Fully nonparametric survival analysis in the presence of time-dependent covariates and dependent censoring.

In the presence of informative right censoring and time-dependent covariates, we estimate the survival function in a fully nonparametric fashion. We introduce a novel method for incorporating multiple observations per subject when estimating the survival function at different covariate values and compare several competing methods via simulation. The proposed method is applied to survival data from people awaiting liver transplant.

Logistical burden of offers and allocation inefficiency in circle-based liver allocation.

Recent changes to liver allocation replaced donor service areas with circles as the geographic unit of allocation. Circle-based allocation might increase the number of transplantation centers and candidates required to place a liver, thereby increasing the logistical burden of making and responding to offers on organ procurement organizations and transplantation centers. Circle-based allocation might also increase distribution time and cold ischemia time (CIT), particularly in densely populated areas of the country, thereby decreasing allocation efficiency. Using Scientific Registry of Transplant Recipient data from 2019 to 2021, we evaluated the number of transplantation centers and candidates required to place livers in the precircles and postcircles eras, nationally and by donor region. Compared with the precircles era, livers were offered to more candidates (5 vs. 9; p < 0.001) and centers (3 vs. 5; p < 0.001) before being accepted; more centers were involved in the match run by offer number 50 (9 vs. 14; p < 0.001); CIT increased by 0.2 h (5.9 h vs. 6.1 h; p < 0.001); and distribution time increased by 2.0 h (30.6 h vs. 32.6 h; p < 0.001). Increased burden varied geographically by donor region; livers recovered in Region 9 were offered to many more candidates (4 vs. 12; p < 0.001) and centers (3 vs. 8; p < 0.001) before being accepted, resulting in the largest increase in CIT (5.4 h vs. 6.0 h; p < 0.001). Circle-based allocation is associated with increased logistical burdens that are geographically heterogeneous. Continuous distribution systems will have to be carefully designed to avoid exacerbating this problem.

Life expectancy without a transplant for status 1A liver transplant candidates.

Status 1A liver transplant candidates are given the highest medical priority for the allocation of deceased donor livers. Organ Procurement and Transplantation Network (OPTN) policy requires physicians to certify that a candidate has a life expectancy without a transplant of less than 7 days for that candidate to be given status 1A. Additionally, candidates receiving status 1A must have one of six medical conditions listed in policy. Using Scientific Registry of Transplant Recipients data from all prevalent liver transplant candidates from 2010 to 2020, we used a bias-corrected Kaplan-Meier model to calculate the survival of status 1A candidates and to determine their life expectancy without a transplant. We found that status 1A candidates have a life expectancy without a transplant of 24 (95% CI 20-46) days-over three times longer than what policy requires for status 1A designation. We repeated the analysis for subgroups of status 1A candidates based on the medical conditions that grant status 1A. We found that none of these subgroups met the life expectancy requirement. Harmonizing OPTN policy with observed data would sustain the integrity of the allocation process.

Correcting the sex disparity in MELD-Na.

MELD-Na appears to disadvantage women awaiting liver transplant by underestimating their mortality rate. Fixing this problem involves: (1) estimating the magnitude of this disadvantage separately for each MELD-Na, (2) designing a correction for each MELD-Na, and (3) evaluating corrections to MELD-Na using simulated allocation. Using Kaplan-Meier modeling, we calculated 90-day without-transplant survival for men and women, separately at each MELD-Na. For most scores between 15 and 35, without-transplant survival was higher for men by 0-5 percentage points. We tested two proposed corrections to MELD-Na (MELD-Na-MDRD and MELD-GRAIL-Na), and one correction we developed (MELD-Na-Shift) to target the differences we quantified in survival across the MELD-Na spectrum. In terms of without-transplant survival, MELD-Na-MDRD overcorrected sex differences while MELD-GRAIL-Na and MELD-Na-Shift eliminated them. Estimating the impact of implementing these corrections with the liver simulated allocation model, we found that MELD-Na-Shift alone eliminated sex disparity in transplant rates (p = 0.4044) and mortality rates (p = 0.7070); transplant rates and mortality rates were overcorrected by MELD-Na-MDRD (p = 0.0025, p = 0.0006) and MELD-GRAIL-Na (p = 0.0079, p = 0.0005). We designed a corrected MELD-Na that eliminates sex disparities in without-transplant survival, but allocation changes directing smaller livers to shorter candidates may also be needed to equalize women's access to liver transplant.

