United Network Of Organ Sharing Database Research Articles

The effect of donor–recipient sex matches on lung transplant survival: An analysis of the United Network for Organ Sharing database

ObjectiveTo investigate the impact of donor–recipient (DR) sex matches on survival after lung transplantation while controlling for size difference in the United Network of Organ Sharing (UNOS) database. MethodsWe performed a retrospective study of 27,423 lung transplant recipients who were reported in the UNOS database (January 2005-March 2020). Patients were divided into groups based on their respective DR sex match: male to male (MM), male to female (MF), female to female, (FF), and female to male (FM). Kaplan–Meier curve and Cox regression with log-rank tests were used to assess 1-, 3-, 5-, and 10-year survival. We also modeled survival for each group after controlling for size-related variables via the Cox regression. ResultsKaplan–Meier curves showed overall significance at 1-, 3-, 5-, and 10-year end points (P < .0001). Estimated median survival time based on Kaplan–Meier analysis were 6.41 ± 0.15, 6.13 ± 0.18, 5.86 ± 0.10, and 5.37 ± 0.17 years for FF, MF, MM, and FM, respectively (P < .0001). After we controlled for size differences, FF had statistically significantly longer 5- and 10-year survival than all other cohorts. MF also had statistically significantly longer 5- and 10-year survival than FM. ConclusionsWhen variables associated with size were controlled for, FF had improved survival than other DR groups. A female recipient may experience longer survival with a female donor’s lungs versus a male donor’s lungs of similar size.

Improved outcomes of kidney after liver transplantation after the implementation of the safety net policy.

The number of kidney after liver transplants (KALT) increased after the implementation of the United Network of Organ Sharing (UNOS) safety net policy, but the effects of the policy on KALT outcomes remain unknown. Using the UNOS database, we identified KALT between 60 and 365 days from liver transplant from January 1, 2010, to December 31, 2020. The main outcome was 1- and 3-year patient, liver, and kidney graft survival. Secondary outcomes included 6-month and 1-year acute rejection (AR) of liver and kidney, and 1-year kidney allograft function. Of the 256 KALT, 90 were pre-policy and 166 post-policy. Compared to pre-policy, post-policy 1- and 3-year liver graft survival was higher (54% and 54% vs. 86% and 81%, respectively, p <0.001), while 1- and 3-year kidney graft survival (99% and 75% vs. 92% and 79%, respectively, p =0.19), and 1- and 3-year patient survival (99% and 99% vs. 95% and 89%, respectively, p =0.11) were not significantly different. Subgroup analysis revealed similar trends in patients with and without renal failure at liver transplant. Liver AR at 6 months was lower post-policy (6.3% vs. 18.3%, p =0.006) but was similar (10.5% vs. 13%, p =0.63) at 1 year. Kidney AR was unchanged post-policy at 6 months and 1 year. Creatinine at 1 year did not differ post-policy versus pre-policy (1.4 vs. 1.3mg/dL, p =0.07) despite a higher proportion of deceased donors, higher Kidney Donor Profile Index, and longer kidney cold ischemia time post-policy ( p <0.05 for all). This 3-year follow-up after the 2017 UNOS policy revision demonstrated that the safety net implementation has resulted in improved liver outcomes for patients who underwent KALT with no increased AR of the liver or the kidney allografts.

Temporal shift and predictive performance of machine learning for heart transplant outcomes.

Outcome prediction following heart transplant is critical to explaining risks and benefits to patients and decision-making when considering potential organ offers. Given the large number of potential variables to be considered, this task may be most efficiently performed using machine learning (ML). We trained and tested ML and statistical algorithms to predict outcomes following cardiac transplant using the United Network of Organ Sharing (UNOS) database. We included 59,590 adult and 8,349 pediatric patients enrolled in the UNOS database between January 1994 and December 2016 who underwent cardiac transplantation. We evaluated 3 classification and 3 survival methods. Algorithms were evaluated using shuffled 10-fold cross-validation (CV) and rolling CV. Predictive performance for 1 year and 90 days all-cause mortality was characterized using the area under the receiver-operating characteristic curve (AUC) with 95% confidence interval. In total, 8,394 (12.4%) patients died within 1 year of transplant. For predicting 1-year survival, using the shuffled 10-fold CV, Random Forest achieved the highest AUC (0.893; 0.889-0.897) followed by XGBoost and logistic regression. In the rolling CV, prediction performance was more modest and comparable among the models with XGBoost and Logistic regression achieving the highest AUC 0.657 (0.647-0.667) and 0.641(0.631-0.651), respectively. There was a trend toward higher prediction performance in pediatric patients. Our study suggests that ML and statistical models can be used to predict mortality post-transplant, but based on the results from rolling CV, the overall prediction performance will be limited by temporal shifts inpatient and donor selection.

