We developed ML models to validate the importance of AAC score as a predictor variable in identifying patients at risk of mortality waiting for kidney transplantation. The RF, ExtraTrees, and XGBoost models were trained and validated independently and in an ensemble scheme on retrospective data collected from our transplant center. The proposed ensemble ML can be used as a triage system for prioritizing patients needing kidney transplantation.
Previously, medical experts have established a clear link between vascular calcification and atherosclerotic disease[16, 17]. Nonetheless, previous studies utilized varied methods for evaluating arterial calcification, lacking a standardized scoring system associated with ESKD patient survival[18, 19]. We refined the Agatston method to rectify this, resulting in a consistent imaging biomarker. This imaging biomarker can potentially assess the survival risk for kidney transplant candidates.
In light of the study's findings, a significant question emerges concerning the practical implementation of the AAC score-based risk stratification approach in the context of kidney transplant allocation. One potential application of the AAC score-based risk stratification is the possibility of reshaping the allocation of kidney transplants. Patients with higher AAC scores, indicative of increased mortality risks, could be prioritized for transplantation, potentially improving their chances of survival post-transplant. This approach aligns with the principle of maximizing the benefit gained from each available organ and aligning it with patients who stand to gain the most [20, 21]. Furthermore, by expediting the transplant process for those at heightened risk, the transplant community could take proactive measures to mitigate these patients' cardiovascular mortality risks during the waiting period [22, 23].
Conversely, an alternative perspective underscores the importance of patient safety and long-term outcomes. Patients with elevated AAC scores, indicative of a greater mortality risk, might warrant a more cautious approach toward transplantation [24, 25]. Given that transplantation is not without its own set of risks and complications, it could be argued that for individuals with higher mortality risks, the benefits of transplantation might be outweighed by the potential complications. In such cases, alternative treatment strategies or interventions that target cardiovascular health could be explored to reduce mortality risks in these high-risk patients before transplantation [26–28]. Ultimately, deciding whether to modify the transplant order or be cautious about transplantation in high-risk patients should be made carefully considering several factors. These factors include the severity of AAC scores, the overall health status of the patients, the availability of organs, ethical considerations, and the potential impact on waitlisted patients without elevated risks.
While the AAC score-based risk stratification offers a promising avenue for enhancing the effectiveness of kidney transplant allocation, the decision of how to incorporate this stratification into clinical practice is multifaceted. Individualized patient care remains paramount, and a multidisciplinary approach involving transplant professionals, clinicians, ethicists, and patients is necessary to make informed and ethical decisions [29–31],[29–31]. As the medical community navigates this nuanced terrain, ongoing research, and collaborative efforts will be essential in shaping a well-considered and ethically sound approach to kidney transplant prioritization based on risk stratification.
When considering the outcomes of this study, we also wish to highlight its limitations. First, as with all retrospective studies, the dependence on the completeness and accuracy of secondary data is of concern. While we have no reason to believe there are systemic inaccuracies in the data, issues such as missing data or data entry errors could potentially bias results. One of the advantages of ensemble models is less sensitivity to such issues by incorporating multiple independent models. However, ML models are prone to bias, which can lead to high specificity and low sensitivity or vice versa, which practically reduces the usability of such models in practice. Ensemble ML reduces this bias by aggregating outputs of various independently trained ML models. The proposed ensemble ML model balances the specificity and sensitivity criteria in prioritizing patients needing kidney transplantation. Further, one of the major limitations of ML models, particularly deep learning models, is the lack of explainability. To improve understanding of the models, input features visualization and importance analysis were conducted. Also, we utilized a game theoretic approach to analyze the importance of input variables in the decision-making process of the ML models. Training ML models on larger datasets can potentially increase their prediction accuracy. The models needs to be evaluated on multiple external sites for further generalization performance evaluation.