Evaluation of Opening Offers Early for Deceased Donor Kidneys at Risk of Nonutilization.

BACKGROUND: Reducing nonutilization of kidneys recovered from deceased donors is a current policy concern for kidney allocation in the United States. The likelihood of nonutilization is greater with a higher kidney donor risk index (KDRI) offer. We examine how opening offers for organs with KDRI >1.75 to the broader waitlist at varying points of time affects usage rates. METHODS: We simulate kidney allocation using data for January 2018 to June 2019 from Organ Procurement and Transplantation Network. For the simulation experiment, allocation policy is modified so that KDRI >1.75 organs are offered to all local candidates (same donation service area) after a set amount of cold time simultaneously. Open offers to candidates nationally are similarly examined. RESULTS: Simulation results ( n =50 replications) estimate that opening offers locally for KDRI >1.75 after 10 hours yields a nonutilization rate of 38% (range: 35%-42%), less than the prevailing rate of 55% of KDRI >1.75 kidneys. Opening offers after 5 hours yields 30% (range: 26%-34%), reducing the prevailing nonutilization rate by 45%. Opening offers nationally after 10 and 5 hours yields nonutilization rates of 11% (range: 8%-15%) and 6% (range: 4%-9%) for KDRI >1.75 kidneys, respectively. CONCLUSIONS: Simulation findings indicate that opening offers and adjusting their timing can significantly reduce nonutilization of high-KDRI kidneys.

Implications of Accumulated Cold Time for US Kidney Transplantation Offer Acceptance.

BACKGROUND AND OBJECTIVES: Reducing discard is important for the US transplantation system because nearly 20% of the deceased donor kidneys are discarded. One cause for the discards is the avoidance of protracted cold ischemia times. Extended cold ischemia times at transplant are associated with additional risk of graft failure and patient mortality. A preference for local (within the same donor service area) or low-Kidney Donor Risk Index organs, the endogeneity of cold ischemia time during organ allocation, and the use of provisional offers all complicate the analysis of cold ischemia times' influence on kidney acceptance decision making. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using January 2018 to June 2019 Organ Procurement and Transplantation Network data, we modeled the probability of accepting an offer for a kidney after provisional acceptance. We use logistic regression that includes cold ischemia time, Kidney Donor Risk Index, and other covariates selected from literature. Endogeneity of cold ischemia time was treated by a two-stage instrumental variables approach. RESULTS: Logistic regression results for 3.33 million provisional acceptances from 12,369 donors and 108,313 candidates quantify trade-offs between cold ischemia time at the time of offer acceptance and donor-recipient characteristics. Overall, each additional 2 hours of cold ischemia time affected acceptance for nonlocal and local recipients (odds ratio, 0.75; 95% confidence interval, 0.73 to 0.77, odds ratio, 0.88; 95% confidence interval, 0.86 to 0.91; P<0.001). For Kidney Donor Risk Index >1.75 (Kidney Donor Profile Index >85) kidneys, an additional 2 hours of cold ischemia time for nonlocal and local recipients was associated with acceptance with odds ratio, 0.58; 95% confidence interval, 0.54 to 0.63 (nonlocal) and odds ratio, 0.65; 95% confidence interval, 0.6 to 0.7 (local); P<0.001. The effect of an additional 2 hours of cold ischemia time on acceptance of kidneys with Kidney Donor Risk Index ≤1.75 (Kidney Donor Profile Index ≤85) was less pronounced for nonlocal offers (odds ratio, 0.82; 95% confidence interval, 0.80 to 0.85; P<0.001) and not significant for local offers. CONCLUSIONS: The acceptability of marginal organs was higher when placements were nearer to the donor and when cold ischemia time was shorter.

The Role of Procurement Biopsies in Kidney Acceptance Decision Making and Kidney Discard: Perceptions of Physicians, Nurse Coordinators, and OPO Staff and Directors.

Procurement biopsies suffer from challenges with quality and reproducibility and are linked to kidney discard. Nonetheless, procurement biopsies are obtained for the majority of kidneys in the United States, and biopsy findings are commonly relied upon in kidney acceptance decisions. Methods: We conducted in-depth, semistructured interviews with 30 surgeons, nephrologists, nurse coordinators, and organ procurement organization (OPO) staff and directors to assess perceptions of factors contributing to kidney discard and strategies to reduce kidney discard, with a focus on the role of procurement biopsies. Thematic analysis was used to analyze qualitative data. Results: Three main themes emerged: (1) participants emphasized the importance of biopsy findings in making acceptance decisions but expressed concerns about a lack of standardization and quality control; (2) participants reported large variations in the level of importance placed on biopsy findings, the level of reliance on glomerulosclerosis in particular, and the cutoffs used; and (3) participants disagreed about how often procurement biopsies should be taken, with some supporting stricter limits on which kidneys are biopsied and others preferring a biopsy for most kidney offers. Conclusions: These findings support the development of standard practices for which kidneys require biopsy, how the biopsy material is prepared, and how the biopsy is interpreted. Variability in kidney acceptance practices across centers and the use of biopsy findings in guiding recipient selection also lend support to policies to allocate kidneys with suboptimal histological findings to the centers that are willing to use such kidneys and the patients who could most benefit from such offers.

