Obesity is increasingly prevalent among living kidney donor candidates, presenting complex clinical, ethical, and logistical challenges. Although obesity is linked to increased post-donation hypertension, diabetes, proteinuria, and chronic kidney disease, the overall absolute risks are modest and may differ depending on metabolic health and patterns of fat distribution. Current reliance on body mass index alone fails to capture the full spectrum of obesity-related risks, leading to inconsistent donor selection practices and potential inequities. This review examines the pathophysiological mechanisms of obesity-related kidney injury and available evidence on comorbidities and postdonation outcomes related to obesity. We discuss tools for better risk stratification, such as imaging-based adiposity assessment and personalized weight management strategies, and note that some risks may remain after weight loss. Barriers such as rigid body mass index cutoffs, limited access to treatment, and lack of long-term follow-up exacerbate disparities in access to donation. We argue for a shift toward individualized, risk-based evaluation supported by multidisciplinary care. We underscore the importance of donor counseling and advocate for a comprehensive, rather than paternalistic approach to living kidney donor selection. Advancing safe and equitable living donation in the context of rising obesity rates will necessitate revised guidelines, improved access to treatment, and an ongoing commitment to donor well-being.

Background We assessed the accuracy of different GFR estimating equations in kidney transplant recipients across diverse racial backgrounds, addressing the previously identified validation gap in multiethnic populations predominantly studied in White cohorts. Methods In this single-center study, eGFR was compared to the measured GFR (mGFR) one year following kidney transplantation. Results The 1-year eGFR and mGFR data from 1145 participants (54% Whites, 23% Hispanics, 9% Blacks, 7% Native Americans, and 6% Asians) revealed varied correlations across racial groups. For Whites, the combined 2021 CKD-EPI creatinine-cystatin C formulas demonstrated a stronger correlation (r = 0.72, [0.65, 0.78]) compared to the 2021 CKD-EPI creatinine and EKFC cystatin C equation. This equation also achieved the highest accuracy (P30: 77.1%). In Black recipients, both the 2009 CKD-EPI (r = 0.56 [0.42, 0.68]) and the 2021 CKD-EPI creatinine (r = 0.56 [0.41, 0.68]) exhibited modest correlations. The 2021 CKD-EPI creatinine-cystatin C equation showed improved correlation (r = 0.63 (0.43, 0.77)] and an accuracy of 62.7% (P30), which was slightly lower than the EKFC cystatin C equation (P30: 64.7%). However, neither the EKFC (rescaled) cystatin C nor the race-free kidney-specific equations outperformed existing eGFR equations for Black participants In Hispanic patients, combined creatinine-cystatin C equations outperformed creatinine-only equations. Among Native Americans, the combined creatinine-cystatin, EKFC (rescaled) cystatin C, and race-free kidney-specific equations achieved an accuracy rate exceeding 85%. In Asians, the CKD-EPI creatinine-cystatin C equation showed the highest correlation, while the race-free kidney-specific equation had the most accuracy. Conclusion Our findings indicate that creatinine-cystatin C combined equations outperformed single-marker formulas across all racial groups, with negligible differences between the 2009 CKD EPI and the race-neutral 2021 CKD-EPI version.

This single-center retrospective study investigated subclinical rejection prevalence and significance in simultaneous pancreas and kidney transplant (SPKT) recipients. We analyzed 352 SPKT recipients from July 2003 to April 2022. Our protocol included pancreas allograft surveillance biopsies at 1, 4, and 12months post-transplant. After excluding 153 patients unable to undergo pancreas biopsy, our study cohort comprised 199 recipients. Among the 199 patients with protocol pancreas biopsies, 107 had multiple protocol pancreas biopsies in the first year, totaling 323. Subclinical rejection was identified in 132 episodes (41%). Of these, 72% were Grade 1, 20% were indeterminate, and 8% were Banff Grade 2 or higher. All episodes of subclinical rejection were treated. Rates of pancreas graft loss (10% vs. 7%) and clinical rejection (21% vs. 20%) at 3 years were similar between those with and without subclinical rejection. Subclinical rejection Banff Grade 2 or more was associated with poor pancreas graft survival HR of 5.5 (95% CI: 1.24-24.37, p = 0.025). Of 236 simultaneous protocol kidney and pancreas biopsies, 102 (43%) showed pancreas subclinical rejection, while only 17% had concurrent kidney subclinical rejection. Our findings suggest limited predictive value of pancreatic enzymes and euglycemia in detecting pancreas rejection. Furthermore, poor concordance existed between pancreas and kidney subclinical rejection.

