Consequences of low estimated glomerular filtration rate either before or early after kidney donation.

In the general population, decreases in glomerular filtration rate (GFR) are associated with subsequent development of chronic kidney disease (CKD), cardiovascular disease (CVD), and death. It is unknown if low estimated GFR (eGFR) before or early after kidney donation was also associated with these risks. One thousand six hundred ninety-nine living donors who had both predonation and early (4-10 weeks) postdonation eGFR were included. We studied the relationships between eGFR, age at donation, and the time to sustained eGFR<45 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), hypertension, diabetes mellitus (DM), CVD, and death. Median follow-up was 12 (interquartile range, 6-21) years. Twenty-year event rates were 5.8% eGFR<45 mL/min/1.73m2; 1.2% eGFR<30 mL/min/1.73m2; 29.0% hypertension; 7.8% DM; 8.0% CVD; and 5.2% death. The median time to eGFR<45 mL/min/1.73m2 (N = 79) was 17 years, and eGFR<30 mL/min/1.73m2 (N = 22) was 25 years. Both low predonation and early postdonation eGFR were associated with eGFR<45 mL/min/1.73m2 (P < .0001) and eGFR<30 mL/min/1.73m2 (P < .006); however, the primary driver of risk for all ages was low postdonation (rather than predonation) eGFR. Predonation and postdonation eGFR were not associated with hypertension, DM, CVD, or death. Low predonation and early postdonation eGFR are risk factors for developing eGFR<45 mL/min/1.73m2 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), but not CVD, hypertension, DM, or death.

Feature selection for support vector regression using a genetic algorithm.

Support vector regression (SVR) is particularly beneficial when the outcome and predictors are nonlinearly related. However, when many covariates are available, the method's flexibility can lead to overfitting and an overall loss in predictive accuracy. To overcome this drawback, we develop a feature selection method for SVR based on a genetic algorithm that iteratively searches across potential subsets of covariates to find those that yield the best performance according to a user-defined fitness function. We evaluate the performance of our feature selection method for SVR, comparing it to alternate methods including LASSO and random forest, in a simulation study. We find that our method yields higher predictive accuracy than SVR without feature selection. Our method outperforms LASSO when the relationship between covariates and outcome is nonlinear. Random forest performs equivalently to our method in some scenarios, but more poorly when covariates are correlated. We apply our method to predict donor kidney function 1 year after transplant using data from the United Network for Organ Sharing national registry.

The landscape of liver transplantation for patients with alcohol-associated liver disease in the United States.

Indications for liver transplants have expanded to include patients with alcohol-associated liver disease (ALD) over the last decade. Concurrently, the liver allocation policy was updated in February 2020 replacing the Donor Service Area with Acuity Circles (ACs). The aim is to compare the transplantation rate, waitlist outcomes, and posttransplant survival of candidates with ALD to non-ALD and assess differences in that effect after the implementation of the AC policy. Scientific Registry for Transplant Recipients data for adult candidates for liver transplant were reviewed from the post-AC era (February 4, 2020-March 1, 2022) and compared with an equivalent length of time before ACs were implemented. The adjusted transplant rates were significantly higher for those with ALD before AC, and this difference increased after AC implementation (transplant rate ratio comparing ALD to non-ALD = 1.20, 1.13, 1.61, and 1.32 for the Model for End-Stage Liver Disease categories 37-40, 33-36, 29-32, and 25-28, respectively, in the post-AC era, p < 0.05 for all). The adjusted likelihood of death/removal from the waitlist was lower for patients with ALD across all lower Model for End-Stage Liver Disease categories (adjusted subdistribution hazard ratio = 0.70, 0.81, 0.84, and 0.70 for the Model for End-Stage Liver Disease categories 25-28, 20-24, 15-19, 6-14, respectively, p < 0.05). Adjusted posttransplant survival was better for those with ALD (adjusted hazard ratio = 0.81, p < 0.05). Waiting list and posttransplant mortality tended to improve more for those with ALD since the implementation of AC but not significantly. ALD is a growing indication for liver transplantation. Although patients with ALD continue to have excellent posttransplant outcomes and lower waitlist mortality, candidates with ALD have higher adjusted transplant rates, and these differences have increased after AC implementation.

Should children receive a kidney transplant before 2 years of age?

