OPTN/SRTR 2022 Annual Data Report: Liver.

In 2022, liver transplant activity continued to increase in the United States, with an all-time high of 9,527 transplants performed, representing a 52% increase over the past decade (2012-2022). Of these transplants, 8,924 (93.7%) were from deceased donors and 603 (6.3%) were from living donors. Liver transplant recipients were 94.5% adult and 5.5% pediatric. The overall size of the liver transplant waiting list contracted, with more patients being removed than added, although 10,548 adult patients still remained on the waiting list at the end of 2022. Alcohol-associated liver disease continued to be the leading diagnosis among both candidates and recipients, followed by metabolic dysfunction-associated steatohepatitis. Simultaneous liver-kidney transplant was the most common multiorgan combination, with 800 liver-kidney transplants performed in 2022; in addition, there were 303 new listings for kidney transplant via the safety net mechanism. Among adults added to the liver waiting list in 2021, 39.9% received a deceased donor liver transplant within 3 months; 45.7%, within 6 months; and 54.5%, within 1 year. Pretransplant mortality decreased to 12.3 deaths per 100 patient-years in 2022, although still 15.6% of removals from the waiting list were for death or being too sick for transplant. Graft and patient survival outcomes after deceased donor liver transplant improved, approximating pre-COVID-19 pandemic levels, with 5.1% mortality observed at 6 months; 6.8%, at 1 year; 12.7%, at 3 years; 19.8%, at 5 years; and 35.7%, at 10 years. Five-year graft and patient survival rates after living donor liver transplant exceeded those of deceased donor liver transplant. Candidates receiving model for end-stage liver disease exception points for hepatocellular carcinoma constituted 15.5% of transplants performed in 2022, with similar transplant rates and posttransplant outcomes compared to cases without hepatocellular carcinoma exception. In 2022, more pediatric liver transplant candidates were added to the waiting list and underwent transplant compared with either of the preceding 2 years, with an uptick in living donor liver transplant volume. Although pretransplant mortality has improved after the recent policy change prioritizing pediatric donors for pediatric recipients, still, in 2022, 50 children died or were removed from the waiting list for being too sick to undergo transplant. Posttransplant mortality among pediatric liver transplant recipients remained notable, with death occurring in 4.0% at 6 months, 6.0% at 1 year, 8.2% at 3 years, 9.8% at 5 years, and 13.9% at 10 years. Similar to adult living donor recipients, pediatric living donor recipients had better 5-year patient survival compared with deceased donor recipients.

OPTN/SRTR 2022 Annual Data Report: Intestine.

Intestine remains the least frequently transplanted solid organ, although the survival and quality-of-life benefits of transplant to individuals with irreversible intestinal failure have been well demonstrated. The trend seen over the past 15 years of fewer listings and fewer transplants appears to be continuing, most noticeably in infants, children, and adolescents. There were only 146 additions to the intestine waiting list in 2022, and the proportion of adult candidates continues to increase, so that now 61% of the intestine waiting list are adult candidates. There has been little change in the distribution by sex, race and ethnicity, or primary diagnosis on the waiting list, or for those receiving transplant. The transplant rate for adults has decreased to 55.6 transplants per 100 patient-years, but the pediatric transplant rate remains relatively stable at 22.8 transplants per 100 patient-years. The decrease in transplant rates for adults is primarily the result of falling rates for those listed for combined intestine-liver, and this is reflected in the pretransplant mortality rates, which are twice as high for candidates in need of both organs compared with those listed for intestine alone. Overall, intestine transplant numbers decreased to a total of 82 intestine transplants in 2022, only one above the lowest ever value of 81 in 2019. No major changes were seen in the immunosuppression protocols, with most recipients having induction therapy and tacrolimus-based maintenance. Graft failure rates appear to have improved at 1, 3, and 5 years for intestine without liver, but this is not seen for combined intestine-liver. Graft and patient survival are better for pediatric recipients compared with adult recipients for both liver-inclusive and liver-exclusive transplant. Rates of posttransplant lymphoproliferative disorder are higher for recipients of intestine without liver.

