Age Disparities in Access to First and Repeat Kidney Transplantation.

BACKGROUND: Evidence suggests that older patients are less frequently placed on the waiting list for kidney transplantation (KT) than their younger counterparts. The trends and magnitude of this age disparity in access to first KT and repeat KT (re-KT) remain unclear. METHODS: Using the US Renal Data System, we identified 2 496 743 adult transplant-naive dialysis patients and 110 338 adult recipients with graft failure between 1995 and 2018. We characterized the secular trends of age disparities and used Cox proportional hazard models to compare the chances of listing and receiving first KT versus re-KT by age (18-64 y versus ≥65 y). RESULTS: Older transplant-naive dialysis patients were less likely to be listed (adjusted hazard ratio [aHR] = 0.18; 95% confidence interval [CI], 0.17-0.18) and receive first KT (aHR = 0.88; 95% CI, 0.87-0.89) compared with their younger counterparts. Additionally, older patients with graft failure had a lower chance of being listed (aHR = 0.40; 95% CI, 0.38-0.41) and receiving re-KT (aHR = 0.76; 95% CI, 0.72-0.81). The magnitude of the age disparity in being listed for first KT was greater than that for re-KT ( Pinteraction < 0.001), and there were no differences in the age disparities in receiving first KT or re-KT ( Pinteraction = 0.13). Between 1995 and 2018, the age disparity in listing for first KT reduced significantly ( P < 0.001), but the age disparities in re-KT remained the same ( P = 0.16). CONCLUSIONS: Age disparities exist in access to both first KT and re-KT; however, some of this disparity is attenuated among older adults with graft failure. As the proportion of older patients with graft failure rises, a better understanding of factors that preclude their candidacy and identification of appropriate older patients are needed.

Gabapentin, Concomitant Prescription of Opioids, and Benzodiazepines among Kidney Transplant Recipients.

BACKGROUND: Gabapentinoids, commonly used for treating neuropathic pain, may be misused and coprescribed with opioid and benzodiazepine, increasing the risk of mortality and dependency among kidney transplant recipients. METHODS: We identified adult kidney transplant recipients who enrolled in Medicare Part D in 2006-2017 using the United States Renal Data System/Medicare claims database. We characterized recipients' post-transplant concomitant prescription of gabapentinoids, opioids, and benzodiazepine stratified by transplant year and recipient factors (age, sex, race, and diabetes). We investigated whether concomitant prescriptions were associated with postkidney transplant mortality using Cox regression. Models incorporated inverse probability weighting to adjust for confounders. RESULTS: Among 63,359 eligible recipients, 13% of recipients filled at least one gabapentinoid prescription within 1 year after kidney transplant. The prevalence of gabapentinoid prescriptions increased by 70% over the study period (16% in 2017 versus 10% in 2006). Compared with nonusers, gabapentinoids users were more likely to have diabetes (55% versus 37%) and obesity (46% versus 34%). Of the 8509 recipients with gabapentinoid prescriptions, 45% were coprescribed opioids, 7% were coprescribed benzodiazepines, and 3% were coprescribed both opioids and benzodiazepines. Compared with no study prescriptions, gabapentinoid monotherapy (adjusted hazard ratio [aHR]=1.25; 95% confidence interval [CI], 1.16 to 1.32) and combination therapy (gabapentinoids and opioids [aHR=1.49; 95% CI, 1.39 to 1.60], gabapentinoids and benzodiazepines [aHR=1.46; 95% CI, 1.03 to 2.08], and coprescribing all three [aHR=1.88; 95% CI, 1.18 to 2.98]) were all associated with a higher risk of postkidney transplant mortality. CONCLUSIONS: Gabapentinoid coprescription with both benzodiazepines and opioids among kidney transplant recipients increased over time. Kidney transplant recipients prescribed gabapentinoids had a higher risk of post-transplant mortality, and the risk was higher with opioids or benzodiazepine coprescription.

Black patients are more likely to undergo parathyroidectomy for secondary hyperparathyroidism.

BACKGROUND: Prior studies have demonstrated racial disparities in the severity of secondary hyperparathyroidism among dialysis patients. Our primary objective was to study the racial and socioeconomic differences in the timing and likelihood of parathyroidectomy in patients with secondary hyperparathyroidism. METHODS: We used the United States Renal Data System to identify 634,428 adult (age ≥18) patients who were on maintenance dialysis between 2006 and 2016 with Medicare as their primary payor. Adjusted multivariable Cox regression was performed to quantify the differences in parathyroidectomy by race. RESULTS: Of this cohort, 27.3% (173,267) were of Black race. Compared to 15.4% of White patients, 23.1% of Black patients lived in a neighborhood that was below a predefined poverty level (P < .001). The cumulative incidence of parathyroidectomy at 10 years after dialysis initiation was 8.8% among Black patients compared to 4.3% among White patients (P < .001). On univariable analysis, Black patients were more likely to undergo parathyroidectomy (adjusted hazard ratio = 1.83; 95% confidence interval, 1.74-1.93). This association persisted after adjusting for age, sex, cause of end-stage renal disease, body mass index, comorbidities, dialysis modality, and poverty level (adjusted hazard ratio = 1.35; 95% confidence interval, 1.27-1.43). Therefore, patient characteristics and socioeconomic status explained 26% of the association between race and likelihood of parathyroidectomy. CONCLUSION: Black patients with secondary hyperparathyroidism due to end-stage renal disease are more likely to undergo parathyroidectomy with shorter intervals between dialysis initiation and parathyroidectomy. This association is only partially explained by patient characteristics and socioeconomic factors.

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform.

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (emax), the minimum error (emin), the 95th percentile error (e95%), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.

Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT.

Monte Carlo (MC) method is commonly considered as the most accurate approach for particle transport simulation because of its capability to precisely model physics interactions and simulation geometry. Conventionally, MC simulation is performed in a particle-by-particle fashion. In certain problems such as computing scattered X-ray photon signal at a detector of CT, the conventional simulation scheme suffers from low efficiency mainly due to the fact that abundant photons are simulated but do not reach the detector. The computational resources spent on those photons are therefore wasted. To solve this problem, this study develops a novel GPU-based Metropolis MC (gMMC) with a novel path-by-path simulation scheme and demonstrates its effectiveness in an example problem of scattered X-ray photon calculation in CT. In contrast to the conventional MC approach, gMMC samples an entire photon path extending from the X-ray source to the detector using Metropolis-Hasting algorithm. The path-by-path simulation scheme ensures contribution of every sampled event to the signal of interest, improving overall efficiency. We benchmark gMMC against an in-house developed GPU-based MC tool, gMCDRR, which performs simulations in the conventional particle-by-particle fashion. gMMC reaches speed up factors of 37~48 times in simple phantom cases and 20-34 times in real patient cases. The results calculated by gMCDRR and gMMC agree well with average differences < 3%.