BACKGROUND: The optimal age of kidney transplantation for infants and toddlers with kidney failure is unclear. We aimed to evaluate the patient survival associated with kidney transplantation before 2 years of age versus remaining on the waitlist until ≥2 years. METHOD: We used the Scientific Registry of Transplant Recipients to identify all children added to the deceased-donor waitlist before 2 years of age between 1/1/2000 and 4/30/2020. For each case aged <2 years at transplant, we created a control group comprising all candidates on the waitlist on the case's transplant date. Patient survival was evaluated using sequential Cox regression. Dialysis-free time was defined as graft survival time for cases and the sum of dialysis-free time on the waitlist and graft survival time for controls. RESULTS: We observed similar patient survival for posttransplant periods 0-3 and 4-12 months but higher survival for period >12 months for <2-year decreased-donor recipients (aHR: 0.32; 95% CI: 0.13-0.78; p = .01) compared with controls. Similarly, patient survival was higher for <2-year living-donor recipients for posttransplant period >12 months (aHR: 0.21; 95% CI: 0.06-0.73; p = .01). The 5-year dialysis-free survival was higher for <2-year deceased- (difference: 0.59 years; 95% CI: 0.23-0.93) and living-donor (difference: 1.84 years; 95% CI: 1.31-2.25) recipients. CONCLUSION: Kidney transplantation in children <2 years of age is associated with improved patient survival and reduced dialysis exposure compared with remaining on the waitlist until ≥2 years.

School Connectedness and Adolescent E-cigarette Susceptibility in an Urban Sample of Middle and High School Students.

Adolescent school connectedness generally protects from risk behaviors such as tobacco use; however, its relationship to e-cigarette use is unclear. This study examines the relationship between adolescent school connectedness and e-cigarette susceptibility in a diverse longitudinal sample. This secondary analysis of a school-based intervention surveyed 608 middle (66%) and high school (34%) students from 10 schools at 3 time points over 1 year. At baseline, respondents had a mean age of 14 years, 54% were female, and 71% were BIPOC (Black, Indigenous, People of Color). Logistic regression models examined unadjusted and adjusted associations between school connectedness (both baseline and concurrent) and e-cigarette susceptibility over time. E-cigarettes represented the most prevalent form of current nicotine-containing product use in spring 2019 (2.3%), and most respondents reported no e-cigarette susceptibility (69%). E-cigarette susceptibility remained relatively stable during the study. Higher baseline school connectedness levels were associated with lower odds of e-cigarette susceptibility over time. Similarly, higher concurrent school connectedness scores were associated with lower odds of e-cigarette susceptibility over time: spring 2019 (OR, 0.39; 95% CI, 0.32, 0.47), fall 2019 (OR, 0.49; 95% CI, 0.34, 0.72), and spring 2020 (OR, 0.64; 95% CI, 0.47, 0.87). Findings were similar for middle and high school students and did not differ significantly after adjusting for other covariates. Adolescents' school connectedness appears to protect from e-cigarette susceptibility over time, underscoring the importance of promoting positive school experiences to reduce adolescent risk e-cigarette use.

Transportability of causal inference under random dynamic treatment regimes for kidney-pancreas transplantation.

A difficult decision for patients in need of kidney-pancreas transplant is whether to seek a living kidney donor or wait to receive both organs from one deceased donor. The framework of dynamic treatment regimes (DTRs) can inform this choice, but a patient-relevant strategy such as "wait for deceased-donor transplant" is ill-defined because there are multiple versions of treatment (i.e., wait times, organ qualities). Existing DTR methods average over the distribution of treatment versions in the data, estimating survival under a "representative intervention." This is undesirable if transporting inferences to a target population such as patients today, who experience shorter wait times thanks to evolutions in allocation policy. We, therefore, propose the concept of a generalized representative intervention (GRI): a random DTR that assigns treatment version by drawing from the distribution among strategy compliers in the target population (e.g., patients today). We describe an inverse-probability-weighted product-limit estimator of survival under a GRI that performs well in simulations and can be implemented in standard statistical software. For continuous treatments (e.g., organ quality), weights are reformulated to depend on probabilities only, not densities. We apply our method to a national database of kidney-pancreas transplant candidates from 2001-2020 to illustrate that variability in transplant rate across years and centers results in qualitative differences in the optimal strategy for patient survival.

Practical guidance on modeling choices for the virtual twins method.

Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method is a highly cited and implemented method for subgroup identification because of its intuitive framework. However, since its initial publication, many researchers still rely heavily on the authors' initial modeling suggestions without examining newer and more powerful alternatives. This leaves much of the potential of the method untapped. We comprehensively evaluate the performance of VT with different combinations of methods in each of its component steps, under a collection of linear and nonlinear problem settings. Our simulations show that the method choice for Step 1 of VT, in which dense models with high predictive performance are fit for the potential outcomes, is highly influential in the overall accuracy of the method, and Superlearner is a promising choice. We illustrate our findings by using VT to identify subgroups with heterogeneous treatment effects in a randomized, double-blind trial of very low nicotine content cigarettes.

