Morphometric age and survival following kidney transplantation

BackgroundAs patients with chronic kidney disease become older, there is greater need to identify who will most benefit from kidney transplantation. Analytic morphomics has emerged as an objective risk assessment tool distinct from chronologic age. We hypothesize that morphometric age is a significant predictor of survival following transplantation.MethodsA retrospective cohort of 158 kidney transplant patients from 2005 to 2014 with 1‐year preoperative imaging was identified. Based on a control population comprising of trauma patients and kidney donors, morphometric age was calculated using the validated characteristics of psoas area, psoas density, and abdominal aortic calcification. The primary outcome was post‐transplant survival.ResultsCox regression showed morphometric age was a significant predictor of survival (hazard ratio, 1.06 per morphometric year [95% confidence interval, 1.03‐1.08]; P < .001). Chronological age was not significant (hazard ratio, 1.03 per year [0.98‐1.07]; P = .22). Among the chronologically oldest patients, those with younger morphometric age had greater survival rates compared to those with older morphometric age.ConclusionsMorphometric age predicts survival following kidney transplantation. Particularly for older patients, it offers improved risk stratification compared to chronologic age. Morphomics may improve the transplant selection process and provide a greater assessment of prospective survival benefits.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138897/1/ctr13066.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138897/2/ctr13066_am.pd

Age Is Just a Number for Older Kidney Transplant Patients

The rise in the mean age of the global population has led to an increase in older kidney transplant (KT) patients. This demographic shift, coupled with the ongoing organ shortage, requires a nuanced understanding of which older adults are most suitable for KT. Recognizing the increased heterogeneity among older adults and the limitations of solely relying on chronological age, there is a need to explore alternative aging metrics beyond chronological age. In this review, we discuss the impact of older age on access to KT and postoperative outcomes. Emphasizing the need for a comprehensive evaluation that extends beyond chronological age, we explore alternative aging metrics such as frailty, sarcopenia, and cognitive function, underscoring their potential role in enhancing the KT evaluation process. Most importantly, we aim to contribute to the ongoing discourse, fostering an optimized approach to KT for the rapidly growing population of older adults.</p

Practices in the evaluation of potential kidney transplant recipients who are elderly: A survey of U.S. transplant centers

Limited data exist regarding the evaluation and selection of older candidates for transplantation. To help guide the development of program protocols and help define research questions in this area, we surveyed U.S. transplant centers regarding their current practices in the evaluation of older kidney transplant candidates. We emailed a 28‐question survey to the medical and surgical directors of 190 adult kidney transplant programs in the USA. We received usable responses from 59 programs, a 31.1% response rate. Most (76.3%) programs do not have absolute age cutoffs for listing patients, but for the 22.0% of programs that do have cutoffs, the mean age was 79, range 70‐90. Nearly one‐third (29.2%) of programs require a minimum life expectancy to list for transplant, reporting a mean of 4.5 years life expectancy, (range 2‐10). Programs vary significantly in evaluating candidates living in a nursing home or with cognitive impairments. Practices regarding the evaluation of older transplant candidates vary widely between U.S. programs. Further studies are needed on the impact of age and other comorbidities on transplant outcomes, to help guide decisions on which older patients are most appropriate for transplant listing.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138933/1/ctr13088_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138933/2/ctr13088.pd

Criteria for and Appropriateness of Renal Transplantation in Elderly Patients With End-Stage Renal Disease : A Literature Review and Position Statement on Behalf of the European Renal Association-European Dialysis and Transplant Association Descartes Working Group and European Renal Best Practice

