healthcare solutions. Their integration into this field addresses critical issues like organ shortages and post-transplant complications. However, the successful application of these technologies requires careful consideration of their ethical, privacy, and training aspects in healthcare settings.

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Section: Addressing the Organ Shortagementioning

confidence: 99%

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

Ramalhete,

Almeida,

Ferreira

et al. 2024

BioMedInformatics

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“…Kidneys are discarded for a myriad of reasons, with 2 of the top predictive factors being donor age and creatinine. 3 Kidneys retrieved from expanded criteria donors (ECDs), defined as those obtained from deceased donors ≥ 60 years, or donors aged 50-59 years with 2 of following: cerebrovascular cause of death, terminal serum creatinine > 132.6 μmol/L (1.5 mg/dL), or history of hypertension, 4 are more likely to be discarded. 3 , 5 , 6 In an analysis of registry data (October 1999 to June 2005) nearly half (41%, 5139/12 536) of ECD kidneys were not used.…”

Section: Introductionmentioning

confidence: 99%

“… 3 Kidneys retrieved from expanded criteria donors (ECDs), defined as those obtained from deceased donors ≥ 60 years, or donors aged 50-59 years with 2 of following: cerebrovascular cause of death, terminal serum creatinine > 132.6 μmol/L (1.5 mg/dL), or history of hypertension, 4 are more likely to be discarded. 3 , 5 , 6 In an analysis of registry data (October 1999 to June 2005) nearly half (41%, 5139/12 536) of ECD kidneys were not used. 5 Further work has highlighted a significant variation in acceptance rates of lower quality organs between settings, suggesting that kidneys considered unusable by 1 transplant center, could be acceptable for another.…”

Section: Introductionmentioning

confidence: 99%

Waitlisted and Transplant Patient Perspectives on Expanding Access to Deceased-Donor Kidney Transplant: A Qualitative Study

Rosaasen

Mainra

et al. 2022

Can J Kidney Health Dis

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Background: A concerning number of kidneys (eg, expanded donor criteria, extended criteria, or marginal kidneys) are discarded yearly while patients experience significant morbidity and mortality on the transplant waitlist. Novel solutions are needed to solve the shortage of kidneys available for transplant. Patient perceptions regarding the use of these less than ideal kidneys remain unexplored. Objective: To explore the perspectives of patients who have previously received a less than ideal kidney in the past and patients awaiting transplant who could potentially benefit from one. Design: Qualitative description study. Setting: 2 provinces in Canada participated (Saskatchewan and Manitoba). Patients: Patients with end-stage kidney disease who were awaiting kidney transplant and were either (a) aged 65 years and older, or (b) 55 years and older with other medical conditions (eg, diabetes). Methods: Criterion sampling was used to identify participants. Semi-structured, one-on-one interviews were conducted virtually, which explored perceived quality of life, perceptions of less than ideal kidneys, risk tolerance for accepting one, and educational needs to make such a choice. The interviews were transcribed verbatim and thematic analysis was used to analyze the data. Results: 15 interviews were conducted with usable data (n = 10 pretransplant; n = 5 posttransplant). Participants were a mean of 65.5 ± 8.8 years old. Four interrelated themes became prominent including (1) patient awareness and understanding of their situation or context, (2) a desire for information, (3) a desire for freedom from dialysis, and (4) trust. Subthemes of transparency, clarity, standardization, and autonomy were deemed important for participant education. The majority of pretransplant participants (n = 8/10) indicated that between 3 and 5 years off of dialysis would make the risk of accepting a less than ideal kidney feel worthwhile. Limitation: The study setting was limited to 2 Canadian provinces, which limits the generalizability. Furthermore, the participants were homogenous in demographics such as ethnicity. Conclusion: These findings indicate that patients are comfortable to accept a less than ideal kidney for transplant in situations where their autonomy is respected, they are provided clear, standardized, and transparent information, and when they trust their physician. These results will be used to inform the development of a new national registry for expanding access to deceased-donor kidney transplant. Trial Registration: Not registered.

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Kidney transplants from elderly donors: what we have learned 20 years after the Crystal City consensus criteria meeting

Mella,

Calvetti,

Barreca

et al. 2024

J Nephrol

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Based on the current projection of the general population and the combined increase in end-stage kidney disease with age, the number of elderly donors and recipients is increasing, raising crucial questions about how to minimize the discard rate of organs from elderly donors and improve graft and patient outcomes. In 2002, extended criteria donors were the focus of a meeting in Crystal City (VA, USA), with a goal of maximizing the use of organs from deceased donors. Since then, extended criteria donors have progressively contributed to a large number of transplanted grafts worldwide, posing specific issues for allocation systems, recipient management, and therapeutic approaches. This review analyzes what we have learned in the last 20 years about extended criteria donor utilization, the promising innovations in immunosuppressive management, and the molecular pathways involved in the aging process, which constitute potential targets for novel therapies. Graphical abstract

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Prediction of Kidney Allograft Discard Before Procurement: The Kidney Discard Risk Index

Cited by 3 publications

References 15 publications

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

Waitlisted and Transplant Patient Perspectives on Expanding Access to Deceased-Donor Kidney Transplant: A Qualitative Study

Kidney transplants from elderly donors: what we have learned 20 years after the Crystal City consensus criteria meeting

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