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CSR Frequently Asked Questions


General Questions
  1. What does ECD mean?
    ECD stands for expanded criteria donor—that is, a donor who either is over 60 years of age or is between 50 and 59 years of age and meets two of the following three conditions: died of a stroke, had a history of hypertension, or had a serum creatinine greater than 1.5.
  2. How does the SRTR define living vs. deceased donors?
    Living Donor
    A living person who donates an organ for transplantation, such as a kidney or a segment of a lung, liver, pancreas, or intestine. Living donors may be blood relatives, emotionally related individuals, or altruistic strangers.

    Deceased Donor
    An individual whose tissues or organs are donated after his or her death. Such donations come from two sources: patients who have suffered brain death and patients whose hearts have irreversibly stopped beating. The latter group is referred to as non-heart-beating or donation after cardiac death (DCD) donors. Throughout this report, we have used the term deceased donor instead of cadaveric donor.
  3. I have additional questions. How can I contact the SRTR?
    Please use our online data request form or call us at 800-830-9664. Details about the available data can be found here: data requests
General Questions about Program-Specific Reports
  1. Where can I find background information about the center-specific reports (CSRs)?
    The slide presentation, The SRTR Center-Specific Reporting Tools: Key Points, originally presented at the 2004 Transplant Administrator’s Conference and updated in 2005, gives a good introduction to the data sources and survival methodology used in the reports. These slides are free for you to use in any presentation, with appropriate attribution as specified within the presentation.
  2. On CSR Table 6, Time to Transplant for Waitlist Patients, why are some of the percentiles blank?
    Cells are blank if the center has not yet reached the corresponding percent of patients transplanted.
  3. How are multi-organ transplants reflected in the center-specific reports?
    For most tables, these transplants are counted in the CSR for each organ program. For example, a kidney-liver transplant would be counted in the transplant totals and characteristics table for both the kidney and liver programs. For the posttransplant survival tables, however, multi-organ transplants are excluded from these analyses, except for the kidney-pancreas transplant program reports (which analyze the results of kidney-pancreas transplants) and the intestine program reports (which include post-transplant outcomes calculated for liver-intestine, liver-pancreas, and liver-intestine-pancreas). Multi-organ transplants are excluded because they carry additional risks that may make it difficult to compare them to individual transplants of the same organs.
  4. If someone receives a transplant at one center and then moves away, with follow-up treatment or death at another center, how is that outcome reflected in the reports? Why are outcomes that occur after the patient leaves the care of the transplant center attributed to that transplant center?
    Post-Transplant: Center-specific patient and graft survival statistics post-transplant are based on patients who received a transplant at the center. All patients receiving a transplant at center A will have outcomes associated with center A, even if care is transferred to center B.

    These analyses take the perspective of asking the following question at the time of transplant: “At time X after the transplant, what percent of patients are alive (or have a functioning graft),” without regard for where the patient is being treated. All outcomes within the study timeframe are considered and are associated with that transplant, even if this center’s care has discontinued because of return to dialysis or other treatment, re-transplant at another center (for patient survival), or other reason for discontinuing care. Both positive and negative outcomes are considered: a surviving patient will contribute positively to the survival rate for a center just as a dying patient would count against survival.

    Waiting list: Transplant and mortality rates for patients on the waiting list are based on all patients listed at the center during the time they are on the waiting list. When a patient transfers to a different center, outcomes (transplant and mortality) will be part of the new center statistics starting on the day the patient is listed at the new center, and still reflected in the old center’s results for the time that the patient was listed there.
  5. How can I get previously published CSRs and OSRs?
    Previously published CSRs and OSRs are available on our website at CSR and OSR Archives.
  6. What is the timeline for each round and where I can find information on future rounds?
    Updated CSRs and OSRs are released every six months. Details on upcoming reports are available on the Report Timeline page.
  7. How can I easily compare statistics from one center to another?
    Use the National Summary for Select Tables by Center section of our website.
  8. I’m having trouble printing PDFs.
    PDFs: For best printing results, please select the "Download Report in Adobe Acrobat (PDF) Format." You will need to have Adobe Acrobat installed to view the reports. You can download this free program at www.adobe.com/acrobat
  9. How are the cohorts for the center-specific post-transplant survival statistics chosen, and why are they not the same as what is reported on the OPTN site?
    The SRTR is contracted to provide inferential statistical measures for a wide variety of audiences. Our choice of cohorts for the Center-Specific Reports reflects a balance between having the most recent information and having sufficient sample size to draw conclusions about differences between observed and expected survival for each center, given the patient case-mix at that center. The 2.5 year cohorts used for the CSRs reflect this balance. In many cases, a one-year cohort would be too small to draw such conclusions, and survival results for patients who were transplanted several years ago are not as pertinent to the goal of the CSRs. Each 2.5 year cohort is chosen to be the most recent transplants for which any reliable post-transplant outcomes can be calculated.