Propensity Score Modeling in Electronic Health Records with Time-to-Event Endpoints: Application to Kidney Transplantation

For large observational studies lacking a control group (unlike randomized controlled trials, RCT), propensity scores (PS) are often the method of choice to account for pre-treatment confounding in baseline characteristics, and thereby avoid substantial bias in treatment estimation. A vast majority of PS techniques focus on average treatment effect estimation, without any clear consensus on how to account for confounders, especially in a multiple treatment setting. Furthermore, for time-to event outcomes, the analytical framework is further complicated in presence of high censoring rates (sometimes, due to non-susceptibility of study units to a disease), imbalance between treatment groups, and clustered nature of the data (where, survival outcomes appear in groups). Motivated by a right-censored kidney transplantation dataset derived from the United Network of Organ Sharing (UNOS), we investigate and compare two recent promising PS procedures, (a) the generalized boosted model (GBM), and (b) the covariate-balancing propensity score (CBPS), in an attempt to decouple the causal effects of treatments (here, study subgroups, such as hepatitis C virus (HCV) positive/negative donors, and positive/negative recipients) on time to death of kidney recipients due to kidney failure, post transplantation. For estimation, we employ a 2-step procedure which addresses various complexities observed in the UNOS database within a unified paradigm. First, to adjust for the large number of confounders on the multiple sub-groups, we fit multinomial PS models via procedures (a) and (b). In the next stage, the estimated PS is incorporated into the likelihood of a semi-parametric cure rate Cox proportional hazard frailty model via inverse probability of treatment weighting, adjusted for multi-center clustering and excess censoring, Our data analysis reveals a more informative and superior performance of the full model in terms of treatment effect estimation, over sub-models that relaxes the various features of the event time dataset.

MELD-Na Accurately Predicts 6-Month Mortality in Patients With Decompensated Cirrhosis: Potential Trigger for Hospice Referral.

The goal of this study was to determine the accuracy of Model of End-stage Liver Disease-Sodium (MELD-Na) in predicting 6-month mortality for patients listed for liver transplantation on the United Network of Organ Sharing (UNOS) waitlist. End-stage liver disease patients underutilize hospice services despite significant morbidity and mortality associated with advanced liver disease. A well-known barrier to hospice referral is clinician uncertainty in identifying patients with an expected survival of <6 months, a requirement for a referral. Retrospective cross-sectional analysis was performed from UNOS data spanning February 27, 2002, to September 30, 2019. Inclusion criteria of patients aged 18 years and above, diagnosis of cirrhosis, liver transplant eligible, and listed in the UNOS database. Exclusion criteria included fulminant hepatic failure, prior history of liver transplantation, diagnosis of hepatocellular carcinoma, receipt of liver transplant in <180 days, or removal from waiting list <180 days for a reason other than death. Mortality by 180 days. Of the 93,157 patients that met inclusion criteria, MELD-Na was calculated for all patients with sodium, total bilirubin, international normalized ratio, and creatinine available (N=79,611). The c -statistic with 95% confidence interval for MELD-Na for the predicted 6-month mortality was 0.83 (0.827-0.835). Mean MELD-Na of 28.2 was associated with ≤50% 6-month survival. MELD-Na is an objective, quick measure that can aid providers in identifying patients with increased 6-month mortality in time-constrained settings, and a score of 28 can trigger the discussion for hospice as a means of improving value-based health care.

