Sumit Mohan

Purpose of review: Genetic testing is increasingly common in the live organ donation context and raises numerous ethical questions. Recent findings: Existing guidelines and expert recommendations regarding genetic testing in live organ donation are limited, and leave many ethical questions unanswered. Summary: Decisions regarding use of genetic testing in live organ donation should be examined in light of existing ethical frameworks that guide live organ donation for all organ types.

Changes in organ allocation, technology, and regulatory policy have led to significant increases in deceased donor liver offers for transplantation. Expansion of potential donors is associated with inefficiencies, including increased liver nonutilization, resources, and complications. We evaluated the contribution of individual waitlisted candidates to the national allocation of deceased donor liver offers to understand characteristics of candidates with numerous offer turndowns. We performed an observational cohort study of the Scientific Registry of Transplant Recipients, including deceased donor liver offers in the United States between January 1, 2021, and December 31, 2024. We used standard and hierarchical multivariable logistic models to evaluate factors associated with high-frequency candidates (HFCs). There were 1,021,569 offers of 32,750 deceased donor livers to 55,124 waitlisted candidates with ≥1 offer. Waitlisted candidates had 10.4 months average waitlist follow-up and received a median of 9 (IQR=[4, 22]) offers. However, 46% of offers (n=468,699) went to 10% (HFC, n=5726), who had ≥45 offers over the period. HFC were disproportionally ages 18–39 (adjusted odds ratio [AOR]=1.58, 95% CI=1.33–1.86, relative to 70+), Black (AOR=1.31, 95% CI: 1.17–1.47, relative to White), type-O blood (AOR=1.14, 95% CI: 1.07–1.21, relative to type A), body mass index ≥35 kg/m ² (AOR=1.17, 95% CI: 1.06–1.28, relative to body mass index=20–24 kg/m ² ), MELD 12–17 (AOR=1.24, 95% CI: 1.15–1.34, relative to >25), metabolic dysfunction (AOR=1.32, 95% CI: 1.09–1.62, relative to cirrhosis) and history of portal vein thrombosis (AOR=1.34, 95% CI: 1.23–1.46). There was significant heterogeneity in HFC by transplant center (median=8%, IQR=[3%, 13%]), and centers with higher proportions of HFC had lower transplant rates and offer acceptance ratios ( p <0.001). HFCs who received transplants had fewer living donor transplants and transplanted with older age donors. Results indicate liver donor offers are disproportionately explained by a minority of candidates. Efforts to identify reasons for repetitive offer declines, strategic use of offer filters and transition of applicable candidates to inactive status may dramatically improve the efficiency of deceased donor liver allocation.

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The Centers for Medicare & Medicaid Services uses the Star quality program to evaluate Medicare Advantage (Medicare Part C) care quality. Since the 21st Century Cures Act, ESRD enrollment within Medicare has shifted from e.g ., customer service). The American Society of Nephrology committee evaluated 25 (of 45) Star quality measures that were directly related to clinical outcomes. The committee assessment identified 13 high-validity Star measures that monitored care related to diabetes, hypertension, and care transitions, as well as 12 medium-validity measures that evaluated preventive care and other chronic care areas. The appropriateness and clinical evidence base of Star measures were generally strong but weaker in patients with kidney failure. The committee noted concerns with ten Stars measures that may exclude patients with kidney failure. Only one measure captured guideline-directed kidney care (albuminuria screening in patients with diabetes). No Stars measures captured other guideline-directed recommendations, such as CKD screening and risk stratification, nephrology referral, use of kidney pharmacotherapies, CKD education, or preemptive kidney transplantation referral. The Medicare 2026 Part C and D Stars measures represent a missed opportunity to align care measures and guideline-directed kidney care to improve outcomes, reduce costs, and advance high-value care for the millions of Americans living with kidney disease.