    More information about data sources and cohort choice can be found on our website in Chapter X of the 2005 Annual Report Analytical Methods and Database Design: Implications for Transplant Researchers, 2005.
  10. How are inactive patients treated in Waiting List (WL) calculations?
    The waitlist analyses in the SRTR's Center-Specific Reports (CSRs) document activity of all patients that have been waitlisted at a given center, including both active and inactive patients. More information on how data are tabulated within the CSRs is available in the Technical Methods, posted to our website under: Technical Methods.
  11. How frequently are the Program and OPO -Specific Report risk-adjusted models updated?
    All transplant program and OPO models are re-evaluated and updated as necessary every six months during each Center-Specific and OPO-Specific report release cycle.
Patient and Graft Survival Rate Questions
  1. What does “risk-adjusted” mean?
    Risk adjustment is a statistical method of accounting for the impact of variations in patient (or service area) characteristics on an outcome of interest (e.g., mortality).Using risk adjustment when comparing patient or facility outcomes helps us ask, “What result could we expect for similar patients (or service areas, for OPOs) based on the national experience?” A risk-adjusted expected value is usually a more appropriate comparison point than a national average, because it takes into account the differences between patients or service areas served by different transplant centers or OPOs.
  2. How do you choose what factors to include in risk adjustment?
    Selection of model covariates for risk adjustment is based on the entire body of analytical work performed by the SRTR for the OPTN committees and other groups. For each time period following the date of transplant, many separate models are estimated for each organ. Pediatric and adult transplants are evaluated with separate models because of different factors influencing pediatric survival (e.g., immune responsiveness and compliance with medications). Similarly, separate models are calculated for transplants from living and deceased donors, for patient and graft survival, and for different study endpoints (e.g., one-month versus three-year outcomes). Separating models allows us to use covariates specific to each transplant type; it also allows their effects to vary. For details, please see Chapter VIII of the 2005 Annual Report, SRTR Center-Specific Reporting Tools.

    Are the data available to be used in risk adjustment? The list of covariates that could be used in these models includes all the data elements collected by the OPTN during the cohort period. Characteristics that may be clinically significant cannot be included in the models unless they are collected consistently for all transplant patients in the country, creating a trade-off between full adjustment and data submission requirements for transplant centers.

    What are the known predictors of survival? From the list of available covariates, we focus on those shown to be important in SRTR analyses or the medical literature, such as donor and recipient age, medical characteristics, and compatibility (see Model Description Tables). We usually start by including variables that often display p-values below or nearly below 0.10, even if they may not be significant at the 0.10 level in this particular model. In some cases, decisions must be made about which specific variables to use to incorporate certain factors into the model when there are several highly associated variables to choose from. These decisions are based on significance, interpretability of coefficients, and data quality.

    Are there additional factors that we know or suspect are clinically significant? Based on input from clinical experts from the SRTR and the OPTN organ-specific committees, additional variables are tested for inclusion in the model. Some of these are only added to the models if they reach a certain level of statistical significance; others may be included regardless of their statistical significance because they are widely believed to have an effect on survival.