Liver simulated allocation model does not effectively predict organ offer decisions for pediatric liver transplant candidates.

The SRTR maintains the liver-simulated allocation model (LSAM), a tool for estimating the impact of changes to liver allocation policy. Integral to LSAM is a model that predicts the decision to accept or decline a liver for transplant. LSAM implicitly assumes these decisions are made identically for adult and pediatric liver transplant (LT) candidates, which has not been previously validated. We applied LSAM's decision-making models to SRTR offer data from 2013 to 2016 to determine its efficacy for adult (≥18) and pediatric (<18) LT candidates, and pediatric subpopulations-teenagers (≥12 to <18), children (≥2 to <12), and infants (<2)-using the area under the receiver operating characteristic (ROC) curve (AUC). For nonstatus 1A candidates, all pediatric subgroups had higher rates of offer acceptance than adults. For non-1A candidates, LSAM's model performed substantially worse for pediatric candidates than adults (AUC 0.815 vs. 0.922); model performance decreased with age (AUC 0.898, 0.806, 0.783 for teenagers, children, and infants, respectively). For status 1A candidates, LSAM also performed worse for pediatric than adult candidates (AUC 0.711 vs. 0.779), especially for infants (AUC 0.618). To ensure pediatric candidates are not unpredictably or negatively impacted by allocation policy changes, we must explicitly account for pediatric-specific decision making in LSAM.

The Precise Relationship Between Model for End-Stage Liver Disease and Survival Without a Liver Transplant.

BACKGROUND AND AIMS: Scores from the Model for End-Stage Liver Disease (MELD), which are used to prioritize candidates for deceased donor livers, are widely acknowledged to be negatively correlated with the 90-day survival rate without a liver transplant. However, inconsistent and outdated estimates of survival probabilities by MELD preclude useful applications of the MELD score. APPROACH AND RESULTS: Using data from all prevalent liver waitlist candidates from 2016 to 2019, we estimated 3-day, 7-day, 14-day, 30-day, and 90-day without-transplant survival probabilities (with confidence intervals) for each MELD score and status 1A. We used an adjusted Kaplan-Meier model to avoid unrealistic assumptions and multiple observations per person instead of just the observation at listing. We found that 90-day without-transplant survival has improved over the last decade, with survival rates increasing >10% (in absolute terms) for some MELD scores. We demonstrated that MELD correctly prioritizes candidates in terms of without-transplant survival probability but that status 1A candidates' short-term without-transplant survival is higher than that of MELD 40 candidates and lower than that of MELD 39 candidates. Our primary result is the updated survival functions themselves. CONCLUSIONS: We calculated without-transplant survival probabilities for each MELD score (and status 1A). The survival function is an invaluable tool for many applications in liver transplantation: awarding of exception points, calculating the relative demand for deceased donor livers in different geographic areas, calibrating the pediatric end-stage liver disease score, and deciding whether to accept an offered liver.

Impact of Acuity Circles on Outcomes for Pediatric Liver Transplant Candidates.

BACKGROUND: In December 2018, United Network for Organ Sharing approved an allocation scheme based on recipients' geographic distance from a deceased donor (acuity circles [ACs]). Previous analyses suggested that ACs would reduce waitlist mortality overall, but their impact on pediatric subgroups was not considered. METHODS: We applied Scientific Registry of Transplant Recipients data from 2011 to 2016 toward the Liver Simulated Allocation Model to compare outcomes by age and illness severity for the United Network for Organ Sharing-approved AC and the existing donor service area-/region-based allocation schemes. Means from each allocation scheme were compared using matched-pairs t tests. RESULTS: During a 3-year period, AC allocation is projected to decrease waitlist deaths in infants (39 versus 55; P < 0.001), children (32 versus 50; P < 0.001), and teenagers (15 versus 25; P < 0.001). AC allocation would increase the number of transplants in infants (707 versus 560; P < 0.001), children (677 versus 547; P < 0.001), and teenagers (404 versus 248; P < 0.001). AC allocation led to decreased median pediatric end-stage liver disease/model for end-stage liver disease at transplant for infants (29 versus 30; P = 0.01), children (26 versus 29; P < 0.001), and teenagers (26 versus 31; P < 0.001). Additionally, AC allocation would lead to fewer transplants in status 1B in children (97 versus 103; P = 0.006) but not infants or teenagers. With AC allocation, 77% of pediatric donor organs would be allocated to pediatric candidates, compared to only 46% in donor service area-/region-based allocation (P < 0.001). CONCLUSIONS: AC allocation will likely address disparities for pediatric liver transplant candidates and recipients by increasing transplants and decreasing waitlist mortality. It is more consistent with federally mandated requirements for organ allocation.

