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

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

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

Prevalence of frailty among kidney transplant candidates and recipients in the United States: Estimates from a National Registry and Multicenter Cohort Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154612/1/ajt15709.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154612/2/ajt15709_am.pd

Harms in Systematic Reviews Paper 2: Methods used to assess harms are neglected in systematic reviews of gabapentin

Objective: We compared methods used with current recommendations for synthesizing harms in systematic reviews and meta-analyses (SRMAs) of gabapentin. Study Design & Setting: We followed recommended systematic review practices. We selected reliable SRMAs of gabapentin (i.e., met a pre-defined list of methodological criteria) that assessed at least one harm. We extracted and compared methods in four areas: pre-specification, searching, analysis, and reporting. Whereas our focus in this paper is on the methods used, Part 2 examines the results for harms across reviews. Results: We screened 4320 records and identified 157 SRMAs of gabapentin, 70 of which were reliable. Most reliable reviews (51/70; 73%) reported following a general guideline for SRMA conduct or reporting, but none reported following recommendations specifically for synthesizing harms. Across all domains assessed, review methods were designed to address questions of benefit and rarely included the additional methods that are recommended for evaluating harms. Conclusion: Approaches to assessing harms in SRMAs we examined are tokenistic and unlikely to produce valid summaries of harms to guide decisions. A paradigm shift is needed. At a minimal, reviewers should describe any limitations to their assessment of harms and provide clearer descriptions of methods for synthesizing harms

Harms in Systematic Reviews Paper 3: Given the same data sources, systematic reviews of gabapentin have different results for harms

Objective: In this methodologic study (Part 2 of 2), we examined the overlap in sources of evidence and the corresponding results for harms in systematic reviews for gabapentin. Study Design & Setting: We extracted all citations referenced as sources of evidence for harms of gabapentin from 70 systematic reviews, as well as the harms assessed and numerical results. We assessed consistency of harms between pairs of reviews with a high degree of overlap in sources of evidence (>50%) as determined by corrected covered area (CCA). Results: We found 514 reports cited across 70 included reviews. Most reports (244/514, 48%) were not cited in more than one review. Among 18 pairs of reviews, we found reviews had differences in which harms were assessed and their choice to meta-analyze estimates or present descriptive summaries. When a specific harm was meta-analyzed in a pair of reviews, we found similar effect estimates. Conclusion: Differences in harms results across reviews can occur because the choice of harms is driven by reviewer preferences, rather than standardized approaches to selecting harms for assessment. A paradigm shift is needed in the current approach to synthesizing harms