    Are we modeling each variable correctly? The proper form must be chosen for each covariate. Some variables may have a linear relationship with the outcome (e.g., cold ischemia time may be measured in effect per hour), while others use categories, allowing nonlinear relationships between the covariate and outcome. Often, categorical variables are chosen because of their versatility. In addition, interactions among variables in the model are examined.
  3. When living donor and deceased donor outcomes are combined to calculate overall survival outcome statistics, how is it possible that survival may become significantly different from what is expected when this is not the case for either subgroup?
    When divided up by donor type, the sample size for deceased and living donor transplants may be insufficient to conclude that the actual number of failures/deaths observed is statistically significantly higher or lower than the expected number. When dividing statistics up into interesting subgroups, the subgroups become small enough that the relationship originally observed will eventually become insignificant due to the small sample sizes, even if the relationship is strong overall. If the living and deceased donor failures/deaths are both higher or both lower than expected, the increase in sample size resulting from the combination of the two can still result in statistical significance for the overall number of failures/deaths.
  4. How can I find out what covariates are used in the risk adjustments?
    Each risk-adjustment model is published one month in advance of the CSRs. These models are presented as tables with the features described below in the Model Description Tables. For details, please see the Selecting Model Covariates section of Chapter VIII of the 2005 Annual Report, SRTR Center-Specific Reporting Tools.

    The beta, or calculated coefficient, shows the effect of each characteristic on expected risk of death or graft failure. Some users may be more familiar with the relative risk of each factor, which can be obtained by calculating exp(beta).

    The standard error and p-value indicate how much random variance there was around this estimate, and our degree of certainty that the given characteristic has a real effect.

    The index of concordance measures the goodness of fit for each model. This measure shows the percentage of variation in the order of events (deaths or graft failures) that is accurately predicted by the model. An index of concordance of 100% would suggest that the model perfectly predicts the order of events displayed in real life; 50% would suggest that the order is random with regard to predictors. Indexes of concordance are best for organs with many transplants in each cohort, such as liver and kidney for adult recipients.

    Models are repeated for a series of three different cohorts of transplants, allowing a comparison of how stable the coefficients are across time.
  5. How do you choose which transplants to calculate survival for in the CSRs? Can’t we get more recent data?

    The survival statistics in the CSR are based on those cohorts of patients who received the most recent transplants for which follow-up is available. Only transplants for which we expect at least a six-month follow-up form (one year for thoracic organs) can be included. To calculate one-year survival, much of the cohort needs to have at least one year of reported follow-up. Allowing time for form completion, report calculation, and center review, published reports are based on follow-up data for “transplant anniversaries” at least six months before publication—i.e., anniversaries of transplants received at least one year before publication.
  6. Why are survival cohorts for thoracic (heart and lung) patients different than the others prior to the July 2009 reports?
    No six-month follow-up forms were filed for thoracic patients prior to March 2008. Therefore, a year must have passed before a patient who received a thoracic transplant was included in the survival calculations. Thoracic cohorts began six months earlier than non-thoracic cohorts to account for this difference in reporting. Now that a six month follow-up form is submitted, follow-up is sufficient for a more recent cohort matching the cohorts of other organs.
  7. How are survival rates calculated?
    Graft and patient survival calculations are not simple ratios, but rather the Kaplan-Meier method is used to compute these measures. Survival at one month, one year, and three years is calculated from the follow-up data using the Kaplan-Meier (KM) method and is an estimate of the fraction of all grafts that would still be functioning at the reporting time point had they been followed until that time. The KM method uses all available data on each patient, including patients who were lost to follow-up before the end of the period. The KM method assumes that the failure rate for those patients lost to follow-up would be the same as the rate observed for those with complete data. Transplants that occurred in the last six months of the accrual period for the 1-year reporting time point are only followed for six months after transplant because the follow-up information is not yet available in the most recent data we have available, as is described in the Technical Methods.


    Patient Survival Table 11 reports patient survival (the proportion of patients who are still alive) at one month, one year, and three years after first transplantation for each organ. These survival rates use the Kaplan-Meier (KM) method (details in The SRTR Center-Specific Reporting Tools: Key Points) and reflect (1) data reported by the transplanting center to the OPTN, (2) data reported by other centers, and (3) data reported in additional data sources such as the Social Security Death Master File (SSDMF). If a patient’s death is not reported in any of these sources, the patient is assumed to be alive throughout the reporting period (i.e., until their most recent expected follow-up form). Further details available in Technical Methods.


