Pretransplant survival of patients with end-stage heart failure under competing risks.

Heart transplantation is the gold standard of care for end-stage heart failure in the United States. Donor hearts are a scarce resource, however the current allocation policy-proposed in 2016 and implemented in 2018-has not addressed certain disparities. Between 2005 and 2016, the number of active candidates increased 127%, whereas transplant rates decreased 27.8%. Pretransplant mortality rates declined steadily for that period from 14.6 to 9.7, especially for candidates with mechanical circulatory assistive devices (MCSDs). This study reports survival analyses of candidates for heart transplantation list under competing events of transplantation and MCSD implantation. We queried the transplant data for a cohort of adult patients (age ≥ 16) without MCSDs prior to listing for transplantation between 2005 and 2014 (n = 23,373). We used cause-specific and subdistribution hazards models as multivariate regressions for all competing events. Patients listed as low priority for transplantation are less likely to require implantation but less likely to survive after 1,000 days of listing than patients listed at higher priorities. The current policy does not address this disparity as it focuses on stratifying patients with different types of MCSD. Clinical characteristics must be considered in prioritization.

Genetic capitalism and stabilizing selection of antimicrobial resistance genotypes in Escherichia coli.

Antimicrobial resistance (AMR) in pathogenic strains of bacteria, such as Escherichia coli (E. coli), adversely impacts personal and public health. In this study, we examine competing hypotheses for the evolution of AMR including (i) 'genetic capitalism' in which genotypes that confer antibiotic resistance are gained and not often lost in lineages, and (ii) 'stabilizing selection' in which genotypes that confer antibiotic resistance are gained and lost often. To test these hypotheses, we assembled a dataset that includes annotations for 409 AMR genotypes and a phylogenetic tree based on genome-wide single nucleotide polymorphisms from 29 255 isolates of E. coli collected over the past 134 years. We used phylogenetic methods to count the times each AMR genotype was gained and lost across the tree and used model-based clustering of the genotypes with respect to their gain and loss rates. We demonstrate that many genotypes cluster to support the hypothesis for genetic capitalism while a few genotypes cluster to support the hypothesis for stabilizing selection. Comparing the sets of genotypes that fall under each of the hypotheses, we found a statistically significant difference in the breakdown of resistance mechanisms through which the AMR genotypes function. The result that many AMR genotypes cluster under genetic capitalism reflects that strong positive selective forces, primarily induced by human industrialization of antibiotics, outweigh the potential fitness costs to the bacterial lineages for carrying the AMR genotypes. We expect genetic capitalism to further drive bacterial lineages to resist antibiotics. We find that antibiotics that function via replacement and efflux tend to behave under stabilizing selection and thus may be valuable in an antibiotic cycling strategy.

Metapopulation Model from Pathogen's Perspective: A Versatile Framework to Quantify Pathogen Transfer and Circulation between Environment and Hosts.

Metapopulation models have been primarily explored in infectious disease epidemiology to study host subpopulation movements and between-host contact structures. They also have the potential to investigate environmental pathogen transferring. In this study, we demonstrate that metapopulation models serve as an ideal modeling framework to characterize and quantify pathogen transfer between environment and hosts. It therefore unifies host, pathogen, and environment, collectively known as the epidemiological triad, a fundamental concept in epidemiology. We develop a customizable and generalized pathogen-transferring model where pathogens dwell in and transferring (via contact) between environment and hosts. We analyze three specific case studies: pure pathogen transferring without pathogen demography, source-sink dynamics, and pathogen control via external disinfection. We demonstrate how pathogens circulate in the system between environment and hosts, as well as evaluate different controlling efforts for healthcare-associated infections (HAIs). For pure pathogen transferring, system equilibria can be derived analytically to explicitly quantify long-term pathogen distribution in the system. For source-sink dynamics and pathogen control via disinfection, we demonstrate that complete eradication of pathogens can be achieved, but the rates of converging to system equilibria differ based on specific model parameterization. Direct host-host pathogen transferring and within-host dynamics can be future directions of this modeling framework by adding specific modules.

Accuracy of the Pediatric End-stage Liver Disease Score in Estimating Pretransplant Mortality Among Pediatric Liver Transplant Candidates.

Importance: Fair allocation of livers between pediatric and adult recipients is critically dependent on the accuracy of mortality estimates afforded by the Pediatric End-stage Liver Disease (PELD) and Model for End-stage Liver Disease, respectively. Widespread reliance on exceptions for pediatric recipients suggests that the 2 systems may not be comparable. Objective: To evaluate the accuracy of the PELD score in estimating 90-day pretransplant mortality among pediatric patients on the United Network for Organ Sharing (UNOS) waiting list. Design, Setting, and Participants: Patients who were listed from February 27, 2002, to March 31, 2014, for primary liver transplant were included in this retrospective analysis and were followed up for at least 2 years through June 17, 2016. The study analyzed 2 cohorts using the UNOS Standard Transplant Analysis and Research data files. The full cohort comprised 4298 patients (<18 years of age) who had chronic liver disease (excluding cancer). The reduced cohort (n = 2421) excluded patients receiving living donor transplantation or PELD exception points. Main Outcomes and Measures: Observed and expected 90-day pretransplant mortality rates evaluated at 10-point interval PELD levels. Results: Among the 4298 patients in the full cohort (mean [SD] age, 2.5 [4.2] years; 2251 [52.4%] female; 2201 [51.2%] white), PELD scores and mortality were concordant (C statistic, 0.8387 [95% CI, 0.8191-0.8584] for the full cohort and 0.8123 [95% CI, 0.7919-0.8327] for the reduced cohort). However, the estimated 90-day mortality using the PELD score underestimated the actual probability of death by as much as 17%. Conclusions and Relevance: With use of the PELD score, the ranking of risk among children was preserved, but direct comparisons between adult and pediatric candidates were not accurate. Children with chronic liver disease who are in need of transplant may be at a disadvantage compared with adults in a similar situation.

Using time-varying models to estimate post-transplant survival in pediatric liver transplant recipients.

PURPOSE: To distinguish clinical factors that have time-varying (as opposed to constant) impact upon patient and graft survival among pediatric liver transplant recipients. METHODS: Using national data from 2002 through 2013, we examined potential clinical and demographic covariates using Gray's piecewise constant time-varying coefficients (TVC) models. For both patient and graft survival, we estimated univariable and multivariable Gray's TVC, retaining significant covariates based on backward selection. We then estimated the same specification using traditional Cox proportional hazards (PH) models and compared our findings. RESULTS: For patient survival, covariates included recipient diagnosis, age, race/ethnicity, ventilator support, encephalopathy, creatinine levels, use of living donor, and donor age. Only the effects of recipient diagnosis and donor age were constant; effects of other covariates varied over time. We retained identical covariates in the graft survival model but found several differences in their impact. CONCLUSION: The flexibility afforded by Gray's TVC estimation methods identify several covariates that do not satisfy constant proportionality assumptions of the Cox PH model. Incorporating better survival estimates is critical for improving risk prediction tools used by the transplant community to inform organ allocation decisions.

Gray's time-varying coefficients model for posttransplant survival of pediatric liver transplant recipients with a diagnosis of cancer.

Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH) model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray's piecewise constant time-varying coefficients (PC-TVC) model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable (P < 0.15) and multivariable models with forward selection (P < 0.05) for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.