ABSTRACT Post-transplantation diabetes mellitus (PTDM) remains a leading complication after solid organ transplantation. Previous international PTDM consensus meetings in 2003 and 2013 provided standardized frameworks to reduce heterogeneity in diagnosis, risk stratification and management. However, the last decade has seen significant advancements in our PTDM knowledge complemented by rapidly changing treatment algorithms for management of diabetes in the general population. In view of these developments, and to ensure reduced variation in clinical practice, a 3rd international PTDM Consensus Meeting was planned and held from 6–8 May 2022 in Vienna, Austria involving global delegates with PTDM expertise to update the previous reports. This update includes opinion statements concerning optimal diagnostic tools, recognition of prediabetes (impaired fasting glucose and/or impaired glucose tolerance), new mechanistic insights, immunosuppression modification, evidence-based strategies to prevent PTDM, treatment hierarchy for incorporating novel glucose-lowering agents and suggestions for the future direction of PTDM research to address unmet needs. Due to the paucity of good quality evidence, consensus meeting participants agreed that making GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) recommendations would be flawed. Although kidney-allograft centric, we suggest that these opinion statements can be appraised by the transplantation community for implementation across different solid organ transplant cohorts. Acknowledging the paucity of published literature, this report reflects consensus expert opinion. Attaining evidence is desirable to ensure establishment of optimized care for any solid organ transplant recipient at risk of, or who develops, PTDM as we strive to improve long-term outcomes.