    Graft Survival Graft survival at one month, one year, and three years is calculated from the follow-up data using the Kaplan-Meier (KM) method (details in The SRTR Center-Specific Reporting Tools: Key Points) and is an estimate of the fraction of all grafts that would still be functioning at the reporting time point had they been followed until that time. The KM method uses all available data on each patient, including patients who were lost to follow-up before the end of the period. The KM method assumes that the failure rate for those patients lost to follow-up would be the same as the rate observed for those with complete data. Further details available in Technical Methods.
  8. How is expected survival calculated?
    The slide presentation, The SRTR Center-Specific Reporting Tools: Key Points, originally presented at the 2004 Transplant Administrator’s Conference, and updated since then, gives a good background about the data sources and survival methodology. These slides are free for you to use in any presentation, with appropriate attribution as specified in the slide presentation.

    The expected number of deaths (or graft failures) is the number of deaths (failures) that we would expect based on the national experience for patients similar to those at this center. The national experience was analyzed using data for all transplants at all facilities in the United States during the cohort period. A Cox proportional hazards regression model for time to death (Cox 1972) was fitted to the national data. A regression model is essentially a mathematical formula that helps isolate the extent to which different factors (such as ABO compatibility and age of the donor) affect the survival experience of the recipient. This estimated regression equation is then applied to each patient at a given center to compute the expected number of deaths (graft failures) during the time that the patient is followed. Examples of the factors that are used in the statistical models include recipient and donor demographic characteristics, ABO compatibility, primary disease, donor cause of death, ischemia time, previous transplant, life support, HLA mismatch and PRA (KI), duration on dialysis (KI), and creatinine levels (LI). The specific risk factors are listed in the Technical Methods section of the SRTR website in the Model Description Tables. Multi-organ transplants are excluded from the analyses because their survival is not comparable to single-organ transplants. Further details available in the Technical Methods expected survival section.
    (1) Cox DR. Regression models and life tables (with discussion). J Roy Stat Soc, Series B 1972(34):197-220.
  9. Why are there no expected survival values for pediatric lung and kidney recipients?
    There are too few events to calculate statistically powerful expected patient survival values for pediatric lung and kidney recipients. For more information on patient survival statistics reported for each organ, see the Technical Methods for Table 11.
  10. Why aren't there exactly 5% of centers that have a survival rate that is significantly different than expected?
    The significance level is estimated using the universe of patients receiving transplants, but we do not just take the 5% of the centers with the highest values and call them "significantly different." In calculating significance, we say that if the centers' death rates vary randomly around the estimates we calculate based on facility characteristics according to a Poisson distribution*, then we would only see values as extreme as or more extreme than what was observed 5% of the time. Centers in the transplant universe will go beyond this threshold by luck (5%) or by violating the assumption that their deaths would be randomly distributed around their expected values. Centers with consistently poor practices are introducing a non-random component into their death rates (violating the assumption that the observations are randomly distributed according to a Poisson distribution), and are more likely to violate this "significance threshold" than centers with an "average" mix of practices.