During the last 20 years, waiting lists for renal transplantation (RT) have grown significantly older. However, elderly patients (ie ≥65 years of age) are still more rarely referred or accepted to waiting lists and, if enlisted, have less chances of actually receiving a kidney allograft, than younger counterparts. In this review, we looked at evidence for the benefits and risks of RT in the elderly trying to answer the following questions: Should RT be advocated for elderly patients? What should be the criteria to accept elderly patients on the waiting list for RT? What strategies might be used to increase the rate of RT in waitlisted elderly candidates? For selected elderly patients, RT was shown to be superior to dialysis in terms of patient survival. Virtually all guidelines recommend that patients should not be deemed ineligible for RT based on age alone, although a short life expectancy generally might preclude RT. Concerning the assessment of comorbidities in the elderly, special attention should be paid to cardiac evaluation and screening for malignancy. Comorbidity scores and frailty assessment scales might help the decision making on eligibility. Psychosocial issues should also be evaluated. To overcome the scarcity of organ donors, elderly RT candidates should be encouraged to consider expanded criteria donors and living donors, as alternatives to deceased standard criteria donors. It has been demonstrated that expanded criteria donor RT in patients 60 years or older is associated with higher survival rates than remaining on dialysis, whereas living donor RT is superior to all other options.Peer reviewe

Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims

Encompassing numerous nationwide, statewide, and institutional initiatives in the United States, provider profiling has evolved into a major health care undertaking with ubiquitous applications, profound implications, and high-stakes consequences. In line with such a significant profile, the literature has accumulated a number of developments dedicated to enhancing the statistical paradigm of provider profiling. Tackling wide-ranging profiling issues, these methods typically adjust for risk factors using linear predictors. While this approach is simple, it can be too restrictive to characterize complex and dynamic factor-outcome associations in certain contexts. One such example arises from evaluating dialysis facilities treating Medicare beneficiaries with end-stage renal disease. It is of primary interest to consider how the coronavirus disease (COVID-19) affected 30-day unplanned readmissions in 2020. The impact of COVID-19 on the risk of readmission varied dramatically across pandemic phases. To efficiently capture the variation while profiling facilities, we develop a generalized partially linear model (GPLM) that incorporates a neural network. Considering provider-level clustering, we implement the GPLM as a stratified sampling-based stochastic optimization algorithm that features accelerated convergence. Furthermore, an exact test is designed to identify under- and over-performing facilities, with an accompanying funnel plot to visualize profiles. The advantages of the proposed methods are demonstrated through simulation experiments and profiling dialysis facilities using 2020 Medicare claims from the United States Renal Data System.Comment: 8 figures, 6 table

Comorbidities in patients with gout prior to and following diagnosis: case-control study

OBJECTIVES: To determine the burden of comorbidities in patients with gout at diagnosis and the risk of developing new comorbidities post diagnosis. METHODS: There were 39 111 patients with incident gout and 39 111 matched controls identified from the UK Clinical Practice Research Data-link. The risk of comorbidity before (ORs) and after the diagnosis of gout (HRs) were estimated, adjusted for age, sex, diagnosis year, body mass index, smoking and alcohol consumption. RESULTS: Gout was associated with adjusted ORs (95% CIs) of 1.39 (1.34 to 1.45), 1.89 (1.76 to 2.03) and 2.51 (2.19 to 2.86) for the Charlson index of 1-2, 3-4 and >/=5, respectively. Cardiovascular and genitourinary diseases, in addition to hyperlipidaemia, hypothyroidism, anaemia, psoriasis, chronic pulmonary diseases, osteoarthritis and depression, were associated with a higher risk for gout. Gout was also associated with an adjusted HR (95% CI) of 1.41 (1.34 to 1.48) for having a Charlson index >/=1. Median time to first comorbidity was 43 months in cases and 111 months in controls. Risks for incident comorbidity were higher in cardiovascular, genitourinary, metabolic/endocrine and musculoskeletal diseases, in addition to liver diseases, hemiplegia, depression, anaemia and psoriasis in patients with gout. After additionally adjusting for all comorbidities at diagnosis, gout was associated with a HR (95% CI) for all-cause mortality of 1.13 (1.08 to 1.18; p<0.001). CONCLUSIONS: The majority of patients with gout have worse pre-existing health status at diagnosis and the risk of incident comorbidity continues to rise following diagnosis. The range of associated comorbidities is broader than previously recognised and merits further evaluation