Sex hormones may play an important role in the kidney.1Coggins C.H. Breyer Lewis J. Caggiula A.W. Castaldo L.S. Klahr S. Wang S.R. Differences between women and men with chronic renal disease.Nephrol Dial Transplant. 1998; 13: 1430-1437https://doi.org/10.1093/ndt/13.6.1430Crossref PubMed Scopus (123) Google Scholar,2Neugarten J. Gender and the progression of chronic kidney disease.Mayo Clin Proc. 2020; 95: 2582-2584https://doi.org/10.1016/j.mayocp.2020.10.013Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar Shorter duration of a woman’s reproductive lifespan, or the time from menarche to menopause, has been associated with cardiovascular disease and chronic kidney disease (CKD).3Feng Y. Hong X. Wilker E. et al.Effects of age at menarche, reproductive years, and menopause on metabolic risk factors for cardiovascular diseases.Atherosclerosis. 2008; 196: 590-597https://doi.org/10.1016/j.atherosclerosis.2007.06.016Abstract Full Text Full Text PDF PubMed Scopus (164) Google Scholar,4Kang S.C. Jhee J.H. Joo Y.S. et al.Association of reproductive lifespan duration and chronic kidney disease in postmenopausal women.Mayo Clin Proc. 2020; 95: 2621-2632https://doi.org/10.1016/j.mayocp.2020.02.034Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar We have recently demonstrated that women with bilateral oophorectomy before the natural age of menopause are at an increased risk of CKD.5Kattah A.G. Smith C.Y. Gazzuola Rocca L. Grossardt B.R. Garovic V.D. Rocca W.A. CKD in patients with bilateral oophorectomy.Clin J Am Soc Nephrol. 2018; 13: 1649-1658https://doi.org/10.2215/CJN.03990318Crossref PubMed Scopus (28) Google Scholar Pregnancy also causes significant physiologic changes in kidney function, including hypertrophy, hyperfiltration, and proteinuria, though it is not clear if these changes persist.6Odutayo A. Hladunewich M. Obstetric nephrology: renal hemodynamic and metabolic physiology in normal pregnancy.Clin J Am Soc Nephrol. 2012; 7: 2073-2080https://doi.org/10.2215/CJN.00470112Crossref PubMed Scopus (126) Google Scholar The objective of our study was to determine how past reproductive history can impact kidney function and structure. We sent surveys on reproductive history to women enrolled in the Aging Kidney Anatomy cohort, which has been previously described.7Denic A. Alexander M.P. Kaushik V. et al.Detection and clinical patterns of nephron hypertrophy and nephrosclerosis among apparently healthy adults.Am J Kidney Dis. 2016; 68: 58-67https://doi.org/10.1053/j.ajkd.2015.12.029Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar The cohort includes living kidney donors at the Scottsdale, AZ, and Rochester, MN, sites of the Mayo Clinic, and for this analysis, included donors from January 2000 to December 2017.8Merzkani M.A. Denic A. Narasimhan R. et al.Kidney microstructural features at the time of donation predict long-term risk of chronic kidney disease in living kidney donors.Mayo Clin Proc. 2021; 96: 40-51https://doi.org/10.1016/j.mayocp.2020.08.041Abstract Full Text Full Text PDF PubMed Scopus (12) Google Scholar Generally, all prospective donors undergo a thorough medical evaluation, including iothalamate clearance to measure glomerular filtration rate, 24-hour albumin excretion, computed tomography (CT) angiography, and a biopsy of the donated kidney at the time of surgery. Morphometric analysis of kidney biopsies and calculations of kidney volumes on CT are then performed. Survey questions were grouped into 3 areas: menstrual, pregnancy, and menopausal history (Item S1). Using a retrospective cohort study design, we determined whether past reproductive factors were predictive of kidney function and structure outcomes at donation. Among postmenopausal women at the time of donation, differences in kidney structure were compared by years since menopause, years of reproductive lifespan, and years of endogenous estrogen exposure. Endogenous estrogen exposure was calculated as previously described9de Kleijn M.J. van der Schouw Y.T. Verbeek A.L. Peeters P.H. Banga J.D. van der Graaf Y. Endogenous estrogen exposure and cardiovascular mortality risk in postmenopausal women.Am J Epidemiol. 2002; 155: 339-345https://doi.org/10.1093/aje/155.4.339Crossref PubMed Scopus (172) Google Scholar and was defined as the portion of a woman’s reproductive lifespan with elevated levels of endogenous estrogen, unopposed by progesterone. Analyses were performed using logistic regression for binary outcomes and linear regression for continuous outcomes and were adjusted for age, body mass index (BMI), and hypertension at donation. Differences by past reproductive factors were reported as percent differences. There were 1,870 female kidney donors, and 673 women completed the survey and were included in the cohort (Item S1 and Table S1). Clinical and kidney characteristics at donation and reproductive history results by survey are listed in Table S2. Women with at least 1 predonation pregnancy resulting in a delivery (n = 498) had significantly larger cortex per glomerulus on biopsy in unadjusted analysis and larger total kidney volume and cortical kidney volume on CT after adjusting for age, BMI, and hypertension (Table 1). Time from last pregnancy to donation was not associated with kidney structural findings in adjusted analyses.Table 1Association of At Least 1 Past Pregnancy Resulting in a Delivery (Parous State) With Clinical and Kidney Characteristics at the Time of Donation (N = 673 Donors)Patient Characteristics at DonationaWhen listed, units apply to mean (SD) values only.Mean (SD) or No. (%)UnadjustedAdjustedbAdjusted for age, body mass index, and hypertension.NulliparousParous% Diff. or OR (95% CI)P Value% Diff. or OR (95% CI)P ValueKidney volumes by CT angiogramTotal kidney volume, mm3265,395 (53,508)266,594 (42,371)1.5% (˗2.7% to 5.8%)0.54.3% (0.3% to 8.4%)0.03cValues are statistically significant at P<0.05.Total cortex volume, mm3184,486 (34,119)185,608 (32,549)1.1% (˗3.4% to 5.8%)0.64.5% (0.3% to 8.9%)0.03cValues are statistically significant at P<0.05.Total medulla volume, mm377,164 (18,466)80,319 (18,248)6.3% (0.2% to 12.9%)0.04cValues are statistically significant at P<0.05.6.0% (˗0.4% to 12.7%)0.07Kidney biopsy featuresGlomerular volume, mm30.0023 (0.0009)0.0024 (0.0009)7.6% (˗2.7% to 19.1%)0.27.8% (˗2.8% to 19.6%)0.2Cortex per glomerulus, mm30.061 (0.035)0.066 (0.034)12.3% (0.2% to 25.9%)0.05cValues are statistically significant at P<0.05.12.0% (˗0.5% to 26.0%)0.06Percent GSG3.4 (6.0)4.4 (7.8)18.6% (˗9.4% to 55.3%)0.2-6.1% (˗28.3% to 22.8%)0.6Percent IFTA0.33 (1.95)0.42 (0.77)55.9% (6.2% to 129.9%)0.02cValues are statistically significant at P<0.05.15.2% (˗21.6% to 69.3%)0.5Percent IFTA >0%30 (17.1%)124 (24.9%)1.66 (1.07 to 2.63)0.03cValues are statistically significant at P<0.05.1.29 (0.81 to 2.09)0.3IFTA density, foci per mm2 cortex10.7 (32.2)11.6 (24.4)25.5% (˗2.6% to 61.7%)0.081.8% (˗20.9% to 31.1%)0.9Percent luminal stenosis >50%30 (17.1%)96 (19.3%)1.11 (0.74 to 1.80)0.60.86 (0.53 to 1.42)0.5Kidney function measurementsMeasured GFR, mL/min/1.73 m2109.5 (31.1)100.8 (19.8)˗10.0% (˗14.4% to -5.3%)<0.001cValues are statistically significant at P<0.05.