    *A quick discussion of the Poisson:
    A Poisson distribution assumes that the probability of observing a death at a given center is related to the duration of the observation time (longer time spent observing = more deaths observed), that deaths are independent of one another, and that the probability of two deaths occurring within a short period of time is negligible. We also assume that the overall death rate isn't increasing or decreasing by much during the period used to calculate the center-specific characteristics. Using the Poisson distribution here is analogous to using the Normal distribution when calculating significance levels and confidence intervals using linear regression. A difference is the fact that the standard deviation and variance for the Poisson distribution is calculated based on the expected number of deaths; in the Normal distribution, the expected value (mean) is calculated separately from the variance. So the confidence limits we are calculating do not depend on the variance shown by the universe of transplant centers; we are not testing whether each center has a number of deaths within the range found among all centers (controlling for characteristics). We are saying for each center that if their deaths, after controlling for characteristics, were randomly distributed according to the assumptions used by the Poisson distribution (which we think are pretty reasonable), they would only violate the given threshold 5% of the time.
  11. If a center accepts expanded criteria donor (ECD) kidneys, won’t the lower quality of these organs be detrimental to post-transplant survival measures?
    No. While it’s true that we expect worse outcomes with ECD kidneys, all factors determining ECD status are accounted for in the expected survival rate against which observed survival is compared in the CSRs. Therefore, the expected survival rate for these kidneys will also be lower.
  12. Many of the post-transplant models reflect adjustment for prior events, such as a previous transplant or prior surgeries. Are these still reflected if they took place before the study time period?
    Yes. The purpose of including these covariates is to account for the fact that these previous events can, at any time, affect the expected survival. The “cohort period” defines the timeframe for transplants to be studied, and the follow-up period reflects the time after transplant that is examined for the follow-up event (e.g., death or graft failure). As long as prior surgeries or transplants are reflected on recipients’ Candidate or Transplant Registration forms, or we know about these events by looking at actual transplant history, they will be reflected in the model no matter when they occurred.
  13. Is it true that donation after cardiac death (DCD) status is not accounted for in risk-adjusted expected rates?
    Sometimes. Most models, especially those for which a substantial number of DCD organs have been used, adjust survival expectations on the basis of DCD organs. For CSRs released January 2005 and later, DCD was controlled for in all adult kidney models and all adult liver models except one-month survival; too few DCD donors were used for pediatric patients to obtain a stable estimate.
  14. What does "follow-up days reported by center" mean in CSR Tables 10 and 11?

