Variability in donor organ offer acceptance and lung transplantation survival.

BACKGROUND: Lung transplantation offers a survival benefit for patients with end-stage lung disease. When suitable donors are identified, centers must accept or decline the offer for a matched candidate on their waitlist. The degree to which variability in per-center offer acceptance practices impacts candidate survival is not established. The purpose of this study was to determine the degree of variability in per-center rates of lung transplantation offer acceptance and to ascertain the associated contribution to observed differences in per-center waitlist mortality. METHODS: We performed a retrospective cohort study of candidates waitlisted for lung transplantation in the US using registry data. Logistic regression was fit to assess the relationship of offer acceptance with donor, candidate, and geographic factors. Listing center was evaluated as a fixed effect to determine the adjusted per-center acceptance rate. Competing risks analysis employing the Fine-Gray model was undertaken to establish the relationship between adjusted per-center acceptance and waitlist mortality. RESULTS: Of 15,847 unique organ offers, 4,735 (29.9%) were accepted for first-ranked candidates. After adjustment for important covariates, transplant centers varied markedly in acceptance rate (9%-67%). Higher cumulative incidence of 1-year waitlist mortality was associated with lower acceptance rate. For every 10% increase in adjusted center acceptance rate, the risk of waitlist mortality decreased by 36.3% (sub-distribution hazard ratio 0.637; 95% confidence interval 0.592-0.685). CONCLUSIONS: Variability in center-level behavior represents a modifiable risk factor for waitlist mortality in lung transplantation. Further intervention is needed to standardize center-level offer acceptance practices and minimize waitlist mortality.

Diffusion tensor imaging tensor shape analysis for assessment of regional white matter differences.

Purpose The purpose of this study was to investigate a novel tensor shape plot analysis technique of diffusion tensor imaging data as a means to assess microstructural differences in brain tissue. We hypothesized that this technique could distinguish white matter regions with different microstructural compositions. Methods Three normal canines were euthanized at seven weeks old. Their brains were imaged using identical diffusion tensor imaging protocols on a 7T small-animal magnetic resonance imaging system. We examined two white matter regions, the internal capsule and the centrum semiovale, each subdivided into an anterior and posterior region. We placed 100 regions of interest in each of the four brain regions. Eigenvalues for each region of interest triangulated onto tensor shape plots as the weighted average of three shape metrics at the plot's vertices: CS, CL, and CP. Results The distribution of data on the plots for the internal capsule differed markedly from the centrum semiovale data, thus confirming our hypothesis. Furthermore, data for the internal capsule were distributed in a relatively tight cluster, possibly reflecting the compact and parallel nature of its fibers, while data for the centrum semiovale were more widely distributed, consistent with the less compact and often crossing pattern of its fibers. This indicates that the tensor shape plot technique can depict data in similar regions as being alike. Conclusion Tensor shape plots successfully depicted differences in tissue microstructure and reflected the microstructure of individual brain regions. This proof of principle study suggests that if our findings are reproduced in larger samples, including abnormal white matter states, the technique may be useful in assessment of white matter diseases.