˗3.9% (˗8.6% to 1.0%)0.124 h urine albumin, mg5.7 (6.1)4.8 (22.3)˗10.8% (-36.6% to 25.5%)0.53.3% (˗27.7% to 47.6%)0.9Nephron number2,302,358 (890,383)2,099,534 (825,643)˗6.4% (˗15.6% to 3.8%)0.2˗6.8% (˗16.0% to 3.4%)0.2Single-nephron GFR, µL/min55.5 (24.0)56.9 (21.8)7.1% (˗3.6% to 19.1%)0.26.7% (˗4.1% to 18.6%)0.2Abbreviations: CT, computed tomography; GFR, glomerular filtration rate; GSG, global glomerulosclerosis; IFTA, interstitial fibrosis tubular atrophy; OR, odds ratio.a When listed, units apply to mean (SD) values only.b Adjusted for age, body mass index, and hypertension.c Values are statistically significant at P < 0.05. Open table in a new tab Abbreviations: CT, computed tomography; GFR, glomerular filtration rate; GSG, global glomerulosclerosis; IFTA, interstitial fibrosis tubular atrophy; OR, odds ratio. There were 218 women (32.4%) who were postmenopausal at the time of kidney donation. After adjustment for age, BMI, and hypertension, each year since menopause was associated with increased percentage of interstitial fibrosis and tubular atrophy (%IFTA), each 5-year increase in reproductive lifespan was associated with decreased %IFTA, and each year of endogenous estrogen exposure was associated with decreased %IFTA and decreased IFTA foci density (Table 2, Fig S1). The amount of %IFTA by morphometry in these kidney donors is quite low, though still visible on representative samples (Fig S2).Table 2Age-, BMI-, and Hypertension-Adjusted Association of Reproductive History Factors With Clinical and Kidney Characteristics at the Time of Donation Among Postmenopausal Women (N = 218)Patient Characteristics at DonationPer Year Since MenopausePer 5 Years of Reproductive LifespanPer Year of Endogenous Estrogen ExposureDiff. or OR (95% CI)P ValueDiff. or OR (95% CI)P ValueDiff. or OR (95% CI)P ValueKidney volumes by CT angiogramTotal kidney volume (%)0.0% (˗0.4% to 0.5%)0.90.0% (˗2.0% to 2.0%)0.90.2% (˗0.5% to 0.9%)0.5Total cortex volume (%)0.1% (˗0.4% to 0.6%)0.7˗0.5% (˗2.7% to 1.7%)0.6˗0.2% (˗0.8% to 0.6%)0.8Total medulla volume (%)˗0.2% (˗0.9% to 0.5%)0.61.2% (˗2.0% to 4.4%)0.51.0% (˗0.1% to 2.0%)0.06Kidney biopsy featuresGlomerular volume (%)˗0.6% (˗1.7% to 0.5%)0.33.9% (˗1.3% to 9.4%)0.10.8% (˗0.8% to 2.4%)0.3Cortex per glomerulus (%)0.5% (˗1.0% to 2.0%)0.3˗1.9% (˗8.3% to 5.0%)0.6˗1.6% (˗3.7% to 0.6%)0.1Percent GSG (%)1.8% (˗1.6% to 5.3%)0.3˗2.5% (˗16.7% to 14.2%)0.8˗3.8% (˗8.5% to 1.1%)0.1Percent IFTA (%)5.9% (0.5% to 11.5%)0.03aValues are statistically significant at P<0.05.˗20.2% (˗37.3% to -0.1%)0.05aValues are statistically significant at P<0.05.˗10.1% (˗16.8% to -2.9%)0.008aValues are statistically significant at P<0.05.Percent IFTA >0% (OR)1.05 (1.00 to 1.10)0.05aValues are statistically significant at P<0.05.0.83 (0.66 to 1.03)0.090.91 (0.84 to 0.98)0.01aValues are statistically significant at P<0.05.IFTA density (%)3.1% (˗0.4% to 6.7%)0.08˗11.5% (˗24.5% to 3.7%)0.1˗6.7% (˗11.3% to -1.8%)0.009aValues are statistically significant at P<0.05.Percent luminal stenosis >50% (OR)1.03 (0.98 to 1.09)0.20.81 (0.64 to 1.04)0.090.98 (0.90 to 1.06)0.6Kidney function measurementsMeasured GFR (%)˗0.5% (˗1.1% to 0.1%)0.092.4% (˗0.3% to 5.2%)0.080.5% (˗0.3% to 1.2%)0.224 h urine albumin (%)˗1.6% (˗5.2% to 2.1%)0.41.8% (˗14.3% to 21.0%)0.84.1% (˗1.7% to 10.3%)0.2Nephron number (%)0.2% (˗1.1% to 1.5%)0.8˗2.3% (˗7.8% to 3.6%)0.40.9% (˗0.9% to 2.8%)0.3Single-nephron GFR (%)˗0.5% (˗1.7% to 0.7%)0.43.9% (˗1.8% to 10.1%)0.2˗0.5% (˗2.3% to 1.3%)0.6Abbreviations: BMI, body mass index; CT, computed tomography; GFR, glomerular filtration rate; GSG, global glomerulosclerosis; IFTA, interstitial fibrosis tubular atrophy; OR, odds ratio.a Values are statistically significant at P < 0.05. Open table in a new tab Abbreviations: BMI, body mass index; CT, computed tomography; GFR, glomerular filtration rate; GSG, global glomerulosclerosis; IFTA, interstitial fibrosis tubular atrophy; OR, odds ratio. Our study demonstrates that reproductive history does have a measurable impact on kidney structure. Past pregnancy was associated with larger kidney volumes on imaging, as well as larger cortical volume per glomerulus on kidney biopsy. Nephron hypertrophy is known to occur during pregnancy, but it has not been clear whether this hypertrophy persists after delivery in humans. We observed an increase in cortical volume per glomerulus, but not an increase in glomerular volume, suggesting that an increase in the tubular volume accounts for this observation and does not fully regress after delivery. We did not find any significant impact of pregnancy complications (n = 45 women) on kidney structural findings. This could be due not only to the small sample size but to the requirement of normal kidney function and the lack of certain CKD risk factors to be a donor. We found that among postmenopausal women, those with a longer duration of menopause were more likely to have detectable IFTA on biopsy than women with a shorter duration of menopause, after adjusting for age, BMI, and hypertension. Similarly, those with longer reproductive lifespans and endogenous estrogen exposure were less likely to have detectable IFTA on biopsy. Women generally have a slower progression of CKD than men, as well as a lower risk of developing end-stage kidney disease, possibly owing to estrogen effects.10Ricardo A.C. Yang W. Sha D. et al.Sex-related disparities in CKD progression.J Am Soc Nephrol. 2019; 30: 137-146https://doi.org/10.1681/ASN.2018030296Crossref PubMed Scopus (111) Google Scholar Our study supports the hypothesis that estrogen is nephroprotective and also suggests a role for the duration of estrogen deficiency. Our study has limitations. The survey response rate was modest at 37.5%, and responses may be subject to recall bias. We also evaluated multiple parameters, which increases the potential for a type I error. These are all subclinical findings. However, this study confirms that reproductive factors warrant consideration when evaluating the effects of disease and aging on kidney health. Conceived the study idea: AGK, VDG, ADR; collected the data and performed the analysis: AGK, ADR, AFM, AD; helped interpret results: all authors. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate. This study was supported by a Mayo Clinic Department of Medicine Catalyst Award (Dr Kattah), Mayo CCaTS grant number UL1TR002377, and the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK090358). The funders had no role in study design, data collection, data analysis, interpretation of data, or writing of the report, nor the decision to submit for publication. The authors declare that they have no relevant financial interests. We thank the Mayo Clinic Survey Research Center for their help in survey design, planning, testing, and implementing the survey, as well as data collection. Our data cannot be shared. This patient data contains highly personal information relating to reproductive history. We are open to collaboration with de-identified data and IRB-approved protocols. Received July 4, 2022. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor, an Associate Editor, and the Editor-in-Chief. Accepted in revised form December 27, 2022. This article was corrected online on March 31, 2023 to correct units listed in Table 1. Download .pdf (2.39 MB) Help with pdf files Supplementary File (PDF)Figures S1-S2; Item S1; Tables S1-S2.