Temporal shift and accuracy of machine learning in heart transplant outcomes

Abstract Background Accurate prediction of outcomes following a heart transplant is critical to explaining risks and benefits to patients and decision-making when considering potential organ offers. Given the large number of potential variables to be considered, this task may be most efficiently performed using machine learning (ML). Purpose We trained and tested different ML algorithms to accurately predict outcomes following a cardiac transplant using the United Network of Organ Sharing (UNOS) database. Methods We included 67 939 adult and pediatric patients enrolled in the UNOS database between January 1994 and December 2016 who underwent cardiac transplantation (median age 53 [IQR 38 – 60], 72.7% males). In our models, as an input, we included 114 features that have been collected from recipients and donors prior to transplant. The primary outcome was all-cause mortality at one-year post-transplant. We evaluated three different ML methods: XGBoost, Random Forest (RF) and L2 regularized logistic regression. Algorithms were trained and tested using shuffled 10-fold cross-validation (CV) as well as rolling CV. In the rolling CV, to mimic prospective procedure, ML models were trained by incrementally adding patients according to transplant year and testing models on the data in the following year. The hyperparameters, controlling the learning process, were tuned using Bayesian optimization. Prognostic accuracy for one-year all-cause mortality was characterized using the area under the receiver-operating characteristic curve (AUC). Results In total, 8,394 patients died within 1 year of transplant. We observed a substantial difference in prognostic accuracy between the shuffled 10-fold CV and the rolling CV. In the 10-fold CV, XGBoost and RF achieved high predictive performance with AUC of 0.848 (95% CI: 0.842–0.854) and 0.891 (95% CI: 0.886–0.896), respectively. In the rolling CV, which is a more realistic setting, AUC dropped to 0.673 (95% CI: 0.661–0.684) for XGBoost and 0.670 (0.657–0.683) for RF. Predictive performance of L2 regularized logistic regression remained stable across the two CV procedures, achieving AUC 0.669 (95% CI: 0.662–0.676) in the 10-fold shuffled CV and 0.665 (95% CI: 0.649–0.680) in the rolling CV procedure (Figure). Conclusions Our study suggests that ML models could be used to predict mortality in the first year post-transplant. We also show that the choice of CV procedure is crucial for evaluating ML models, particularly in data collected over a long period of time. The difference between the shuffled and rolling CV in the predictive performance of the tree-based ML models might indicate temporal dataset shift. In the rolling CV, all three methods achieved similar predictive performance. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders (FWO)

Marginal Improvement in Survival Post-Heart Transplantation in Patients With Prior Left Ventricular Assist Device: A Temporal Analysis of United Network of Organ Sharing Registry

An increasing number of patients undergoing heart transplantation are being bridged with left ventricular assist devices (LVADs). Bridge-to-transplantation (BTT) LVAD has improved wait list survival remarkably. Historically, post-heart transplantation survival in BTT-LVAD patients, however, has remained inferior to that of primary heart transplantation. The authors hypothesized that in the modern era, the difference between post-heart transplantation survival in BTT-LVAD versus primary heart transplantation should be reduced. The objective of the present study was to determine whether there has been a change in survival after heart transplantation in patients with prior LVAD. The present study's cohort was compared with a historical cohort using the United Network of Organ Sharing (UNOS) database from 1995 to 2004.5 DESIGN: Retrospective observational analysis of data from the United Network of Organ Sharing database. Registry-based, observational, retrospective. Patients undergoing adult orthotopic heart transplantation, excluding redo transplantation and multiorgan transplantations. None. From the UNOS database, 22,065 patients who underwent heart transplantation between January 1, 2006, and December 31, 2016, were analyzed. Of these, 7,008 (31.76%) patients had prior LVAD (BTT-LVAD). Data analysis was performed with R software (Version 3.5.1) for Kaplan-Meier survival analysis and Cox proportional hazard ratio (HR) modeling to identify variables influencing survival. For patients with prior LVAD, the overall HR was 1.15 (95% confidence interval [CI] 1.07-1.24) for survival. An HR of 3.22 (95% CI 2.23-4.68) for death in patients who received extracorporeal membrane oxygenation post-transplantation and an HR of 0.72 (95% CI 0.58-0.90) for survival in patients whose procedures were performed in high-volume centers performing more than 35 transplantations per year were identified. Reduced survival in patients who received an LVAD before heart transplantation persists. However, there may have been a slight improvement in the HR for survival in the study cohort in the recent decade compared with the historical cohort from previous decades. It is intriguing that despite the paramount advances in both technology and clinical practice of LVAD, relatively minor survival benefit, if any, has occurred in post-heart transplantation for patients bridged with prior LVAD.

