Liver Transplantation in Recipients With a Positive Crossmatch: A Retrospective Single-Center Match-Pair Analysis

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.

Translational studies in liver transplantation often require an endpoint of graft function or dysfunction beyond graft loss. Prior definitions of early allograft dysfunction (EAD) vary, and none have been validated in a large multicenter population in the Model for End-Stage Liver Disease (MELD) era. We examined an updated definition of EAD to validate previously used criteria, and correlated this definition with graft and patient outcome. We performed a cohort study of 300 deceased donor liver transplants at 3 U.S. programs. EAD was defined as the presence of one or more of the following previously defined postoperative laboratory analyses reflective of liver injury and function: bilirubin >or=10mg/dL on day 7, international normalized ratio >or=1.6 on day 7, and alanine or aspartate aminotransferases >2000 IU/L within the first 7 days. To assess predictive validity, the EAD definition was tested for association with graft and patient survival. Risk factors for EAD were assessed using multivariable logistic regression. Overall incidence of EAD was 23.2%. Most grafts met the definition with increased bilirubin at day 7 or high levels of aminotransferases. Of recipients meeting the EAD definition, 18.8% died, as opposed to 1.8% of recipients without EAD (relative risk = 10.7 [95% confidence interval: 3.6, 31.9] P < 0.0001). More recipients with EAD lost their grafts (26.1%) than recipients with no EAD (3.5%) (relative risk = 7.4 [95% confidence interval: 3.4, 16.3] P < 0.0001). Donor age and MELD score were significant EAD risk factors in a multivariate model. In summary a simple definition of EAD using objective posttransplant criteria identified a 23% incidence, and was highly associated with graft loss and patient mortality, validating previously published criteria. This definition can be used as an endpoint in translational studies aiming to identify mechanistic pathways leading to a subgroup of liver grafts with clinical expression of suboptimal function. (c) 2010 AASLD.