The OPTN/UNOS utilizes the calculated estimated posttransplant survival (EPTS) score as the measure of post-kidney transplant survival to guide allocation of deceased donor kidney transplantation. This score does not include any metric of functional capacity. Peak oxygen uptake (VO2peak ), is an established predictor of survival among both the general and diseased populations. We assessed the association and discriminative capacity of VO2peak and that of EPTS score and all-cause mortality post-kidney transplant. Additionally, we assessed the "mortality risk" lower VO2peak conferred on those patients with low EPTS score. Among a cohort of 293 transplant recipients with at least 3-years post-transplant follow-up, the median VO2peak was 15.0 ml/Kg/min. Lower pre-transplant VO2peak and higher EPTS score conferred higher risk of post-transplant mortality. Among the cohort of "low-risk" patients (patients with EPTS score < 50) those with lower VO2peak had significantly higher risk of mortality (log rank p = 0.045). In fact, the mortality risk among those with low-EPTS (< 50) and low VO2peak < 12 ml/Kg/min was equivalent to those with high EPTS (> 80) score. We concluded functional capacity as defined by VO2peak is an important reflection of post-transplant survival. VO2peak is able to identify those with low EPTS who have similar survival to that of high EPTS phenotype.

Background. The objective of this study was to compare the long-term outcomes of older (50–65 y) type 1 diabetics with body mass index <35 kg/m2 and type 2 diabetics with body mass index <30 kg/m2 who received simultaneous pancreas kidney transplantation (SPKT) versus living donor kidney transplants (LDKTs). All subjects had insulin-dependent diabetes. Methods. This is a retrospective single-center study from July 2003 to March 2021 with a median follow-up of 7.5 y. Results. There were 104 recipients in the SPKT and 80 in the LDKT group. The mean age was 56 y in SPKT and 58 y in LDKT. There were 55% male recipients in the SPKT group versus 75% in LDKT. The duration of diabetes was 32 y in SPKT versus 25 y in LDKT. The number of preemptive transplants and length of dialysis were similar. However, the wait time was shorter for LDKT (269 versus 460 d). Forty-nine percent of the LDKT recipients received the organ within 6 mo of being waitlisted compared with 28% of SPKT recipients (P = 0.001). Donor age was lower in the SPKT group (27 versus 41 y). The estimated 5-y death censored kidney survival was 92% versus 98%, and 5-y patient survival was 86% versus 89% for SPKT versus LDKT. Death censored kidney and patient survival, acute kidney rejection by 1 y, and BK viremia were similar between the 2 groups. There were 17 pancreas graft losses within 1 y of transplant, the majority related to surgical complications, and it was not associated with increased mortality. Conclusions. SPKT in selected recipients aged 50 and above can have excellent outcomes similar to LDKT recipients.