The Minnesota attributable risk of kidney donation (MARKD) study: a retrospective cohort study of long-term (> 50 year) outcomes after kidney donation compared to well-matched healthy controls.

BACKGROUND: There is uncertainty about the long-term risks of living kidney donation. Well-designed studies with controls well-matched on risk factors for kidney disease are needed to understand the attributable risks of kidney donation. METHODS: The goal of the Minnesota Attributable Risk of Kidney Donation (MARKD) study is to compare the long-term (> 50 years) outcomes of living donors (LDs) to contemporary and geographically similar controls that are well-matched on health status. University of Minnesota (n = 4022; 1st transplant: 1963) and Mayo Clinic LDs (n = 3035; 1st transplant: 1963) will be matched to Rochester Epidemiology Project (REP) controls (approximately 4 controls to 1 donor) on the basis of age, sex, and race/ethnicity. The REP controls are a well-defined population, with detailed medical record data linked between all providers in Olmsted and surrounding counties, that come from the same geographic region and era (early 1960s to present) as the donors. Controls will be carefully selected to have health status acceptable for donation on the index date (date their matched donor donated). Further refinement of the control group will include confirmed kidney health (e.g., normal serum creatinine and/or no proteinuria) and matching (on index date) of body mass index, smoking history, family history of chronic kidney disease, and blood pressure. Outcomes will be ascertained from national registries (National Death Index and United States Renal Data System) and a new survey administered to both donors and controls; the data will be supplemented by prior surveys and medical record review of donors and REP controls. The outcomes to be compared are all-cause mortality, end-stage kidney disease, cardiovascular disease and mortality, estimated glomerular filtration rate (eGFR) trajectory and chronic kidney disease, pregnancy risks, and development of diseases that frequently lead to chronic kidney disease (e.g. hypertension, diabetes, and obesity). We will additionally evaluate whether the risk of donation differs based on baseline characteristics. DISCUSSION: Our study will provide a comprehensive assessment of long-term living donor risk to inform candidate living donors, and to inform the follow-up and care of current living donors.

Efficacy and Safety of Ensovibep for Adults Hospitalized With COVID-19 : A Randomized Controlled Trial.

BACKGROUND: Ensovibep (MP0420) is a designed ankyrin repeat protein, a novel class of engineered proteins, under investigation as a treatment of SARS-CoV-2 infection. OBJECTIVE: To investigate if ensovibep, in addition to remdesivir and other standard care, improves clinical outcomes among patients hospitalized with COVID-19 compared with standard care alone. DESIGN: Double-blind, randomized, placebo-controlled, clinical trial. (ClinicalTrials.gov: NCT04501978). SETTING: Multinational, multicenter trial. PARTICIPANTS: Adults hospitalized with COVID-19. INTERVENTION: Intravenous ensovibep, 600 mg, or placebo. MEASUREMENTS: Ensovibep was assessed for early futility on the basis of pulmonary ordinal scores at day 5. The primary outcome was time to sustained recovery through day 90, defined as 14 consecutive days at home or place of usual residence after hospital discharge. A composite safety outcome that included death, serious adverse events, end-organ disease, and serious infections was assessed through day 90. RESULTS: An independent data and safety monitoring board recommended that enrollment be halted for early futility after 485 patients were randomly assigned and received an infusion of ensovibep (n = 247) or placebo (n = 238). The odds ratio (OR) for a more favorable pulmonary outcome in the ensovibep (vs. placebo) group at day 5 was 0.93 (95% CI, 0.67 to 1.30; P = 0.68; OR > 1 would favor ensovibep). The 90-day cumulative incidence of sustained recovery was 82% for ensovibep and 80% for placebo (subhazard ratio [sHR], 1.06 [CI, 0.88 to 1.28]; sHR > 1 would favor ensovibep). The primary composite safety outcome at day 90 occurred in 78 ensovibep participants (32%) and 70 placebo participants (29%) (HR, 1.07 [CI, 0.77 to 1.47]; HR < 1 would favor ensovibep). LIMITATION: The trial was prematurely stopped because of futility, limiting power for the primary outcome. CONCLUSION: Compared with placebo, ensovibep did not improve clinical outcomes for hospitalized participants with COVID-19 receiving standard care, including remdesivir; no safety concerns were identified. PRIMARY FUNDING SOURCE: National Institutes of Health.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials.