Background: Kidney transplant allocation currently relies on the Kidney Donor Profile Index (KDPI), a measure which has limitations. This study evaluated the potential of using KDPI and deceased donor urinary biomarkers to improve the assessment of organ quality and the prediction of allograft outcomes compared with KDPI alone. Methods: Pairs of kidney recipients from the same donor transplanted between 2010 and 2013 were followed for 3 years post-transplant from kidneys procured at five OPOs. By 3 years post transplant, concordant good outcomes were defined as both recipients having an estimated glomerular filtration rate (eGFR) ≥45 mL/min/1.73m ² , while concordant poor outcomes were defined as both recipients experiencing graft failure (return to dialysis or waitlisted) or having an eGFR <20 mL/min/1.73m. Urinary biomarkers (uromodulin (UMOD), osteopontin (OPN), and chitinase-3-like protein 1 (YKL-40)) were measured by immunoassay using a MesoScale Device (MSD) in all donors, and by lateral flow devices in a subset. Discriminatory ability was quantified with the area under the receiver operator characteristics curve (AUC). Results: We identified 474 donors with both kidneys transplanted and either concordant good outcomes (438; 92%) or concordant poor outcomes (36, 8%). For distinguishing concordant good from concordant poor outcomes, the AUC for KDPI alone was 0.80 (SE 0.04); the AUC for KDPI combined with UMOD, OPN, and YKL-40 measured by immunoassay was 0.86 (SE 0.03). In a subset of 105 donors with lateral flow device measurements, the AUC for KDPI plus three biomarkers was 0.85 (SE 0.04), compared with 0.81 (SE 0.05) for KDPI alone. Conclusions: Combining donor urinary biomarkers with KDPI improves prediction of concordant allograft outcomes, regardless of assay platform. Lateral flow device measurements can be obtained at the point-of-care and may provide additional insight into expected deceased donor allograft function, potentially aiding the kidney transplant allocation process.

Suboptimal pharmaceutical drug performance remains a critical issue in healthcare, contributing to preventable mortality, morbidity, and economic burden. Despite advancements in drug development, a...

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Importance: Organ procurement organizations are increasingly turning to expedited (ie, out-of-sequence) allocation of deceased donor kidneys, citing maximal utility of kidneys at risk of nonuse. Objective: To identify and compare kidney pairs from the same donor from whom 1 kidney was transplanted in sequence and the other out of sequence. Design, Setting, and Participants: This retrospective cohort study used US kidney transplant registry data obtained from the Scientific Registry of Transplant Recipients to identify kidney transplant donors from whom at least 1 deceased donor kidney was transplanted out of sequence in 2020 through 2024. Main Outcomes and Measures: Incidence of unilateral out-of-sequence transplants, in which 1 kidney was placed in sequence and the other kidney from the same donor was placed out of sequence, and survival outcomes of recipients. Results: A total of 15 602 kidneys from 8544 deceased donors (5304 [62.1%] male; mean [SD] age at organ recovery, 44.4 [15.1] years) with at least 1 kidney transplanted out of sequence were included in the analysis. Of these, 4313 donors (50.5%) had a unilateral kidney placed out of sequence. Compared with recipients of unilateral in-sequence kidneys, recipients of unilateral out-of-sequence kidneys included a greater percentage of older (median [IQR] age at transplant, 60.0 [50.0-67.0] vs 57.0 [47.0-65.0] years), Asian (472 [10.9%] vs 282 [6.5%]), White (2391 [66.1%] vs 2197 [50.9%]), and male (2850 [66.1%] vs 2453 [56.9%]) recipients, as well as those with private insurance (1255 [29.1%] vs 990 [23.0%]) and preemptive transplant (739 [17.1%] vs 438 [10.2%]) (all P < .001). Unilateral out-of-sequence kidneys accounted for 38.2% of all out-of-sequence transplants over the course of the study period. From 2020 through 2024, unilateral out-of-sequence kidneys increased from 97 instances from 15 organ procurement organizations to 1698 kidneys from all 56 organizations in the US. The first instance of an out-of-sequence refusal code (ie, the point in the waiting list at which kidneys were first placed out of the standard sequence) decreased from median (IQR) sequence number 393 (155-889) in 2020 to 28 (11-77) in 2024. The proportion of donor-related reasons for refusal decreased 10% to 15% during the study period. In adjusted Cox models, outcomes for unilateral out-of-sequence transplant were not significantly different for patient survival (hazard ratio, 0.84 [95% CI, 0.70-1.02]; P = .08) or graft survival (hazard ratio, 0.87 [95% CI, 0.70-1.08]; P = .20), compared with unilateral in-sequence transplant. Conclusions and Relevance: Findings from this cohort study of unilateral out-of-sequence transplants highlight the importance of understanding donor and allocation factors that resulted in bypassing standard allocation and are critical to designing future iterations of the organ allocation system with improved organ use rates.