Patient and Clinician Perceptions of Informed Consent and Decision Making About Accepting KDPI > 85 Kidneys.

BACKGROUND: Although the impact of the kidney donor profile index (KDPI) on kidney discard is well researched, less is known about how patients make decisions about whether to give consent for KDPI > 85 kidney offers. METHODS: We conducted in-depth, semistructured interviews with 16 transplant recipients, 15 transplant candidates, and 23 clinicians (transplant surgeons, nephrologists, and nurse coordinators) to assess and compare perceptions of transplant education, informed consent for KDPI > 85 kidneys' and the decision-making process for accepting kidney offers. Thematic analysis was used to analyze qualitative data. RESULTS: Four themes emerged: (1) patients reported uncertainty about the meaning of KDPI or could not recall information about KDPI; (2) patients reported uncertainty about their KDPI > 85 consent status and a limited role in KDPI > 85 consent decision making; (3) patients' reported willingness to consider KDPI > 85 kidneys depended on their age, health status, and experiences with dialysis, and thus it changed over time; (4) patients' underestimated the survival benefit of transplantation compared with dialysis, which could affect their KDPI > 85 consent decision making. CONCLUSIONS: To better support patients' informed decision making about accepting KDPI > 85 kidneys, centers must ensure that all patients receive education about the trade-offs between accepting a KDPI > 85 kidney and remaining on dialysis. Additionally, education about KDPI and discussions about informed consent for KDPI > 85 kidneys must be repeated at multiple time points while patients are on the waiting list.

Predicting Kidney Discard Using Machine Learning.

BACKGROUND: Despite the kidney supply shortage, 18%-20% of deceased donor kidneys are discarded annually in the United States. In 2018, 3569 kidneys were discarded. METHODS: We compared machine learning (ML) techniques to identify kidneys at risk of discard at the time of match run and after biopsy and machine perfusion results become available. The cohort consisted of adult deceased donor kidneys donated between December 4, 2014, and July 1, 2019. The studied ML models included Random Forests (RF), Adaptive Boosting (AdaBoost), Neural Networks (NNet), Support Vector Machines (SVM), and K-nearest Neighbors (KNN). In addition, a Logistic Regression (LR) model was fitted and used for comparison with the ML models' performance. RESULTS: RF outperformed other ML models. Of 8036 discarded kidneys in the test dataset, LR correctly classified 3422 kidneys, whereas RF correctly classified 4762 kidneys (area under the receiver operative curve [AUC]: 0.85 versus 0.888, and balanced accuracy: 0.681 versus 0.759). For the kidneys with kidney donor profile index of >85% (6079 total), RF significantly outperformed LR in classifying discard and transplant prediction (AUC: 0.814 versus 0.717, and balanced accuracy: 0.732 versus 0.657). More than 388 kidneys were correctly classified using RF. Including biopsy and machine perfusion variables improved the performance of LR and RF (LR's AUC: 0.888 and balanced accuracy: 0.74 versus RF's AUC: 0.904 and balanced accuracy: 0.775). CONCLUSIONS: Kidneys that are at risk of discard can be more accurately identified using ML techniques such as RF.

Physician and patient acceptance of policies to reduce kidney discard.

Despite the shortage of kidneys for transplantation in the United States, approximately 18%-20% of deceased donor kidneys are discarded each year. These discarded kidneys can offer a survival benefit to suitable patients. Revisions to the current kidney allocation policy may be needed to reduce deceased donor kidney discard. We surveyed transplant physicians and patients to assess their perceived acceptability of policy proposals to reduce the discard of deceased donor kidneys. Members of professional societies (AST, ASTS) and a patient organization (AAKP) were invited to complete the survey. Responses were obtained from 97 physicians and 107 patients. The majority of physicians (73.4%) and patients (73.8%) "somewhat" or "completely" accepted a policy for fast-tracking kidneys at risk of discard. Physicians and patients also supported returning a proportion of waiting time to patients who accept KDPI >85 kidneys and experience graft failure within the first 12 months, with 36% of physicians and 50% of patients electing to return 100% of the waiting time. The majority of physicians (75%) "somewhat or completely" accepted a policy to skip less aggressive centers for KDPI 90 + offers. Physicians and patients provided insights into factors researchers, and policymakers should consider in the design and implementation of these policies.

A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19.

We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.