Dynamic prediction modeling of postoperative mortality among patients undergoing surgical aortic valve replacement in a statewide cohort over a 12-year period.

Objective: Clinical prediction models for surgical aortic valve replacement mortality, are valuable decision tools but are often limited in their ability to account for changes in medical practice, patient selection, and the risk of outcomes over time. Recent research has identified methods to update models as new data accrue, but their effect on model performance has not been rigorously tested. Methods: The study population included 44,546 adults who underwent an isolated surgical aortic valve replacement from January 1, 1999, to December 31, 2018, statewide in Pennsylvania. After chronologically splitting the data into training and validation sets, we compared calibration, discrimination, and accuracy measures amongst a nonupdating model to 2 methods of model updating: calibration regression and the novel dynamic logistic state space model. Results: The risk of mortality decreased significantly during the validation period (P < .01) and the nonupdating model demonstrated poor calibration and reduced accuracy over time. Both updating models maintained better calibration (Hosmer-Lemeshow χ2 statistic) than the nonupdating model: nonupdating (156.5), calibration regression (4.9), and dynamic logistic state space model (8.0). Overall accuracy (Brier score) was consistently better across both updating models: dynamic logistic state space model (0.0252), calibration regression (0.0253), and nonupdating (0.0256). Discrimination improved with the dynamic logistic state space model (area under the curve, 0.696) compared with the nonupdating model (area under the curve, 0.685) and calibration regression method (area under the curve, 0.687). Conclusions: Dynamic model updating can improve model accuracy, discrimination, and calibration. The decision as to which method to use may depend on which measure is most important in each clinical context. Because competing therapies have emerged for valve replacement models, updating may guide clinical decision making.

OPTN/SRTR 2021 Annual Data Report: Intestine.

There has been just over 30 years of experience in clinical intestine transplant. A rise in demand until 2007 with improving transplant outcomes preceded a subsequent fall in demand due, at least in part, to improvements in pretransplant care of patients with intestinal failure. Over the past 10 to 12 years, there has been no suggestion of an increase in demand and, particularly for adult transplant, there may be a continued trend toward fewer additions to the waiting list and fewer transplants, especially in those needing combined intestine-liver transplant. In addition, over the same period there has been no noticeable improvement in graft survival, with 1- and 5-year graft failure rates averaging 21.6% and 52.5%, respectively, for intestine-alone transplants and 28.6% and 47.2%, respectively, for combined intestine-liver allografts.

OPTN/SRTR 2021 Annual Data Report: Liver.

In 2021, liver transplant volume continued to grow, with a record 9,234 transplants performed in the United States, 8,665 (93.8%) from deceased donors and 569 (6.2%) from living donors. There were 8,733 (94.6%) adult and 501 (5.4%) pediatric liver transplant recipients. An increase in the number of deceased donor livers corresponded to an increase in the overall transplant rate and shorter waiting times, although still 10.0% of livers that were recovered were not transplanted. Alcohol-associated liver disease was the leading indication for both waitlist registration and liver transplant in adults, outpacing nonalcoholic steatohepatitis, while biliary atresia remained the leading indication for children. Related to allocation policy changes implemented in 2019, the proportion of liver transplants performed for hepatocellular carcinoma has decreased. Among adult candidates listed for liver transplant in 2020, 37.7% received a deceased donor liver transplant within 3 months, 43.8% within 6 months, and 53.3% within 1 year. Pretransplant mortality improved for children following implementation of acuity circle-based distribution. Short-term graft and patient survival outcomes up to 1 year worsened for adult deceased and living donor liver transplant recipients, which is a reversal of previous trends and coincided with the onset of the COVID-19 pandemic in early 2020. Longer-term outcomes among adult deceased donor liver transplant recipients were unaffected, with overall posttransplant mortality rates of 13.3% at 3 years, 18.6% at 5 years, and 35.9% at 10 years. Pretransplant mortality improved for children following implementation of acuity circle-based distribution and prioritization of pediatric donors to pediatric recipients in 2020. Pediatric living donor recipients had superior graft and patient survival outcomes compared with deceased donor recipients at all time points.