IntroductionInpatient hyperglycemia is an established independent risk factor among several patient cohorts for hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission.MethodsData on first-time solitary kidney transplantations were retrieved between September 2015 and December 2018. Information was linked to the electronic health records to determine diagnosis of diabetes mellitus and extract glucometric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on bootstrapped partitions of the data to ensure statistical significance.ResultsThe cohort included 1036 patients who received kidney transplantation; 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average area under the receiver operator curve (AUC) of 78% with (76.1%, 79.9%) 95% confidence interval (CI). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia, the recipient and donor body mass index (BMI) values, presence of delayed graft function, and African American race as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed.ConclusionsSuboptimal glucose metrics during hospitalization after kidney transplantation are associated with an increased risk for 30-day hospital readmission. Optimizing hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Background. Improving both patient and graft survival after kidney transplantation are major unmet needs. The goal of this study was to assess risk factors for specific causes of graft loss to determine to what extent patients who develop either death with a functioning graft (DWFG) or graft failure (GF) have similar baseline risk factors for graft loss. Methods. We retrospectively studied all solitary renal transplants performed between January 1, 2006, and December 31, 2018, at 3 centers and determined the specific causes of DWFG and GF. We examined outcomes in different subgroups using competing risk estimates and cause-specific Cox models. Results. Of the 5752 kidney transplants, graft loss occurred in 21.6% (1244) patients, including 12.0% (691) DWFG and 9.6% (553) GF. DWFG was most commonly due to malignancy (20.0%), infection (19.7%), cardiac disease (12.6%) with risk factors of older age and pretransplant dialysis, and diabetes as the cause of renal failure. For GF, alloimmunity (38.7%), glomerular diseases (18.6%), and tubular injury (13.9%) were the major causes. Competing risk incidence models identified diabetes and older recipients with higher rates of both DWFG and nonalloimmune GF. Conclusions. These data suggest that at baseline, 2 distinct populations can be identified who are at high risk for renal allograft loss: a younger, nondiabetic patient group who develops GF due to alloimmunity and an older, more commonly diabetic population who develops DWFG and GF due to a mixture of causes—many nonalloimmune. Individualized management is needed to improve long-term renal allograft survival in the latter group.

There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions. However, the degree to which such algorithms can affect clinical decision-making is not well understood. Our work attempts to address this problem, investigating the effect of algorithmic predictions on human expert judgment. Leveraging an online survey of medical providers and data from a leading U.S. hospital, we develop a ML algorithm and compare its performance with that of medical experts in the task of predicting 30-day readmissions after solid-organ transplantation. We find that our algorithm is not only more accurate in predicting clinical risk but can also positively influence human judgment. However, its potential impact is mediated by the users’ degree of algorithm aversion and trust. We show that, while our ML algorithm establishes non-linear associations between patient characteristics and the outcome of interest, human experts mostly attribute risk in a linearfashion. To capture potential synergies between human experts and the algorithm, we propose a human-algorithm “centaur” model. We show that it is able to outperform human experts and the best ML algorithm by systematically enhancing algorithmic performance with human-based intuition. Our results suggest that implementing the centaur model could reduce the average patient readmission rate by 26.4%, yielding up to a $770k reduction in annual expenditure at our partner hospital and up to $67 million savings in overall U.S. healthcare expenditures.

BACKGROUND New-onset diabetes after transplantation (NODAT) is a complication of solid organ transplantation. We sought to determine the extent to which NODAT goes undiagnosed over the course of 1 year following transplantation, analyze missed or later-diagnosed cases of NODAT due to poor hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) collection, and to estimate the impact that improved NODAT screening metrics may have on long-term outcomes. MATERIAL AND METHODS This was a retrospective study utilizing 3 datasets from a single center on kidney, liver, and heart transplantation patients. Retrospective analysis was supplemented with an imputation procedure to account for missing data and project outcomes under perfect information. In addition, the data were used to inform a simulation model used to estimate life expectancy and cost-effectiveness of a hypothetical intervention. RESULTS Estimates of NODAT incidence increased from 27% to 31% in kidney transplantation patients, from 31% to 40% in liver transplantation patients, and from 45% to 67% in heart transplantation patients, when HbA1c and FBG were assumed to be collected perfectly at all points. Perfect screening for kidney transplantation patients was cost-saving, while perfect screening for liver and heart transplantation patients was cost-effective at a willingness-to-pay threshold of $100 000 per life-year. CONCLUSIONS Improved collection of HbA1c and FBG is a cost-effective method for detecting many additional cases of NODAT within the first year alone. Additional research into both improved glucometric monitoring as well as effective strategies for mitigating NODAT risk will become increasingly important to improve health in this population.

