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Objective: Infants undergoing heart transplantation have the highest early posttransplant mortality of any age group. We sought to determine the pretransplantation factors associated with in-hospital mortality in transplanted infants in the current era. Methods: All infants under 12 months of age who underwent primary heart transplantation during a recent 10-year period (1999–2009) in the United States were identified using the Organ Procurement and Transplant Network database. Multivariable logistic regression was used to identify independent pretransplantation factors associated with in-hospital mortality. Results: Of 730 infants in the study (median age 3.8 months), 462 (63%) had congenital heart disease, 282 (39%) were supported by a ventilator, 94 (13%) with extracorporeal membrane oxygenation, and 22 (3%) with a ventricular assist device at the time of transplantation. Overall, 82 (11.2%) infants died before their initial hospital discharge. In adjusted analysis, in-hospital mortality was associated with repaired congenital heart disease (odds ratio [OR], 3.6; 95% confidence interval [CI], 1.8, 7.2), unrepaired congenital heart disease not on prostaglandin E (OR, 2.8; CI, 1.3, 6.1), extracorporeal membrane oxygenator support (OR, 6.1; CI, 2.8, 13.4), ventilator support (OR, 4.4; CI, 2.3, 8.3), creatinine clearance less than 40 mL · min−1 · 1.73 m−2 (OR, 3.1; CI, 1.7, 5.3), and dialysis (OR, 6.2; CI, 2.1, 18.3) at transplantation. Conclusions: One in 9 infants undergoing heart transplantation dies before hospital discharge. Pretranplantation factors associated with early mortality include congenital heart disease, extracorporeal membrane oxygenator support, mechanical ventilation, and renal failure. Risk stratification for early posttransplant mortality among infants listed for heart transplantation may improve decision-making for transplant eligibility, organ allocation, and posttransplant interventions to reduce mortality. [Copyright &y& Elsevier]

Clinically relevant segmentation often incorporates physician insight in the segmentation process (eg. overestimation of tumors). This is a primary reason in the continued use of per-slice based segmentation approaches. Segmenting structures directly by interacting with a volume space provides the advantages of being time efficient and accurate in contrast to per-slice based methods. In this approach volume navigation and parameter specification become critical towards the accuracy of the results obtained. In this paper we present some preliminary results in our research towards segmentation using a haptic device for navigation and parameter specification in an immersive environment. To incorporate the physician's requirements we model the results as a deformable surface model and the user interacts with the surface to obtain the final segmentation.