Transplant data: sources, collection, and caveats

Dickinson, David M.; Bryant, Paula C.; Williams, Macey; Levine, G. N.; Li, Shiqian; Welch, James C.; Keck, Berkeley M.; Webb, Randall L.

doi:10.1111/j.1600-6135.2004.00395.x

Cited by 80 publications

(63 citation statements)

References 2 publications

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“…The OPTN data system includes data on all donor, wait-listed candidates, and transplant recipients in the United States, submitted by the members of the OPTN, and has been described elsewhere. 35 The Health Resources and Services Administration, U.S. Department of Health and Human Services provides oversight to the activities of the OPTN contractor. Through review of OPO charts, we augmented donor data with additional important elements, including admission serum creatinine.…”

Section: Data Sourcesmentioning

confidence: 99%

Associations between Deceased-Donor Urine Injury Biomarkers and Kidney Transplant Outcomes

Reese

Hall

Weng

et al. 2015

JASN

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Assessment of deceased-donor organ quality is integral to transplant allocation practices, but tools to more precisely measure donor kidney injury and better predict outcomes are needed. In this study, we assessed associations between injury biomarkers in deceased-donor urine and the following outcomes: donor AKI (stage 2 or greater), recipient delayed graft function (defined as dialysis in first week post-transplant), and recipient 6-month eGFR. We measured urinary concentrations of microalbumin, neutrophil gelatinaseassociated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), IL-18, and liver-type fatty acid binding protein (L-FABP) from 1304 deceased donors at organ procurement, among whom 112 (9%) had AKI. Each biomarker strongly associated with AKI in adjusted analyses. Among 2441 kidney transplant recipients, 31% experienced delayed graft function, and mean6SD 6-month eGFR was 55.7623.5 ml/min per 1.73 m 2 . In analyses adjusted for donor and recipient characteristics, higher donor urinary NGAL concentrations associated with recipient delayed graft function (highest versus lowest NGAL tertile relative risk, 1.21; 95% confidence interval, 1.02 to 1.43). Linear regression analyses of 6-month recipient renal function demonstrated that higher urinary NGAL and L-FABP concentrations associated with slightly lower 6-month eGFR only among recipients without delayed graft function. In summary, donor urine injury biomarkers strongly associate with donor AKI but provide limited value in predicting delayed graft function or early allograft function after transplant.

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Section: Data Sourcesmentioning

confidence: 99%

Associations between Deceased-Donor Urine Injury Biomarkers and Kidney Transplant Outcomes

Reese

Hall

Weng

et al. 2015

JASN

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“…The minimum time between date of transplantation and follow-up (defined as date of merger of the OPTN and DDS datasets) was 330 days, providing sufficient opportunity for centers to submit 6-month forms to the OPTN. The OPTN data system includes data on all donors, waitlisted candidates, and recipients of transplants in the United States submitted by the members of the OPTN and has been described elsewhere (1,15). The Health Resources and Services Administration, US Department of Health and Human Services provides oversight of the activities of the OPTN contractor.…”

Section: Data Sourcesmentioning

confidence: 99%

Validating Early Post–Transplant Outcomes Reported for Recipients of Deceased Donor Kidney Transplants

Potluri

Parikh

Hall

et al. 2016

Clinical Journal of the American Society of Nephrology

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Background and objectives Data reported to the Organ Procurement and Transplantation Network (OPTN) are used in kidney transplant research, policy development, and assessment of center quality, but the accuracy of early post-transplant outcome measures is unknown.Design, setting, participants, & measurements The Deceased Donor Study (DDS) is a prospective cohort study at five transplant centers. Research coordinators manually abstracted data from electronic records for 557 adults who underwent deceased donor kidney transplantation between April of 2010 and November of 2013. We compared the post-transplant outcomes of delayed graft function (DGF; defined as dialysis in the first posttransplant week), acute rejection, and post-transplant serum creatinine reported to the OPTN with data collected for the DDS.Results Median kidney donor risk index was 1.22 (interquartile range [IQR], 0.97-1.53). Median recipient age was 55 (IQR, 46-63) years old, 63% were men, and 47% were black; 93% had received dialysis before transplant. Using DDS data as the gold standard, we found that pretransplant dialysis was not reported to the OPTN in only 11 (2%) instances. DGF in OPTN data had a sensitivity of 89% (95% confidence interval [95% CI], 84% to 93%) and specificity of 98% (95% CI, 96% to 99%). Surprisingly, the OPTN data accurately identified acute allograft rejection in only 20 of 47 instances (n=488; sensitivity of 43%; 95% CI, 17% to 73%). Across participating centers, sensitivity of acute rejection varied widely from 23% to 100%, whereas specificity was uniformly high (92%-100%). Six-month serum creatinine values in DDS and OPTN data had high concordance (n=490; Lin concordance correlation =0.90; 95% CI, 0.88 to 0.92).Conclusions OPTN outcomes for recipients of deceased donor kidney transplants have high validity for DGF and 6-month allograft function but lack sensitivity in detecting rejection. Future studies using OPTN data may consider focusing on allograft function at 6 months as a useful outcome.

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“…Patients in the UNOS system have OPTN data transferred to the Scientific Registry for Transplant Recipients (SRTR), and the SRTR uses other data sources to supplement its information (10)(11)(12). During the past decade (53), the number of registrants on the UNOS lung transplant waiting list at any point during each year (i.e., the number at risk of dying) has hovered around 5,000.…”

Section: Literature Review Lung Transplantation: Survivalmentioning

confidence: 99%

“…Investigators should be aware of these methods and their limitations to allow better interpretation of the data. The UNOS methodologies have been previously reported in detail (8)(9)(10)(11)(12).…”

Section: Outcomes Assessment In Lung Transplantation: Survivalmentioning

confidence: 99%

Technology and Outcomes Assessment in Lung Transplantation

Yusen¹

2009

Proceedings of the American Thoracic Society

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Lung transplantation offers the hope of prolonged survival and significant improvement in quality of life to patients that have advanced lung diseases. However, the medical literature lacks strong positive evidence and shows conflicting information regarding survival and quality of life outcomes related to lung transplantation. Decisions about the use of lung transplantation require an assessment of trade-offs: do the potential health and quality of life benefits outweigh the potential risks and harms? No amount of theoretical reasoning can resolve this question; empiric data are needed. Rational analyses of these trade-offs require valid measurements of the benefits and harms to the patients in all relevant domains that affect survival and quality of life. Lung transplant systems and registries mainly focus outcomes assessment on patient survival on the waiting list and after transplantation. Improved analytic approaches allow comparisons of the survival effects of lung transplantation versus continued waiting. Lung transplant entities do not routinely collect quality of life data. However, the medical community and the public want to know how lung transplantation affects quality of life. Given the huge stakes for the patients, the providers, and the healthcare systems, key stakeholders need to further support quality of life assessment in patients with advanced lung disease that enter into the lung transplant systems. Studies of lung transplantation and its related technologies should assess patients with tools that integrate both survival and quality of life information. Higher quality information obtained will lead to improved knowledge and more informed decision making.

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Transplant data: sources, collection, and caveats

Cited by 80 publications

References 2 publications

Associations between Deceased-Donor Urine Injury Biomarkers and Kidney Transplant Outcomes

Associations between Deceased-Donor Urine Injury Biomarkers and Kidney Transplant Outcomes

Validating Early Post–Transplant Outcomes Reported for Recipients of Deceased Donor Kidney Transplants

Technology and Outcomes Assessment in Lung Transplantation

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