BackgroundData regarding changes in markers of cardiovascular and all‐cause mortality, namely, VO2peak and minute ventilation/carbon dioxide production (VE/VCO2) slope following renal transplantation past 1‐year post‐transplant are lacking. We examined changes in VO2peak and VE/VCO2 before and after ~3‐years after renal transplantation.MethodsEighteen patients (age 56.2 ± 2.5 years; 82.9 ± 4.9 kg; 9 male) completed an exercise test to exhaustion using a ramp treadmill protocol. Subjects underwent pre‐transplant exercise testing (PreTx) following which they were placed on the transplant wait‐list. Average time on the waitlist was 15.6 ± 2 months. Following transplant, subjects underwent follow‐up maximal exercise testing using the same protocol (PostTx). Mean duration to post‐transplant exercise testing was 35 ± 2 months. Gas exchange data, heart rate and respiratory exchange ratios were recorded throughout exercise testing. All variables are reported as mean ± standard error. α was set at 0.05 and a paired t‐test carried out to examine changes in VO2peak and maximal VE/VCO2 slope before and after transplant.ResultsSignificant reductions in relative VO2peak were observed from PreTx = 15.2 ± 0.8 mL/kg/min to PostTX = 12.2 ± 0.4 mL/kg/min (p = 0.002 ). Also, significant increases in Ve/VCO2 were observed from PreTx = 30 ± 1.4 to PostTx = 43 ± 1.6 (p < .001).ConclusionsLong term follow‐up (~3‐years) in this small cohort of patients following renal transplantation demonstrated significant reductions in VO2peak (20%) and worsening of the VE/VCO2 slope (30%) which are modifiable markers of mortality risk. This underscores the need for larger cohort studies to be undertaken in this population given that these markers are independently linked to cardiovascular and all‐cause mortality risk. These data may have implications for cardiovascular stratification and cardiovascular risk reduction in this population.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Background. The study aims is to use the fragility index (FI) to examine the strength of evidence of randomized controlled trials (RCTs) published in the last decade on kidney transplantation. Methods. We searched MEDLINE for studies on kidney transplantation. We included the RCTs that compared 2 groups with 1:1 randomization and reported significant P values (<0.05) for a dichotomous outcome and were published in the top 10 transplant journals. We calculated the FI; a calculation used to determine the minimum number of subjects needed to change from a nonevent to an event to make the study results nonsignificant (P >= 0.05). Results. Fifty-seven RCTs met our inclusion criteria. The median sample size was 100 participants in each arm, the median number of events was 16 (interquartile range, 8-30) in the intervention group. Among the included trials, 79% were industry-funded, 93% involved medications, and the majority were open label. The median FI was 3 (interquartile range, 1-11). In 43% of the trials, the number of patients reported lost to follow-up was higher than or equal to the FI. Only 4% of the RCTs imputed a value for the missing dichotomous outcome. Furthermore, the median number of subjects who discontinued the trial because of adverse effects was 21, which was greater than the FI in 60% of the RCTs. Conclusions. The arbitrary classification of results into "significant" and "nonsignificant" based on P value <0.05 should perhaps be interpreted with the help of other statistical parameters and FI is one of them.

Objective: To determine whether microstructural features on a kidney biopsy specimen obtained during kidney transplant surgery predict long-term risk of chronic kidney disease in the donor. Patients and Methods: We studied kidney donors from May 1, 1999, through December 31, 2018, with a follow-up survey for the results of recent blood pressure and kidney function tests (estimated glomerular filtration rate [eGFR] and proteinuria). If not recently available, blood pressure and eGFRs were requested from a local clinic. Microstructural features on kidney biopsy at the time of donation were assessed as predictors of hypertension and kidney function after adjusting for years of follow-up, baseline age, sex, and clinical predictors. Results: There were 807 donors surveyed a mean 10.5 years after donation. An eGFR less than 45 mL/min/1.73 m(2) in 6.4% (43/673) of donors was predicted by larger glomerular volume per standard deviation (odds ratio [OR], 1.48; 95% CI, 1.08 to 2.04) and nephron number below the age-specific 5th percentile (OR, 3.38; 95% CI, 1.31 to 8.72). An eGFR less than 60 mL/min/1.73 m(2) in 42.5% (286/673) of donors was not predicted by any microstructural feature. Residual eGFR (postdonation/predonation eGFR) was predicted by nephron number below the age-specific 5th percentile (difference, -6.07%; 95% CI, -10.24% to -1.89%). Self-reported proteinuria in 5.1% (40/786) of donors was predicted by larger glomerular volume (OR, 1.42; 95% CI, 1.08 to 1.86). Incident hypertension in 18.8% (119/633) of donors was not predicted by any microstructural features. Conclusion: Low nephron number for age and larger glomeruli are important microstructural predictors for long-term risk of chronic kidney disease after living kidney donation. (C) 2020 Mayo Foundation for Medical Education and Research

BACKGROUND:Participant withdrawal from clinical trials occurs for various reasons, predominantly adverse effects or intervention inefficacy. Because these missing participant data can have implications for the validity, reproducibility, and generalizability of study results, when conducting a systematic review, it is important to collect and appropriately analyze missing data information to assess its effects on the robustness of the study results.METHODS:In this methodologic survey of missing participant data reporting and handling in systematic reviews, we included meta-analyses that provided pooled estimates of at least 1 dichotomous intervention outcome of a randomized controlled trial performed in adult kidney transplant subjects.RESULTS:Eighty-three systematic reviews (17 Cochrane and 66 non-Cochrane reviews) met the inclusion criteria. The most common intervention was drugs (80%), with the majority involving immunosuppressant drugs 55% (n = 46), followed by surgery in 14% (n = 12). The median follow-up duration was 12 months (maximum, 240 mo). Intention-to-treat or modified intention-to-treat analysis was reported in 24% (n = 20) of the reviews (76% of Cochrane and 10% of non-Cochrane). Overall, the majority of systematic reviews did not quantify (90% [n = 60] non-Cochrane and 29% [n = 5] Cochrane) or include the reasons for missing participant data (88% [n = 58] non-Cochrane and 24% [n = 4] Cochrane). Eleven percent (n = 9) handled missing participant data, 5% (n = 4) justified the analytical method(s) used to handle it, and 2% (n = 2) performed a sensitivity analysis for it.CONCLUSIONS:Systematic reviews of kidney transplantation provide inadequate information on missing participant data and usually do not handle or discuss the associated risk of bias with it.

