Decision modelling for economic evaluation of liver transplantation.

As the gap between a shortage of organs and the immense demand for liver grafts persists, every available donor liver needs to be optimized for utility, urgency and equity. To overcome this challenge, decision modelling might allow us to gather evidence from previous studies as well as compare the costs and consequences of alternative options. For public health policy and clinical intervention assessment, it is a potentially powerful tool. The most commonly used types of decision analytical models include decision trees, the Markov model, microsimulation, discrete event simulation and the system dynamic model. Analytic models could support decision makers in the field of liver transplantation when facing specific problems by synthesizing evidence, comprising all relevant options, generalizing results to other contexts, extending the time horizon and exploring the uncertainty. For modeling studies of economic evaluation for transplantation, understanding the current nature of the disease is crucial, as well as the selection of appropriate modelling techniques. The quality and availability of data is another key element for the selection and development of decision analytical models. In addition, good practice guidelines should be complied, which is important for standardization and comparability between economic outputs.

Evaluation of published assessment tools for comorbidity in liver transplantation: a systematic review protocol.

INTRODUCTION: Liver transplantation is considered the best therapy option for end-stage liver disease. Different factors including recipient comorbidity at time of transplantation are supposed to have substantial impact on outcomes. Although several studies have focused on comorbidity assessment indices for liver transplant recipients, there is no systematic review available on the methodological details and prognostic accuracy of these instruments. The aim of this study is to systematically review recipient comorbidity assessment indices in the context of liver transplantation. METHODS AND ANALYSIS: PubMed, Embase, Web of Science and PsyINFO databases will be searched. Studies describing, using or evaluating specific assessment tools to predict the effect of comorbidity on clinical outcomes after liver transplantation will be included. The selection will be conducted independently by two reviewers. The study characteristics and methodological information on published comorbidity assessment tools will be extracted into a predefined structural table. This approach will be deployed to systematically extract information on the validity, reliability and practical feasibility of investigated comorbidity assessment tools for comparative evaluation. Narrative information synthesis will be conducted, and additional meta-analytical comparison will be performed, if appropriate. ETHICS AND DISSEMINATION: All data are collected from published literature. Thus, formal ethics review for the research is not required. The findings of this systematic review will be published in a peer-reviewed journal and presented at relevant conferences. The results of this systematic review will be highly relevant for further research on prognostic models, clinical decision making and optimisation of donor organ allocation. PROSPERO REGISTRATION NUMBER: CRD42017074609.

Statistical approach to quality assessment in liver transplantation.

PURPOSE: This study investigated the utility of retrospective two one-sided cumulative sum (CUSUM) charts combined with multivariable regression analysis in liver transplantation for transplant center benchmarking. METHODS: One thousand seven hundred and forty-nine consecutive adult primary liver transplants (January 1, 1983 to December 31, 2012) were analyzed retrospectively with two one-sided CUSUM chart analysis of 90-day mortality. RESULTS: Three eras and two subseries in latest era 3 were identified due to graphically delineated relevant shifts in mean 90-day mortality. Delineation of eras 1, 2, and 3 coincided with relevant changes in allocation policies. CUSUM analysis detected a resurgence of higher mean 90-day mortality in era 3 after results had improved continuously over 25 years. In era 3, two subseries were identified with improving mean 90-day mortality rates from 15.4% in subseries 1 to 8.9% in the following subseries 2. The quantitative influence of independent risk factors on 90-day mortality differed markedly between all identified eras and subseries as assessed with multivariable regression analysis deployed on era-specific subcohorts. CONCLUSION: The assessed methodology is able to identify meaningful center-specific eras and subseries of liver transplantation with striking alterations of the significance and weight of outcome drivers for post-transplant 90-day mortality over time. This warrants the introduction of prospective risk-adjusted two one-sided CUSUM chart analysis into quality management in liver transplantation in Germany with the goal to obtain alarm signals as early as possible.

Prognostic Abilities and Quality Assessment of Models for the Prediction of 90-Day Mortality in Liver Transplant Waiting List Patients.

BACKGROUND: Model of end-stage liver disease (MELD)-score and diverse variants are widely used for prognosis on liver transplant waiting-lists. METHODS: 818 consecutive patients on the liver transplant waiting-list included to calculate the MELD, MESO Index, MELD-Na, UKELD, iMELD, refitMELD, refitMELD-Na, upMELD and PELD-scores. Prognostic abilities for 90-day mortality were investigated applying Receiver-operating-characteristic-curve analysis. Independent risk factors for 90-day mortality were identified with multivariable binary logistic regression modelling. Methodological quality of the underlying development studies was assessed with a systematic assessment tool. RESULTS: 74 patients (9%) died on the liver transplant waiting list within 90 days after listing. All but one scores, refitMELD-Na, had acceptable prognostic performance with areas under the ROC-curves (AUROCs)>0.700. The iMELD performed best (AUROC = 0.798). In pediatric cases, the PELD-score just failed to reach the acceptable threshold with an AUROC = 0.699. All scores reached a mean quality score of 72.3%. Highest quality scores could be achieved by the UKELD and PELD-scores. Studies specifically lack statistical validity and model evaluation. CONCLUSIONS: Inferior quality assessment of prognostic models does not necessarily imply inferior prognostic abilities. The iMELD might be a more reliable tool representing urgency of transplantation than the MELD-score. PELD-score is assumedly not accurate enough to allow graft allocation decision in pediatric liver transplantation.

The new liver allocation score for transplantation is validated and improved transplant survival benefit in Germany but not in the United Kingdom.

Prognostic models for the prediction of 90-day mortality after transplantation with pretransplant donor and recipient variables are needed to calculate transplant benefit. Transplants in adult recipients in Germany (Hannover, n = 770; Kiel, n = 234) and the United Kingdom (Birmingham, n = 829) were used for prognostic model design and validation in separate training and validation cohorts. The survival benefit of transplantation was estimated by subtracting the observed posttransplant 90-day mortality from the expected 90-day mortality without transplantation determined by the Model for End-Stage Liver Disease (MELD) score. A prognostic model called the liver allocation score (LivAS) was derived using a randomized sample from Hannover using pretransplant donor and recipient variables. This model could be validated in the German training and validation cohorts (area under the receiver operating characteristic curve [AUROC] > 0.70) but not in the English cohort (AUROC, 0.58). Although 90-day mortality rates after transplantation were 13.7% in Hannover, 12.1% in Kiel, and 8.3% in Birmingham, the calculated 90-day survival benefits of transplantation were 6.8% in Hannover, 7.8% in Kiel, and 2.8% in Birmingham. Deployment of the LivAS for limiting allocation to donor and recipient combinations with likely 90-day survival as indicated by pretransplant LivAS values below the cutoff value would have increased the survival benefit to 12.9% in the German cohorts, whereas this would have decreased the benefit in England to 1.3%. The English and German cohorts revealed significant differences in 21 of 28 pretransplant variables. In conclusion, the LivAS could be validated in Germany and may improve German allocation policies leading to greater survival benefits, whereas validation failed in England due to profound differences in the selection criteria for liver transplantation. This study suggests the need for national prognostic models. Even though the German centers had higher rates of 90-day mortality, estimated survival benefits were greater. Liver Transplantation 22 743-756 2016 AASLD.