    Follow up days reported is the percentage of days that are targeted for inclusion during the follow-up period relative to the number of days that were actually reported with OPTN transplant recipient follow-up forms. Days which were covered only by data from the SSDMF or CMS death data are not included in this percentage even though these days are included in the analyses. The numerator is from the date of transplant until the first of the date of death, the end of the follow up period, or the date the patient status was last reported for patient survival statistics. The denominator is from the date of transplant until the first of death or the end of the follow up period. For patients who did not die before the end of the period, the targeted number of days of follow-up is the entire period (30, 365, or 1096 days; see next section for details on maximum follow-up). For grafts transplanted during the last 6 months of the period, the targeted follow-up for 1 year survival is 6 months. For patients who died before the end of the period, the number of targeted days of follow-up is the number of days until death. This percentage is a measure of how dependent the results are on outside sources of data. A low percent of follow-up days reported indicates that there may be under-ascertainment of mortality for the facility since the completeness of follow-up data is partially determined by center reporting. Furthermore, it is worth noting that if a center reports one year follow-up after just 11 months, for example, the percent of follow up days would only be 91.7%. Similarly, if the center reports one year follow up for a patient after 18 months the percent would be 100 % even though this form was completed late. Although it can be related to compliance, this statistic does not actually measure compliance. In the graft survival tables for organs other than liver and heart, it measures the amount of censoring in the analysis (a lower follow up percentage means more censoring); censoring occurs when follow up data is not available. In the liver and heart graft survival tables and all the patient survival tables, it indicates how much time the analysis is depending entirely on sources of data other than center follow up forms (lower percentage means more dependence on external data sources such as Social Security Death Master file). Since these external sources are available, censoring is not necessary when center follow up data is not present.
  15. How are national averages calculated?
    The national experience was analyzed using data for all accrued transplants at all facilities in the United States. A Cox proportional hazards regression model for time to death (Cox 1972) was fitted to the national data, which yielded the probability of survival to the reporting time point for each patient, based upon the characteristics of each patient and the reporting time point. The expected survival rate is the average of these computed probabilities. The characteristics accounted for in these calculations are reported in the Technical Methods and are similar to those that have been used in previous reports. The expected patient survival rate for each organ was adjusted for the patient characteristics listed in the Model Description Tables. See Chapter VIII of the 2005 Annual Report for details on the calculation of the expected patient survival.
  16. What about non-transplant-related deaths?
    In evaluating the lifetime of a transplanted organ, both retransplant and death of the recipient are counted as transplant failures, even if the death was unrelated to transplantation. For kidney transplant recipients, return to dialysis is also reported and counted as organ failure. However, in order to understand the mechanisms that lead to transplant failure, it is sometimes useful to count only failures of the transplanted organ itself, not deaths from other causes. In order to study such mechanisms, the actuarial methods described in Chapter X of the 2005 Annual Report can be used for censoring the follow-up of an organ when a recipient dies without organ failure.
  17. When comparing one-year survival rates, how do you account for the difference between a death that happens in week 1 vs. a death in week 51?
    The slide presentation, SRTR Center-Specific Reporting Tools: Key Points, originally presented at the 2004 Transplant Administrator’s Conference and updated in 2005, gives good background information about the data sources and survival methodology. These slides are free for you to use in any presentation, with appropriate attribution. These reports allow for a distinction between deaths at different time points by reporting the ratio of observed to expected deaths. While the survival percentage at a given time point depends only on the number of deaths before that time point, not on the timing of those deaths, the expected number of deaths during an interval depends on the length of time the patients are followed. The follow-up time for each patient (time at risk) is the number of days from the date of transplantation until either death or the reporting time point (e.g., one month, one year, or three years), whichever is first. The number of expected deaths is the death rate expected for each patient multiplied by the time at risk for the patient. A patient dying in week 51 has 50 more weeks of follow-up time than a patient dying after just one week so that patients who die at 1 week would contribute less to the number of expected deaths than those who died at 51 weeks. EXAMPLE: Two centers with 10 recipients and a 20% expected death rate during the year for each patient. We expect two deaths during the year and therefore an 80% one-year survival rate. In both centers, two patients do die during the year, but in center A the deaths occur at 1 week and in center B the deaths occur at 51 weeks. The table below shows the patient survival, follow-up time, expected deaths, and ratio of observed to expected deaths at one year for these two hypothetical centers. Patient survival at one year is the same in these two cases, but the ratio of observed to expected deaths allows us to see that the deaths rates were higher than expected for center A and the same as expected for center B.
     Center A: 2 deaths at 1 weekCenter B: 2 deaths at 51 weeks
    1 Year Patient Survival 80% 80%
    Follow-up Years 8.04 years (= 8 years and 2 weeks = 1 year each for 8 pts alive at 1 year + 1 week each for 2 pts who died at 1 week) 9.96 years (= 9 years and 50 weeks = 1 year each for 8 pts alive at 1 year + 51 weeks each for 2 pts who died at 51 weeks)
    Observed Deaths 2 2
    Expected Deaths (= 20% death rate times the number of follow-up years) 1.61 1.99
    Ratio Observed to Expected Deaths 1.24 (=2 / 1.61) meaning that the death rates were 24% higher than expected 1.00 (= 2 / 1.99) meaning that the death rates were the same as expected
  18. If someone receives a transplant at one center and then moves away, with follow-up treatment or death at another center, how is that outcome reflected in the reports? Why are outcomes that occur after the patient leaves the care of the transplant center attributed to that transplant center?
    Post-Transplant: Center-specific patient and graft survival statistics post-transplant are based on patients who received a transplant at the center. All patients receiving a transplant at center A will have outcomes associated with center A, even if care is transferred to center B.

    These analyses take the perspective of asking the following question at the time of transplant: “At time X after the transplant, what percent of patients are alive (or have a functioning graft),” without regard for where the patient is being treated. All outcomes within the study timeframe are considered and are associated with that transplant, even if this center’s care has discontinued because of return to dialysis or other treatment, re-transplant at another center (for patient survival), or other reason for discontinuing care. Both positive and negative outcomes are considered: a surviving patient will contribute positively to the survival rate for a center just as a dying patient would count against survival.