Survival on the Heart Transplant Waiting List: Impact of Continuous Flow Left Ventricular Assist Device as Bridge to Transplant

Continued donor organ shortage and improved outcomes with current left ventricular assist device (LVAD) technology have increased the number of patients supported with bridge-to-transplantation (BTT) therapy. Using the United Network of Organ Sharing (UNOS) database, we assessed the impact on survival in patients supported with BTT while on the heart transplant waiting list. The UNOS database was queried from January 2005 to June 2012 to identify patients listed for heart transplantation as UNOS status 1A or 1B. Patients implanted with a pulsatile-flow device or an LVAD other than the HeartMate II (HM II; Thoratec Inc, Pleasanton, CA) were excluded. Patients were divided into LVAD and non-LVAD groups based on status at the time of listing. Patients were propensity matched (LVAD -non-LVAD = 1:2) for age, sex, weight, presence of diabetes, creatinine levels, mean pulmonary artery pressure, and UNOS status. Kaplan-Meier curves were analyzed for survival. A total of 8,688 patients were analyzed, with 1,504 (17%) in the LVAD group. Average age (52.6 ± 11.8 versus 51.3 ± 12.9 years; p = 0.0002) and weight (86.6 ± 18.6 versus 80.8 ± 18.2 kg; p < 0.0001) at time of listing were higher in the LVAD group. There were more men (79% versus 74%; p < 0.0001) and more patients with diabetes (30% versus 27%; p = 0.03) in the LVAD group. Of all patients, 6,943 patients (80%) underwent transplantation, 862 (10%) died, and 883 (10%) remained on the waiting list. After propensity matching, survival to transplantation was significantly better in the LVAD group than in the non-LVAD group at both 1 year (91% versus 77%) and 2 years (85% versus 68%). Patients supported with an HM II LVAD as BTT therapy were older with increased comorbidities; they demonstrated an improved survival while listed for heart transplantation. The use of LVADs as a BTT strategy can potentially improve patient survival while waiting for transplantation and allow better allocation of donor hearts.

Is mechanically bridging patients with a failing cardiac graft to retransplantation an effective therapy? Analysis of the United Network of Organ Sharing database

The results of bridging patients with cardiac allograft failure to retransplantation (ReTx) with mechanical circulatory support (MCS) have not been well studied. The United Network of Organ Sharing (UNOS) database was used to analyze outcomes of patients successfully bridged with MCS to cardiac ReTx. Of 1,690 cardiac ReTx identified in the UNOS database from October 1987 to July 2011, 149 (8.8%) were bridged to ReTx with MCS. Patients bridged to ReTx with MCS had a poorer survival than patients not bridged (p < 0.0001). ReTx after ventricular assist device (VAD) support had better survival than ReTx after extracorporeal membrane oxygenation (ECMO; half-life, 3.9 years vs 61 days, p = 0.026). For patients bridged to ReTx, graft survival was 40% for ReTx within 1 year of primary Tx vs 64% (p = 0.003). When ReTx was performed 1 year after cardiac Tx, survival was similar in patients bridged with a VAD and those not bridged (mean, 7.5 vs 8.7 years; p = 0.8). Survival for patients bridged to ReTx with ECMO was consistently worse (p ≤ 0.05) in all analyses. The 1-year survival of ReTx after VAD performed in 2003 to 2011 (67%) was better than in the earlier era of 1987 to 2002 (37%, p = 0.005). Bridging patients to ReTx with ECMO at any time is not advisable. Bridging patients with MCS to ReTx within 1 year of primary cardiac Tx is not advisable. Survival after ReTx for patients bridged by VAD has improved considerably over time. Patients who survive the first year after cardiac Tx can be bridged by VAD to ReTx with an expectation that outcomes can be similar to ReTx patients who did not require MCS.

Are Drowning Victims Good Donors for Lung Transplantation? Analysis of Over 18,000 Lung Transplants

Background: Drowning accounts for 8,000 deaths annually in the US. Many of these victims may be suitable donors because they are “dry drownings” and/or drowning in cold water. Therefore there are approximately 1,200 potential donors annually. However, many still question the efficacy of these potential lung donors. To evaluate the suitability of donor lungs from drowning victims, we analyzed outcomes from the United Network of Organ Sharing (UNOS) database. Methods: The UNOS database was queried for all lung transplants (LTx) performed from 10/1987 - 5/2008.

Liver transplantation for hepatocellular carcinoma: the MELD impact.