Dynamic logistic state space prediction model for clinical decision making.

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data.

A Conceptual Model for Sources of Differential Selection in Lung Transplant Allocation.

Rationale: In the United States, donor lungs are allocated to transplant candidates on the basis of lung allocation scores (LAS). However, additional factors beyond the LAS can impact who is transplanted, including listing and donor-organ acceptance practices. These factors can result in differential selection, undermining the objectivity of lung allocation. Yet their impact on the lung transplant pathway has been underexplored. Objectives: We sought to systematically examine sources of differential selection in lung transplantation via qualitative methods. Methods: We conducted semistructured qualitative interviews with lung transplant surgeons and pulmonologists in the United States between June 2019 and June 2020 to understand clinician perspectives on differential selection in lung transplantation and the LAS. Results: A total of 51 respondents (30 surgeons and 21 pulmonologists) identified many sources of differential selection arising throughout the pathway from referral to transplantation. We synthesized these sources into a conceptual model with five themes: 1) transplant center's degree of risk tolerance and accountability; 2) successfulness and fairness of the LAS; 3) donor-organ availability and regional competition; 4) patient health versus program health; and 5) access to care versus responsible stewardship of organs. Conclusions: Our conceptual model demonstrates how differential selection can arise throughout lung transplantation and facilitates the further study of such selection. As new organ allocation models are developed, differential selection should be considered carefully to ensure that these models are more equitable.

Clinical impact of a modified lung allocation score that mitigates selection bias.

BACKGROUND: The Lung Allocation Score (LAS) is used in the U.S. to prioritize lung transplant candidates. Selection bias, induced by dependent censoring of waitlisted candidates and prediction of posttransplant survival among surviving, transplanted patients only, is only partially addressed by the LAS. Recently, a modified LAS (mLAS) was designed to mitigate such bias. Here, we estimate the clinical impact of replacing the LAS with the mLAS. METHODS: We considered lung transplant candidates waitlisted during 2016 and 2017. LAS and mLAS scores were computed for each registrant at each observed organ offer date; individuals were ranked accordingly. Patient characteristics associated with better priority under the mLAS were investigated via logistic regression and generalized linear mixed models. We also determined whether differences in rank were explained more by changes in predicted pre- or posttransplant survival. Simulations examined how 1-year waitlist, posttransplant, and overall survival might change under the mLAS. RESULTS: Diagnosis group, 6-minute walk distance, continuous mechanical ventilation, functional status, and age demonstrated the highest impact on differential allocation. Differences in rank were explained more by changes in predicted pretransplant survival than changes in predicted posttransplant survival, suggesting that selection bias has more impact on estimates of waitlist urgency. Simulations suggest that for every 1000 waitlisted individuals, 12.8 (interquartile range: 5.2-24.3) fewer waitlist deaths per year would occur under the mLAS, without compromising posttransplant and overall survival. CONCLUSIONS: Implementing a mLAS that mitigates selection bias into clinical practice can lead to important differences in allocation and possibly modest improvement in waitlist survival.

Decision-making Among Hepatitis C Virus-negative Transplant Candidates Offered Organs from Donors with HCV Infection.

Background: Historically, many organs from deceased donors with hepatitis C virus (HCV) were discarded. The advent of highly curative direct-acting antiviral (DAA) therapies motivated transplant centers to conduct trials of transplanting HCV-viremic organs (nucleic acid amplification test positive) into HCV-negative recipients, followed by DAA treatment. However, the factors that influence candidates' decisions regarding acceptance of transplant with HCV-viremic organs are not well understood. Methods: To explore patient-level perceptions, influences, and experiences that inform candidate decision-making regarding transplant with organs from HCV-viremic donors, we conducted a qualitative semistructured interview study embedded within 3 clinical trials investigating the safety and efficacy of transplanting lungs and kidneys from HCV-viremic donors into HCV-negative recipients. The study was conducted from June 2019 to March 2021. Results: Among 44 HCV-negative patients listed for organ transplant who were approached for enrollment in the applicable clinical trial, 3 approaches to decision-making emerged: positivist, risk analyses, and instinctual response. Perceptions of risk contributed to conceptualizations of factors influencing decisions. Moreover, most participants relied on multiple decision-making approaches, either simultaneously or sequentially. Conclusions: Understanding how different decisional models influence patients' choices regarding transplant with organs from HCV-viremic donors may promote shared decision-making among transplant patients and providers.