    Waiting list: Transplant and mortality rates for patients on the waiting list are based on all patients listed at the center during the time they are on the waiting list. When a patient transfers to a different center, outcomes (transplant and mortality) will be part of the new center statistics starting on the day the patient is listed at the new center, and still reflected in the old center’s results for the time that the patient was listed there.
  19. If patients die after transplant due to an unrelated cause (e.g., another illness or a car accident), how are these deaths counted in the center’s post-transplant patient survival rate?
    All deaths are reflected in patient survival rates, including transplant-related and non-transplant-related deaths. Observed death rates should be compared to expected rates, which also include unrelated deaths, and are adjusted for characteristics (such as age) that are associated with both transplant- and non-transplant-related deaths.
  20. How are deaths reflected in the post-transplant graft survival rate?
    All deaths are counted as graft failures. After death, the graft is no longer supporting life, and it is not always clear whether or not a death may is transplant-related. For example, a patient may die because of poor handling of immunosuppression therapy, and still not be coded as a graft failure. Observed graft survival rates should be compared to expected rates, which also include deaths and are adjusted for characteristics (such as age) that are associated with both transplant- and non-transplant-related deaths, as well as graft failure.
  21. How are patient and graft annual survival statistics affected by the reporting of patients lost to follow-up (LTFU)?
    For center-specific reporting and most other analyses by the SRTR, a report of LTFU is not considered a failure (death or graft failure).

    For mortality analysis, we believe that we have reasonably complete ascertainment of death using multiple sources such as the SSDMF and other centers’ reporting, (see the 2005 Annual Report Chapter VIII (particularly Table 8 and the section Accounting for the Uncertainty of Loss to Follow-up) , so we assume a patient is alive – for the time periods that we believe we have complete reporting — unless we know otherwise. Therefore, a report of LTFU does not censor (end without event) the patient’s follow-up for mortality, but we extend time-at-risk and continue to count adverse events during this time.

    For most graft failure analyses, including analyses for the Center-Specific Reports, we do not have a complete source of follow-up in the same way that we have for mortality, and these analyses are censored at LTFU. Of course, censored is not equivalent to graft failure, but is the assumption that from the date of LTFU onward, the experiences of this patient would be similar to the experiences of patients who also survived to the same date (this is a technique used in Kaplan-Meier analyses).

    To the end that reports of patients being LTFU might impact the completeness of mortality or graft failure data reported in the CSRs, we do report the percentage of days of follow-up covered by center-reported events (i.e., excluding time after LTFU). This is only meant to help the user understand that our information is not always perfectly complete. It is our experience that survival rates may either increase or decrease, at a center-specific level, when extra ascertainment is added (since both extra events and time at risk are added).
  22. I understand that the SRTR uses the Kaplan-Meier method to impute outcomes for patients who have incomplete follow-up. Isn’t it dangerous to assume that the LTFU patients have similar outcomes to the followed patients?
    The SRTR uses the Kaplan-Meier method to calculate a survival rate (at one month, one year, and three years) because survival rates at a point in time are intuitive to understand. However, all p-values and comparisons between observed and expected survival are based on events observed and expected during the actual follow-up time, with no imputation.
  23. How frequently are the Program and OPO -Specific Report risk-adjusted models updated?
    All transplant program and OPO models are re-evaluated and updated as necessary every six months during each Center-Specific and OPO-Specific report release cycle.
  24. As models are updated and new covariates are added as adjustments to expected survival (e.g. DCD livers), what happens to centers that may have been previously flagged for worse than expected survival due to a larger than average experience with such transplants (i.e. expected survival would be worse using the updated models)?
    The SRTR does not re-calculate expected values for prior reports; however, the adjustment models for these reports are updated frequently (at each reporting cycle as needed) to reflect changes in policy and practice. In addition, the use of rolling cohorts assures that new covariates will be applied to data from previous reports and that obsolete adjustments will be removed as the new models are applied.
Questions for programs completing secure site review of data
  1. Why can’t I make changes to my program’s data after the deadline?
    The SRTR works to maintain fair and consistent policies regarding review of data and reports. Changing any data after analyses are completed affects all reports, not just those for your center. The SRTR sends a memo to all centers and OPOs in the month before the data capture listing all upcoming deadlines for submission. Institutions have the opportunity to review draft reports on the secure website twice a year, beginning on April 1st and October 1st, at a time when data can still be modified for the final report. Once tables are finalized, centers have an opportunity to comment on the reports before they are available for public viewing. The comments, which will be published with the reports on the the public website, may explain irregularities in the data reported to the OPTN, upon which the SRTR statistics are based.

