An exploratory analysis of patient factors influencing acceptance of extended criteria liver grafts.

INTRODUCTION AND OBJECTIVES: There is a shortage of ideal donor organs with consequential increasing waitlist times, drop-off, and mortality. Teams have thus extended the donor criteria. Little is known about patients' actual choices and what factors may influence their decisions regarding different extended criteria liver grafts. PATIENTS AND METHODS: The documented acceptance or refusal of seven extended criteria liver graft types of patients consented for transplant in a single institution over a 2-year period was reviewed. Patient factors including sex, age, indication, aetiology, and model for end-stage liver disease (MELD) score were analysed using logistic regression. RESULTS: Most patients were willing to accept most graft types. MELD score did not impact the acceptance or refusal of any graft type. Older patients and those with hepatocellular carcinoma (HCC) or ascites had significantly higher rates of acceptance. Hepatitis B or C disease aetiology was predictive of willingness to accept a similarly infected graft, respectively. HCC was predictive of acceptance of grafts from donors with a cancer history. CONCLUSIONS: In general, patients embrace the available extended criteria donors. Our analysis suggests that consent should be revisited as patients deteriorate or ameliorate on the waitlist, especially if in the form of ascites or HCC but not necessarily MELD score.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.