Measuring and monitoring performance--be it waiting list and posttransplant outcomes by a transplant center, or organ donation success by an organ procurement organization and its partnering hospitals--is an important component of ensuring good care for people with end-stage organ failure. Many parties have an interest in examining these outcomes, from patients and their families to payers such as insurance companies or the Centers for Medicare and Medicaid Services; from primary caregivers providing patient counseling to government agencies charged with protecting patients. The Scientific Registry of Transplant Recipients produces regular, public reports on the performance of transplant centers and organ procurement organizations. This article explains the statistical tools used to prepare these reports, with a focus on graft survival and patient survival rates of transplant centers--especially the methods used to fairly and usefully compare outcomes of centers that serve different populations. The article concludes with a practical application of these statistics--their use in screening transplant center performance to identify centers that may need remedial action by the OPTN/UNOS Membership and Professional Standards Committee.

Access to timely, risk-adjusted measures of transplant center outcomes is crucial for program quality improvement. The cumulative summation technique (CUSUM) has been proposed as a sensitive tool to detect persistent, clinically relevant changes in transplant center performance over time. Scientific Registry of Transplant Recipients data for adult kidney and liver transplants (1/97 to 12/01) were examined using logistic regression models to predict risk of graft failure (kidney) and death (liver) at 1 year. Risk-adjusted CUSUM charts were constructed for each center and compared with results from the semi-annual method of the Organ Procurement and Transplantation Network (OPTN). Transplant centers (N = 258) performed 59 650 kidney transplants, with a 9.2% 1-year graft failure rate. The CUSUM method identified centers with a period of significantly improving (N = 92) or declining (N = 52) performance. Transplant centers (N = 114) performed 18 277 liver transplants, with a 13.9% 1-year mortality rate. The CUSUM method demonstrated improving performance at 48 centers and declining performance at 24 centers. The CUSUM technique also identified the majority of centers flagged by the current OPTN method (20/22 kidney and 8/11 liver). CUSUM monitoring may be a useful technique for quality improvement, allowing center directors to identify clinically important, risk-adjusted changes in transplant center outcome.

We tested the hypothesis that the renin inhibitor aliskiren ameliorates organ damage in rats transgenic for human renin and angiotensinogen genes (double transgenic rat [dTGR]). Six-week-old dTGR were matched by albuminuria (2 mg per day) and divided into 5 groups. Untreated dTGR were compared with aliskiren (3 and 0.3 mg/kg per day)-treated and valsartan (Val; 10 and 1 mg/kg per day)-treated rats. Treatment was from week 6 through week 9. At week 6, all groups had elevated systolic blood pressure (BP). Untreated dTGR showed increased BP (202±4 mm Hg), serum creatinine, and albuminuria (34±5.7 mg per day) at week 7. At week 9, both doses of aliskiren lowered BP (115±6 and 139±5 mm Hg) and albuminuria (0.4±0.1 and 1.6±0.6 mg per day) and normalized serum creatinine. Although high-dose Val lowered BP (148±4 mm Hg) and albuminuria (2.1±0.7 mg per day), low-dose Val reduced BP (182±3 mm Hg) and albuminuria (24±3.8 mg per day) to a lesser extent. Mortality was 100% in untreated dTGR and 26% in Val (1 mg/kg per day) treated rats, whereas in all other groups, survival was 100%. dTGR treated with low-dose Val had cardiac hypertrophy (4.4±0.1 mg/g), increased left ventricular (LV) wall thickness, and diastolic dysfunction. LV atrial natriuretic peptide and β-myosin heavy chain mRNA, albuminuria, fibrosis, and cell infiltration were also increased. In contrast, both aliskiren doses and the high-dose Val lowered BP to a similar extent and more effectively than low-dose Val. We conclude that in dTGR, equieffective antihypertensive doses of Val or aliskiren attenuated end-organ damage. Thus, renin inhibition compares favorably to angiotensin receptor blockade in reversing organ damage in dTGR.