BACKGROUND: A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods. OBJECTIVE: This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529). METHODS: Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction. RESULTS: The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold). CONCLUSIONS: Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.

Dynamic borrowing in the presence of treatment effect heterogeneity.

A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered.

A group-sequential randomized trial design utilizing supplemental trial data.

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

A permutation procedure to detect heterogeneous treatment effects in randomized clinical trials while controlling the type I error rate.

BACKGROUND/AIMS: Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population. METHODS: We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes. RESULTS: In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects. CONCLUSIONS: The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups.

Deeply Tailored Adaptive Interventions to Reduce College Student Drinking: a Real-World Application of Q-Learning for SMART Studies.

M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking.

Nonparametric cluster significance testing with reference to a unimodal null distribution.

Cluster analysis is an unsupervised learning strategy that is exceptionally useful for identifying homogeneous subgroups of observations in data sets of unknown structure. However, it is challenging to determine if the identified clusters represent truly distinct subgroups rather than noise. Existing approaches for addressing this problem tend to define clusters based on distributional assumptions, ignore the inherent correlation structure in the data, or are not suited for high-dimension low-sample size (HDLSS) settings. In this paper, we propose a novel method to evaluate the significance of identified clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution that preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation, and thus, does not require that the data follow a particular distribution. By utilizing sparse covariance estimation, the method is adapted for the HDLSS setting. The approach can be used to test the null hypothesis that the data cannot be partitioned into clusters and to determine the optimal number of clusters. Simulation examples, theoretical evaluations, and applications to temporomandibular disorder research and cancer microarray data illustrate the utility of the proposed method.

Borrowing from supplemental sources to estimate causal effects from a primary data source.

The increasing multiplicity of data sources offers exciting possibilities in estimating the effects of a treatment, intervention, or exposure, particularly if observational and experimental sources could be used simultaneously. Borrowing between sources can potentially result in more efficient estimators, but it must be done in a principled manner to mitigate increased bias and Type I error. Furthermore, when the effect of treatment is confounded, as in observational sources or in clinical trials with noncompliance, causal effect estimators are needed to simultaneously adjust for confounding and to estimate effects across data sources. We consider the problem of estimating causal effects from a primary source and borrowing from any number of supplemental sources. We propose using regression-based estimators that borrow based on assuming exchangeability of the regression coefficients and parameters between data sources. Borrowing is accomplished with multisource exchangeability models and Bayesian model averaging. We show via simulation that a Bayesian linear model and Bayesian additive regression trees both have desirable properties and borrow under appropriate circumstances. We apply the estimators to recently completed trials of very low nicotine content cigarettes investigating their impact on smoking behavior.

Detecting participant noncompliance across multiple time points by modeling a longitudinal biomarker.

INTRODUCTION: Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of randomized clinical trials. No gold standard has been identified for detecting noncompliance, but in some trials participants' biomarkers can provide objective information that suggests exposure to non-study treatments. However, existing methods are limited to retrospectively detecting noncompliance at a single time point based on a single biomarker measurement. We propose a novel method that can leverage participants' full biomarker history to detect noncompliance across multiple time points. Conditional on longitudinal biomarker data, our method can estimate the probability of compliance at (1) a single time point of the trial, (2) all time points, and (3) a future time point. METHODS: Across time points, we model the biomarker as a mixture density with (latent) components corresponding to longitudinal patterns of compliance. To estimate the mixture density, we fit mixed effects models for both compliance and the biomarker. We use the mixture density to derive compliance probabilities that condition on the longitudinal biomarker data. We evaluate our compliance probabilities by simulation and apply them to a trial in which current smokers were asked to only smoke low nicotine study cigarettes (Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2). In the simulation, we investigated three different effects of compliance on the biomarker, as well as the effect of misspecification of the covariance structures. We compared probability estimators (1) and (2) to those that ignore the longitudinal correlation in the data according to area under the receiver operating characteristic curve. We evaluated estimator (3) by plotting its calibration lines. For Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, we compared estimators (1) and (3) to a probability estimator of compliance at the last time point that ignores the longitudinal correlation. RESULTS: In the simulation, for both compliance at the last time point and at all time points, conditioning on the longitudinal biomarker data uniformly raised area under the receiver operating characteristic curve across all three compliance effect scenarios. The gains in area under the receiver operating characteristic curve were smaller under misspecification. The calibration lines for the prediction of compliance closely followed 45°, though with additional variability under misspecification. For compliance at the last time point of Center for the Evaluation of Nicotine in Cigarettes Project 1 Study 2, conditioning on participants' full biomarker history boosted area under the receiver operating characteristic curve by three percentage points. The prediction probabilities somewhat accurately approximated the non-longitudinal compliance probabilities. DISCUSSION: Compared to existing methods that only use a single biomarker measurement, our method can account for the longitudinal correlation in the biomarker and compliance to more accurately identify noncompliant participants. Our method can also use participants' biomarker history to predict compliance at a future time point.

