Background Induction therapy in deceased donor kidney transplantation (DDKT) is costly, with wide discrepancy in utilization and a limited evidence base, particularly regarding cost-effectiveness. Methods We linked the United States Renal Data System dataset to Medicare claims to estimate cumulative costs, graft survival, and incremental cost-effectiveness ratio (ICER –cost per additional year of graft survival) within 3 years of transplantation in 19,450 DDKT recipients with Medicare as primary payer from 2000 to 2008. We divided the study cohort into high-risk (age>60 years, panel reactive antibody>20%, African American race, Kidney Donor Profile Index>50%, cold ischemia time>24 hours) and low risk (not having any risk factors, comprising approximately 15% of the cohort). Following the elimination of dominated options, we estimated expected ICER among induction categories: no-induction, alemtuzumab, rabbit anti-thymocyte globulin (r-ATG), and interleukin-2 receptor-antagonist. Results No-induction was the least effective and most costly option in both risk groups. Depletional antibodies (r-ATG and alemtuzumab) were more cost-effective across all willingness-to-pay thresholds in the low-risk group. For the high-risk group and its subcategories, the ICER was very sensitive to the graft survival; overall both depletional antibodies were more cost-effective, mainly for higher willingness to pay threshold ($100,000 and $150,000). r-ATG appears to achieve excellent cost-effectiveness acceptability curves (%80 of the recipients) in both risk groups at $50,000 threshold (except age>60 years). In addition, only r-ATG was associated with graft survival benefit over no-induction category (hazard ratio 0.91, 95% confidence interval 0.84 to 0.99) in a multivariable Cox regression analysis. Conclusions Antibody-based induction appears to offer substantial advantages in both cost and outcome compared to no-induction. Overall, depletional induction (preferably r-ATG) appears to offer the greatest benefits.
Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.
Background: Opportunistic infections have become much more considerable in the last decades, especially in immunocompromised patients and due to the medical interventions. Cryptosporidium is a pathogenic protozoan parasite causing diarrhea in children and some times acts as a life threatening opportunistic pathogen in the immunocompromised adults. Objectives: This study aimed to investigate the presence of Cryptosporidium infection among patients undergone renal transplantation, who are at risk for this infection. Patients and Methods: This was a cross-sectional study and the sample collection consisted of 180 renal transplanted patients referred to Shaheed-Beheshti Hospital, Hamadan city, Iran. The stool specimens were concentrated using formalin-ether technique and then the fecal smears were prepared from the sediments. Afterwards, the slides were stained using the Ziehl-Neelsen staining method and then examined for the presence of Cryptosporidium oocysts. Results: One out of 180 fecal samples was positive for Cryptosporidium infection. The infected patient was a 51-year-old woman who had a renal transplantation six years earlier, with continuous use of CellCept ® (mycophenolate mofetil) and prednisolone. The patient had been referred to the hospital with gastrointestinal symptoms. Conclusions: Based on the results of this study the prevalence of cryptosporidiosis was very low in renal transplanted patients in Hamadan city, Iran. It could be concluded that cryptosporidiosis is not a life threatening risk in this region and it probably showed well post-transplantation hygienic status of the patients and/or low oocysts load in the area.
In this work, we introduce a microscopic traffic flow model called Scalar Capacity Model (SCM) which can be used to study the formation of traffic on an airway link for autonomous Unmanned Aerial Vehicles (UAVs) as well as for the ground vehicles on the road. Given the 3D trajectory of UAV flights (as opposed to the 2D trajectory of ground vehicles), the main novelty in our model is to eliminate the commonly used notion of lanes and replace it with a notion of density and capacity of flow, but in such a way that individual vehicle motions can still be modeled. We name this a Density/Capacity View (DCV) of the link capacity and how vehicles utilize it versus the traditional One/Multi-Lane View (OMV). An interesting feature of this model is exhibiting both passing and blocking regimes (analogous to multi-lane or single-lane) depending on the set scalar parameter for capacity. We show the model has linear local (platoon) and asymptotic linear stability. Additionally, we perform numerical simulations and show evidence for non-linear stability. Our traffic flow model is represented by a nonlinear differential equation which we transform into a linear form. This makes our model analytically solvable in the blocking regime and piece-wise analytically solvable in the passing regime. Finally, a key advantage of using our model over an OMV model for representing UAV’s flights is the removal of the artificial restriction on passing via only adjacent lanes. This will result in an improved and more realistic traffic flow for UAVs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.