Modified Brier score for evaluating prediction accuracy for binary outcomes.

The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer Surveillance Consortium and compare the performance of predicting breast cancer risk using the models with and without its most important predictor.

A Comparison of Methods to Detect Changes in Prediction Models.

BACKGROUND: Prediction models inform decisions in many areas of medicine. Most models are fitted once and then applied to new (future) patients, despite the fact that model coefficients can vary over time due to changes in patients' clinical characteristics and disease risk. However, the optimal method to detect changes in model parameters has not been rigorously assessed. METHODS: We simulated data, informed by post-lung transplant mortality data and tested the following two approaches for detecting model change: (1) the "Direct Approach," it compares coefficients of the model refit on recent data to those at baseline; and (2) "Calibration Regression," it fits a logistic regression model of the log-odds of the observed outcomes versus the linear predictor from the baseline model (i.e., the log-odds of the predicted probabilities obtained from the baseline model) and tests whether the intercept and slope differ from 0 and 1, respectively. Four scenarios were simulated using logistic regression for binary outcomes as follows: (1) we fixed all model parameters, (2) we varied the outcome prevalence between 0.1 and 0.2, (3) we varied the coefficient of one of the ten predictors between 0.2 and 0.4, and (4) we varied the outcome prevalence and coefficient of one predictor simultaneously. RESULTS: Calibration regression tended to detect changes sooner than the Direct Approach, with better performance (e.g., larger proportion of true claims). When the sample size was large, both methods performed well. When two parameters changed simultaneously, neither method performed well. CONCLUSION: Neither change detection method examined here proved optimal under all circumstances. However, our results suggest that if one is interested in detecting a change in overall incidence of an outcome (e.g., intercept), the Calibration Regression method may be superior to the Direct Approach. Conversely, if one is interested in detecting a change in other model covariates (e.g., slope), the Direct Approach may be superior.

Comparison of dynamic updating strategies for clinical prediction models.

BACKGROUND: Prediction models inform many medical decisions, but their performance often deteriorates over time. Several discrete-time update strategies have been proposed in the literature, including model recalibration and revision. However, these strategies have not been compared in the dynamic updating setting. METHODS: We used post-lung transplant survival data during 2010-2015 and compared the Brier Score (BS), discrimination, and calibration of the following update strategies: (1) never update, (2) update using the closed testing procedure proposed in the literature, (3) always recalibrate the intercept, (4) always recalibrate the intercept and slope, and (5) always refit/revise the model. In each case, we explored update intervals of every 1, 2, 4, and 8 quarters. We also examined how the performance of the update strategies changed as the amount of old data included in the update (i.e., sliding window length) increased. RESULTS: All methods of updating the model led to meaningful improvement in BS relative to never updating. More frequent updating yielded better BS, discrimination, and calibration, regardless of update strategy. Recalibration strategies led to more consistent improvements and less variability over time compared to the other updating strategies. Using longer sliding windows did not substantially impact the recalibration strategies, but did improve the discrimination and calibration of the closed testing procedure and model revision strategies. CONCLUSIONS: Model updating leads to improved BS, with more frequent updating performing better than less frequent updating. Model recalibration strategies appeared to be the least sensitive to the update interval and sliding window length.

Mitigating selection bias in organ allocation models.

BACKGROUND: The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions. METHODS: We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots. RESULTS: The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization. CONCLUSIONS: Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.

Survey to determine the relative importance of clinical factors used to make empiric antibiotic decisions.

A cross-sectional survey study of inpatient prescribers in a university health system was performed to assess the importance they place on different clinical risk factors when making empiric antibiotic decisions. Our findings show that these clinical risk factors were weighted differently based on the clinical scenario and the type of prescriber.