Background - Aldosterone and angiotensin (Ang) II both may cause organ damage. Circulating aldosterone is produced in the adrenals; however, local cardiac synthesis has been reported. Aldosterone concentrations depend on the activity of aldosterone synthase (CYP11B2). We tested the hypothesis that reducing aldosterone by inhibiting CYP11B2 or by adrenalectomy (ADX) may ameliorate organ damage. Furthermore, we investigated how much local cardiac aldosterone originates from the adrenal gland.Methods and Results - We investigated the effect of the CYP11B2 inhibitor FAD286, losartan, and the consequences of ADX in transgenic rats overexpressing both the human renin and angiotensinogen genes (dTGR). dTGR-ADX received dexamethasone and 1% salt. Dexamethasone-treated dTGR-salt served as a control group in the ADX protocol. Untreated dTGR developed hypertension and cardiac and renal damage and had a 40% mortality rate (5/13) at 7 weeks. FAD286 reduced mortality to 10% (1/10) and ameliorated cardiac hypertrophy, albuminuria, cell infiltration, and matrix deposition in the heart and kidney. FAD286 had no effect on blood pressure at weeks 5 and 6 but slightly reduced blood pressure at week7 (177 +/- 6 mm Hg in dTGR +/- FAD286 and 200 +/- 5 mm Hg in dTGR). Losartan normalized blood pressure during the entire study. Circulating and cardiac aldosterone levels were reduced in FAD286 or losartan- treated dTGR. ADX combined with dexamethasone and salt treatment decreased circulating and cardiac aldosterone to barely detectable levels. At week 7, ADX-dTGR-dexamethasone-salt had a 22% mortality rate compared with 73% in dTGR-dexamethasone-salt. Both groups were similarly hypertensive (190 +/- 9 and 187 +/- 4 mm Hg). In contrast, cardiac hypertrophy index, albuminuria, cell infiltration, and matrix deposition were significantly reduced after ADX ( P < 0.05).Conclusions - Aldosterone plays a key role in the pathogenesis of Ang II - induced organ damage. Both FAD286 and ADX reduced circulating and cardiac aldosterone levels. The present results show that aldosterone produced in the adrenals is the main source of cardiac aldosterone.

The process of collecting and analyzing transplant data is complex. Familiarity with how these data are collected is crucial to a thorough understanding of the information. This article focuses on available OPTN-SRTR data and the continuing evolution of data collection mechanisms; how that data collection system is improving the data quality and reducing the data collection burden; how additional ascertainment of outcomes both completes and validates existing data; and caveats that remain for researchers. This year's article focuses further on research considerations related to cohort choice, timing of data submission, and potential biases in follow-up data. Ongoing improvements in data collection timeliness and scope are covered. The impact of extra ascertainment of outcomes, particularly for post-transplant kidney graft failure from Medicare data, are also examined. A section on graft failure reporting among different sources traces the steps by which the SRTR reconciles different data sources in its analyses. It is important that those reading and conducting transplant research understand the origin, structure, and scope of the available data. All of these issues should be carefully considered when choosing cohorts and data sources for analysis.

It is highly desirable to base decisions designed to improve medical practice or organ allocation policies on the analyses of the most recent data available. Yet there is often a need to balance this desire with the added value of evaluating long-term outcomes (e.g. 5-year mortality rates), which requires the use of data from earlier years. This article explains the methods used by the Scientific Registry of Transplant Recipients in order to achieve these goals simultaneously.The analysis of waiting list and transplant outcomes depends strongly on statistical methods that can combine data from different cohorts of patients that have been followed for different lengths of time. A variety of statistical methods have been designed to address these goals, including the Kaplan-Meier estimator, Cox regression models, and Poisson regression.An in-depth description of the statistical methods used for calculating waiting times associated with the various types of organ transplants is provided. Risk of mortality and graft failure, adjusted analyses, cohort selection, and the many complicating factors surrounding the calculation of follow-up time for various outcomes analyses are also examined.

By examining the sources, quality and organization of transplant data available, as well as making observations about data reporting patterns and accuracy, we hope to improve understanding of existing results, help researchers with study design and stimulate new exploratory initiatives. The primary data source, collected by the OPTN, has benefited from extensive recent technological advances. Transplant professionals now report patient and donor data more easily, quickly, and accurately, improving data timeliness and precision. Secondary sources may be incorporated, improving the accuracy and expanding the scope of analyses. For example, auxiliary mortality data allows more accurate survival analysis and conclusions regarding the completeness of center-reported post-transplant follow-up. Furthermore, such sources enable examination of outcomes not reported by centers, such as mortality after waiting list removal, providing more appropriate comparisons of waiting list and post-transplant mortality. Complex collection and reporting processes require specific analytical methods and may lead to potential pitfalls. Patterns in the timing of reporting adverse events differ from those for 'positive' events, yielding the need for care in choosing cohorts and censor dates to avoid bias. These choices are further complicated by the use of multiple sources of data, with different time lags and reporting patterns.

Effective use of GIS data in distributed hydrologic modeling requires understanding of type, structure, and scale of geospatial data used to represent the watershed and runoff processes. GIS data often lacks sufficient detail, space-time resolution, attributes, or differs in some fundamental way from how the model expects the character of the parameter and how the parameter is measured. Existing data sources are often surrogate measures that attempt to represent a particular category with direct or indirect relation to the parameter or physical characteristic. Generation of a surface from measurements taken at points, reclassification of generalized map categories into parameters, and extraction of terrain attributes from digital elevation data are important operations in the preparation of a distributed hydrologic model using GIS. Having transformed the original map into useable parameter maps, the parameter takes on the characteristic data structure of the original map. Thus, the data structure inherent in the original GIS map has lasting influence on the hydrologic process simulated using the derived parameters. In the following chapter we turn to the generation of surfaces from point data.