Predictors of Survival After Liver Transplantation in Patients With the Highest Acuity (MELD ≥40).

OBJECTIVE: To identify factors that accurately predict 1-year survival for liver transplant recipients with a MELD score ≥40. BACKGROUND: Although transplant is beneficial for patients with the highest acuity (MELD ≥40), mortality in this group is high. Predicting which patients are likely to survive for >1 year would be medically and economically helpful. METHODS: The Scientific Registry of Transplant Recipients database was reviewed to identify adult liver transplant recipients from 2002 through 2016 with MELD score ≥40 at transplant. The relationships between 44 recipient and donor factors and 1-year patient survival were examined using random survival forests methods. Variable importance measures were used to identify the factors with the strongest influence on survival, and partial dependence plots were used to determine the dependence of survival on the target variable while adjusting for all other variables. RESULTS: We identified 5309 liver transplants that met our criteria. The overall 1-year survival of high-acuity patients improved from 69% in 2001 to 87% in 2016. The strongest predictors of death within 1 year of transplant were patient on mechanical ventilator before transplantation, prior liver transplant, older recipient age, older donor age, donation after cardiac death, and longer cold ischemia. CONCLUSIONS: Liver transplant outcomes continue to improve even for patients with high medical acuity. Applying ensemble learning methods to recipient and donor factors available before transplant can predict survival probabilities for future transplant cases. This information can be used to facilitate donor/recipient matching and to improve informed consent.

Rapid discontinuation of prednisone in kidney transplant recipients from at-risk subgroups: an OPTN/SRTR analysis.

Although rapid discontinuation of prednisone (RDP) after kidney transplantation has been successful in low-risk recipients, there is concern about RDP use in recipients at increased risk for rejection or recurrent disease. Using SRTR, we compared outcomes for RDP versus maintenance prednisone-treated recipients for all adult 1st and 2nd transplants (n = 169 479) and the following 1st transplant subgroups: African American (AA); highly sensitized; those with a potentially recurrent disease; and pediatric recipients. For all adult 1st LD and DD transplants, RDP was associated with better patient and graft survival. For all LD subgroups, RDP and maintenance prednisone were associated with similar patient, graft, and death-censored (DC) graft survival. For 1st transplant DD subgroups, RDP was associated with better patient survival in AA, those with potentially recurrent disease, and pediatric recipients; graft survival with RDP was better in AAs. For adult 2nd DD transplants, RDP was associated with worse DC-graft survival. Importantly, for all differences, the effect size was small. With the exception of 2nd DD transplants, RDP protocols can be used without decreasing patient or graft survival for subgroups of 1st DD and LD kidney transplant recipients and for 2nd LD transplant recipients, at increased risk of rejection or recurrent disease.

Pediatric retransplantation of the liver: A prognostic scoring tool.

Few prognostic models have been created in children that receive liver retransplantation (rLT). We examined the SRTR database of 731 children that underwent second liver transplant between 2002 and 2018. Proportional hazards models using backward variable selection were used to identify recipient, donor, and surgical characteristics associated with survival. A simple prognostic scoring system or nomogram (ie, each risk factor was weighted on a five-point scale) was constructed based on the fitted model. Recipient age (P < .001), MELD/PELD (P < .001), recipient ventilated (P = .003), donor cause of death (P = .024), graft type (P = .045), first graft loss due to biliary tract complications (P = .048), and survival time of the first graft (P = .006) were significant predictors of retransplant survival. The bias-corrected Harrell's C-index for the multivariable model was 0.63. Survival was significantly different (P < .001) for those at low risk (0-4 points), medium risk (5-7 points), and high risk (8+ points). Survival was equivalent between low risk pediatric second transplant recipients and pediatric primary liver transplant recipients (P = .67) but significantly worse for medium- (P < .001) and high-risk (P < .001) recipients. With simple clinical characteristics, this scoring tool can modestly discriminate between those children at high risk and those children at low risk of poor outcomes after second liver transplant.