BACKGROUNDNephrosclerosis, nephron size, and nephron number vary among kidneys transplanted from living donors. However, whether these structural features predict kidney transplant recipient outcomes is unclear.METHODSOur study used computed tomography (CT) and implantation biopsy to investigate donated kidney features as predictors of death-censored graft failure at three transplant centers participating in the Aging Kidney Anatomy study. We used global glomerulosclerosis, interstitial fibrosis/tubular atrophy, artery luminal stenosis, and arteriolar hyalinosis to measure nephrosclerosis; mean glomerular volume, cortex volume per glomerulus, and mean cross-sectional tubular area to measure nephron size; and calculations from CT cortical volume and glomerular density on biopsy to assess nephron number. We also determined the death-censored risk of graft failure with each structural feature after adjusting for the predictive clinical characteristics of donor and recipient.RESULTSThe analysis involved 2293 donor-recipient pairs. Mean recipient follow-up was 6.3 years, during which 287 death-censored graft failures and 424 deaths occurred. Factors that predicted death-censored graft failure independent of both donor and recipient clinical characteristics included interstitial fibrosis/tubular atrophy, larger cortical nephron size (but not nephron number), and smaller medullary volume. In a subset with 12 biopsy section slides, arteriolar hyalinosis also predicted death-censored graft failure.CONCLUSIONSSubclinical nephrosclerosis, larger cortical nephron size, and smaller medullary volume in healthy donors modestly predict death-censored graft failure in the recipient, independent of donor or recipient clinical characteristics. These findings provide insights into a graft's "intrinsic quality" at the time of donation, and further support the use of intraoperative biopsies to identify kidney grafts that are at higher risk for failure.

Problem definition: Organ-transplanted patients typically receive high amounts of immunosuppressive drugs (e.g., tacrolimus) as a mechanism to reduce their risk of organ rejection. However, because of the diabetogenic effect of these drugs, this practice exposes them to a greater risk of new-onset diabetes after transplantation (NODAT), and hence, becoming insulin dependent. We study and develop effective medication management strategies to address the common conundrum of balancing the risk of organ rejection versus that of NODAT. Academic/practical relevance: Our research contributes to the healthcare operations management literature by developing a robust stochastic decision-making framework that allows for incorporating (1) false-positive and false-negative errors of medical tests, (2) inevitable estimation errors when data sets are used, (3) variability among physician' attitudes toward ambiguous outcomes, and (4) dynamic and patient risk-profile-dependent progression of health conditions. Methodology: We apply an ambiguous partially observable Markov decision process (APOMDP) approach where dynamic optimization with respect to a "cloud" of possible models allows us to make decisions that are robust to potential misspecifications of risks. Results: We first provide various structural results that facilitate characterizing the optimal medication policies. Utilizing a clinical data set, we then compare the performance of the optimal medication policies obtained from our APOMDP model with the policies currently used in the medical practice. We observe that, in one year after transplant, our proposed policies can improve the life expectancy of each patient up to 4.58%, while reducing the medical expenditures up to 11.57%. Managerial implications: Balancing the risks of organ rejection and diabetes complications and considering factors such as physicians' attitudes toward ambiguous outcomes, partial observability of medical tests, and patient-specific risk factors are shown to result in more cost-effective strategies for management of post-transplant medications compared with the current medical practice. Finally, simultaneous management of medications can facilitate the care coordination process between transplantation/nephrology and endocrinology departments of a hospital that are typically in charge of administering such medications.

BackgroundMost prior studies characterizing post-transplantation diabetes mellitus (PTDM) have been limited to single-cohort, single-organ studies. This retrospective study determined PTDM across organs by comparing incidence and risk factors among 346 liver and 407 kidney transplant recipients from a single center.MethodsUnivariate and multivariate regression-based analyses were conducted to determine association of various risk factors and PTDM in the two cohorts, as well as differences in glucometrics and insulin use across time points.ResultsThere was a higher incidence of PTDM among liver versus kidney transplant recipients (30% vs. 19%) at 1-year post-transplant. Liver transplant recipients demonstrated a 337% higher odds association to PTDM (OR 3.37, 95% CI (1.38-8.25), p<0.01). 1-month FBG was higher in kidney patients (135 mg/dL vs 104 mg/dL; p < .01), while 1-month insulin use was higher in liver patients (61% vs 27%, p < .01). Age, BMI, insulin use, and inpatient FBG were also significantly associated with differential PTDM risk.ConclusionsKidney and liver transplant patients have different PTDM risk profiles, both in terms of absolute PTDM risk as well as time course of risk. Management of this population should better reflect risk heterogeneity to short-term need for insulin therapy and potentially long-term outcomes.