The new allocation policy of the United Network of Organ Sharing (UNOS) based on the model for end-stage liver disease (MELD) gives candidates with stage T1 or stage T2 hepatocellular carcinoma (HCC) a priority MELD score beyond their degree of hepatic decompensation. The aim of this study was to determine the impact of the new allocation policy on HCC candidates before and after the institution of MELD. The UNOS database was reviewed for all HCC candidates listed between July 1999 and July 2002. The candidates were grouped by two time periods, based on the date of implementation of new allocation policy of February 27, 2002. Pre-MELD candidates were listed for deceased donor liver transplantation (DDLT) before February 27,2002, and post-MELD candidates were listed after February 27, 2002. Candidates were compared by incidence of DDLT, time to DDLT, and dropout rate from the waiting list because of clinical deterioration or death, and survival while waiting and after DDLT. Incidence rates calculated for pre-MELD and post-MELD periods were expressed in person years. During the study, 2,074 HCC candidates were listed for DDLT in the UNOS database. The DDLT incidence rate was 0.439 transplant/person years pre-MELD and 1.454 transplant/person years post-MELD (P < 0.001). The time to DDLT was 2.28 years pre-MELD and 0.69 years post-MELD (P < 0.001). The 5-month dropout rate was 16.5% pre-MELD and 8.5% post-MELD (P < 0.001). The 5-month waiting-list survival was 90.3% pre-MELD and 95.7% post-MELD (P < 0.001). The 5-month survival after DDLT was similar for both time periods. The new allocation policy has led to an increased incidence rate of DDLT in HCC candidates. Furthermore, the 5-month dropout rate has decreased significantly. In addition, 5-month survival while waiting has increased in the post-MELD period. Thus, the new MELD-based allocation policy has benefited HCC candidates.

Post-Transplant Lymphoproliferative Disorder in the United States: Young Caucasian Males are at Highest Risk

We have previously documented Caucasian race and cadaver donor source as risk factors for post-transplant lymphoproliferative disorder (PTLD) development in recipients registered in the North American Pediatric Renal Transplant Cooperative Study (NAPRTCS). We analyzed data from the Scientific Registry of the United Network of Organ Sharing (UNOS) (from January 1988 to December 1999) to determine risk factors for the development of PTLD in all organ systems and its frequency, and we compared these factors to the risk factors in the most recent NAPRTCS database (1987-2000). In the UNOS database, PTLD was reported in 2365 of 205114 organ-transplant recipients (1.2%). PTLD was reported in 3% or more of all intestinal and thoracic organ recipients, but in less than 1% of other abdominal organ recipients. Recipient age < 18 years, Caucasian race and male gender were independent risk factors [Odds Ratios (OR) 2.81, 2.22 and 1.40, respectively, p = 0.0001], but not cadaver donor source. The combination of all three risk factors increased the OR to 8.78. The occurrence of PTLD showed a significant rise per year for heart-lung, kidney, kidney-pancreas and liver transplants, but decreased significantly for heart transplants (p < 0.001). Similar frequencies of PTLD were found in smaller organ-specific registries of heart, intestine, pediatric liver and pediatric kidney transplants. The PTLD incidence per year and incidence density have increased in recent years. Young Caucasian males are at highest risk for PTLD development among solid-organ-transplant recipients. The incidence of PTLD is increasing.

Simultaneous Heart and Kidney Transplantation in Patients with End-stage Heart and Renal Failure

Combined simultaneous organ transplantation has become more common as selection criteria for transplantation have broadened. Broadening selection criteria is secondary to improved immunosuppression and surgical techniques. The kidney is the most common extrathoracic organ to be simultaneously transplanted with the heart. A series of 13 patients suffering from both end-stage heart and renal failure underwent 14 simultaneous heart and kidney transplantations at Temple University Hospital between 1990 and 1999. This is the largest series reported from a single center. Three patients died during the initial hospitalization for an in-hospital mortality of 21%. Of 10 patients who left the hospital, 1-year survival was 100% and 2-year survival 75%. One patient required retransplant for rejection within the first year. Overall mortality at 1 and 2 years was 25 and 41%, respectively. Four out of nine (44%) patients greater than 5 years post-transplant were alive. Of the 10 patients who left the hospital, 66% were alive at 5 years. One patient succumbed to primary nonfunction of the cardiac allograft, while the four other deaths were secondary to bacterial or fungal sepsis. The patient's racial backgrounds were equally divided between African-American and white. These results are similar to those reported in a United Network of Organ Sharing Database (UNOS) registry analysis of 84 simultaneous heart and kidney transplants that found 1- and 2-year survival to be 76 and 67%, respectively. Simultaneous heart and kidney transplantation continues to be a viable option for patients suffering from failure of these two organ systems, although the results do not match those of heart transplant alone.