    Current deadlines for center-specific and OPO reports can be found on the Report Timeline page
  2. Why isn’t all of the time for follow-ups that I’ve submitted counted toward my Kaplan-Meier survival rate?
    Consideration of both the lag time until validation of follow-up forms after transplant and the pattern of form submission — often clustered soon after transplant anniversaries — are important in avoiding biases when analyzing recent data. See Chapter X of the 2005 Annual Report , discussion of Timing of Follow-up Forms.

    The last follow-up date is the date of the last known status of a patient (whether they are alive or dead). Patients who are alive will only have follow-up status reported at six months, one year, two years, and so on after transplant, (i.e., the dates when the follow-up forms are due). When a patient dies, however, the center can turn in the follow-up form early. Following patients until the last known OPTN follow-up date will include extra time for patients who die and whose follow-up forms were turned in early (between due dates for the forms), but will not include this extra time for patients who are alive.

    For example, suppose two patients received transplants on the same date and the database includes their six-month follow-up forms but not their one-year follow-up forms. Suppose that one of these patients dies nine months after receiving a transplant and the transplant center immediately turns in the one-year follow-up form indicating that the patient has died. If we use only the OPTN last follow-up date and forms, the patient who died would contribute nine months and a death to the analysis, while the patient who lived would contribute only six months to the analysis. The fact that we only record the extra follow-up time if a patient dies could bias the analysis.
  3. How do I make changes to my program’s data?
    Centers are not required to submit any data to the SRTR, which receives all of its data from OPTN/UNOS. The only thing centers need to do to ensure that we have accurate information is ensure that their forms are all filed completely and correctly in UNet by the deadline (usually October 31 or April 30 for reports published in January or July, respectively). Current deadlines for center-specific and OPO reports can be found on the Report Timeline page
  4. How do I get a password?
    Please call the SRTR at 800-830-9664. For security reasons, we are unable to email passwords or user IDs.
  5. How do I change contact information (e.g., password contacts)?
    Please contact the SRTR at 800-830-9664.
  6. I can not access the secure site.
    Please bookmark this address: https://securesrtr.transplant.hrsa.gov. You will need the correct user ID and password to access secure content. Please reference the letter sent to you by the SRTR. If you are still having trouble, please contact the SRTR at 800-830-9664, or by email at srtr@arborresearch.org.
  7. Why aren’t all of the graft failure or death events listed on the Kaplan-Meier survival spreadsheet included as events in the Kaplan-Meier calculation?
    Consideration of both the lag time until validation of follow-up forms after transplant and the pattern of form submission — often clustered soon after transplant anniversaries — are important in avoiding biases when analyzing recent data. See Chapter X of the 2005 Annual Report , discussion of Timing of Follow-up Forms.

    The last follow-up date is the date of the last known status of a patient (whether they are alive or dead). Patients who are alive will only have follow-up status reported at six months, one year, two years, and so on after transplant, (i.e., the dates when the follow-up forms are due). When a patient dies, however, the center can turn in the follow-up form early. Following patients until the last known OPTN follow-up date will include extra time for patients who die and whose follow-up forms were turned in early (between due dates for the forms), but will not include this extra time for patients who are alive.

    For example, suppose two patients received transplants on the same date and the database includes their six-month follow-up forms but not their one-year follow-up forms. Suppose that one of these patients dies nine months after receiving a transplant and the transplant center immediately turns in the one-year follow-up form indicating that the patient has died. If we use only the OPTN last follow-up date and forms, the patient who died would contribute nine months and a death to the analysis, while the patient who lived would contribute only six months to the analysis. The fact that we only record the extra follow-up time if a patient dies could bias the analysis.
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Transplant Data

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The SRTR is administered by the Arbor Research Collaborative for Health with the University of Michigan,
with oversight and funding from the Health Resources and Services Administration.

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