The Impact of Increased Allocation Priority for Children Awaiting Liver Transplant: A Liver Simulated Allocation Model (LSAM) Analysis.

OBJECTIVE: The aim of the study was to investigate the impact of prioritizing infants, children, adolescents, and the sickest adults (Status 1) for deceased donor livers. We compared outcomes under two "SharePeds" allocation schema, which prioritize children and Status 1 adults for national sharing and enhanced access to pediatric donors or all donors younger than 35 years, to outcomes under the allocation plan approved by the Organ Procurement and Transplant Network in December 2017 (Organ Procurement and Transplantation Network [OPTN] 12-2017). METHODS: The 2017 Liver Simulated Allocation Model and Scientific Registry of Transplant Recipients data on all US liver transplant candidates and liver offers 7/2013 to 6/2016 were used to predict waitlist deaths, transplants, and post-transplant deaths under the OPTN 12-2017 and SharePeds schema. RESULTS: Prioritizing national sharing of pediatric donor livers with children (SharePeds 1) would decrease waitlist deaths for infants (<2 years, P = 0.0003) and children (2-11 years, P = 0.001), with no significant change for adults (P = 0.13). Prioritizing national sharing of all younger than 35-year-old deceased donor livers with children and Status 1A adults (SharePeds 2) would decrease waitlist deaths for infants, children, and all Status 1A/B patients (P < 0.0001 for each). SharePeds 1 and 2 would increase the number of liver transplants done in infants, children, and adolescents compared to the OPTN-2017 schema (P < 0.00005 for all age groups). Both SharePeds schema would increase the percentage of pediatric livers transplanted into pediatric recipients. CONCLUSIONS: Waitlist deaths could be significantly decreased, and liver transplants increased, for children and the sickest adults, by prioritizing children for pediatric livers and with broader national sharing of deceased donor livers.

Bayesian Assessment of Genetic Risk in Families with a Balanced Translocation.

An important problem from the field of genetics involves the calculation of a personalized risk estimate on behalf of a heterozygous carrier of a balanced translocation. Though phenotypically normal, the carrier may be at increased risk of having a child who is mentally and physically abnormal due to an unbalanced translocation of chromosomal segments. An accurate estimate of the probability of this event is understandably desirable. Unfortunately, translocations are almost always family-specific so there is very little data that are perfectly relevant and one has to rely heavily on general risk figures derived from studies of families with similar translocations. This makes the problem particularly well suited to Bayesian analysis, which coherently combines family-specific data and a priori knowledge. However, much of the genetics counseling literature recommends an either/or approach: if the family is large enough, use family data; else, use pooled population data. In this article, we describe how uncertainty can be significantly reduced by incorporating all available information in the context of deriving a risk estimate for a hypothetical familial translocation.

GC/MS method for positive detection of Bacillus anthracis endospores.

A simple method was developed for detection of Bacillus anthracis (BA) endospores and for differentiation of them from other species in the Bacillus cereus group. Chemical profiles that include lipids (i.e., fatty acids), carbohydrates (i.e., sugars), and the spore-specific biomarker, dipicolinic acid, were generated by one-step thermochemolysis (TCM) at 140 °C in 5 min to provide specific biomarker signatures. Anthrose, which is a biomarker characteristic of the B. cereus group of bacteria, was determined from a fragment produced by TCM. Surprisingly, several virulent BA strains contained very low levels of anthrose, which confounded their detection. A statistical discrimination algorithm was constructed using a combination of biomarkers, which was robust against different growth conditions (medium and temperature). Fifteen endospore-forming Bacillus species were confirmed in a statistically designed test (~90%) using the algorithm, including six BA strains (four virulent isolates), five B. thuringiensis (BT) isolates, and one isolate each for B. cereus (BC), B. mycoides (BM), B. atrophaeus (BG), and B. subtilis (BS). The detection limit for B. anthracis was found to be 50,000 endospores, on the basis of the GC/MS detection limits for 3-methyl-2-butenoic acid methyl ester, which is the biomarker derived from TCM of anthrose.