Schinstock, Carrie A.

ABSTRACTCellular senescence is a biological mechanism of aging and age‐related diseases. The aim of this study was to examine whether senescence biomarkers are associated with frailty and physical function trajectories in patients undergoing kidney transplantation (KT). We also discussed the relationship between senescence biomarkers and KT function. In this multicenter study, we prospectively assessed plasma levels of senescence biomarkers, frailty as measured by the Physical Frailty Phenotype, and physical function as measured by the Short Physical Performance Battery prior to KT. Frailty, physical function, and KT function were also measured 1 year after KT. Variable associations were assessed using Cox and relaxed least absolute shrinkage and selection operation regression. The cohort consisted of 197 participants (mean age 53 ± 13 years, 61.4% male, and 80.2% White race). Higher pre‐KT levels of macrophage‐derived chemokine (MDC/CCL22) and growth differentiation factor‐15 (GDF‐15) were independently associated with less improvement in frailty and/or physical function during the first year after KT. Higher pre‐KT levels tumor necrosis factor receptor superfamily member 6 (FAS) and MMP‐9 levels were independently associated with lower KT function one year after KT. Pre‐KT cellular senescence may contribute to frailty, physical function, and kidney function trajectories during the first year after KT.

ABSTRACTBackgroundAdenovirus (ADV) infection can lead to significant morbidity and mortality in immunocompromised patients, particularly in those with hematopoietic stem cells or solid organ transplants. The incidence of ADV infection in kidney transplant (KT) is not well‐defined as ADV is often asymptomatic and not routinely checked.MethodsThis retrospective case‐series study included KT and simultaneous pancreas‐KT (SPKT) recipients from January 1, 2008, to January 31, 2024, across three Mayo Clinic sites (Arizona, Florida, and Minnesota) with symptomatic adenovirus polymerase chain reaction cases. The primary outcomes were allograft function at various intervals post‐ADV infection, allograft, and patient survival.ResultsWe report one of the largest multi‐site case series regarding outcomes of ADV in KT with 17 patients. The median time to ADV infection was 30 weeks (5–74). Five patients (29%) developed disseminated infection. Nine patients (53%) of the entire cohort experienced graft loss within a median of 35 (4–168) weeks, with four (44%) of graft loss attributed to ADV. Nine patients (53%) developed rejections post‐ADV infection with a median of 4 (2–8) weeks after resolution. One patient died from acute hypoxic respiratory failure from ADV infection.ConclusionADV should be considered in KT/SPKT patients with renal dysfunction, hematuria, and with or without fever. Despite the low mortality rate, there is a significant risk of graft loss and rejection after ADV infection. It is crucial to screen for ADV and develop intervention strategies for treatment. Further multicenter studies are needed to better define, stage, and manage ADV infection. image

ABSTRACTPerformance‐based measures of frailty are associated with healthcare utilization after kidney transplantation (KT) but require in‐person assessment. A promising alternative is self‐reported frailty. The goal of this study was to examine the ability of performance‐based and self‐reported frailty measures to predict 30‐day rehospitalizations after KT. We conducted a prospective, observational cohort study involving 272 adults undergoing KT at Mayo Clinic in Minnesota, Florida, or Arizona. We simultaneously measured frailty before KT using the physical frailty phenotype (PFP), the short physical performance battery (SPPB), and self‐report (the Patient‐Reported Outcomes Measurement Information System [PROMIS] 4‐item physical function short form v2.0). Both the PFP and self‐reported frailty were independently associated with more than a 2‐fold greater odds of 30‐day rehospitalizations, while the SPPB was not. To our knowledge, this is the first study to assess the prognostic value of all three of the above frailty measures in patients undergoing KT. The PFP is more prognostic than the SPPB when assessing the risk of 30‐day rehospitalizations; self‐reported frailty can complement the PFP but not replace it. However, the 4‐item survey assessing self‐reported frailty represents a simple way to identify patients undergoing KT surgery who would benefit from interventions to lower the risk of rehospitalizations.

Background. Microvascular inflammation (MVI) is a key feature of antibody-mediated rejection (AMR) among patients with HLA donor-specific antibody (DSA), but MVI at AMR thresholds (Banff glomerulitis [g] + peritubular capillaritis [ptc] score ≥ 2) without DSA has been increasingly recognized. We aimed to determine the incidence of MVI among highly sensitized kidney transplant recipients without DSA. Methods. We performed a single-center, retrospective, matched cohort study comparing outcomes of kidney transplant recipients with cPRA ≥90% with preexisting DSA (n = 49), cPRA ≥90% without preexisting DSA (n = 47), and matched controls with cPRA = 0 without preexisting DSA (n = 49). Controls were matched by age, donor type, and transplant date. Indication and surveillance biopsies combined with annual de novo DSA screening were obtained. Results. Kidney transplant recipients with a cPRA ≥90% and no evidence of preexisting or de novo DSA had a higher incidence of MVI (glomerulitis + peritubular capillaritis ≥ 2) than patients with cPRA = 0 [35% (17/49) versus 12% (6/49), P = 0.0003] over a median (interquartile range) follow-up of 5 (4–6) y posttransplant. Among this cPRA ≥90% group without DSA, MVI persisted in 54% of cases on follow-up biopsy (7/13), and 24% (4/13) of cases developed transplant glomerulopathy (Banff cg score > 0). Conclusions. Highly sensitized transplant recipients have a high incidence of persistent and progressive MVI, even without DSA. The mechanisms underlying these histologic features needs to be elucidated, but this information is important to consider when making decisions about transplantation among highly sensitized individuals.

Background. Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. Methods. We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms’ outputs based on a single preoperative ECG were correlated with patient mortality data. Results. Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00–9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). Conclusions. The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

AbstractPulmonary hypertension (PH) associated with chronic kidney disease (CKD) (PH‐CKD) affects approximately 20%–40% of CKD patients and is associated with increased morbidity and mortality. PH and CKD are both pathophysiologically associated with nitric oxide (NO) deficiency. The NO pathway, an important therapeutic domain in pulmonary arterial hypertension (PAH), is an intriguing but unexplored target in PH‐CKD. We sought to improve understanding of the clinical significance of the NO pathway in patients with PH‐CKD by assessing the hemodynamic response to inhaled NO (iNO) during right heart catheterization (RHC). In this retrospective cohort study, patients with diagnosis codes of PH and stage IV/V CKD or end‐stage renal disease and estimated glomerular filtration rate < 60 mL/min/body surface area who underwent RHC and hemodynamic drug study between July 2011 and June 2021 were eligible. Patients with mean pulmonary artery pressure (mPAP) > 20 mmHg and pulmonary vascular resistance (PVR) > 3 Wood units were included. The final cohort included 37 patients (45.9% female, mean age 72.5 ± 9.7 years). A total of 56.7% of the cohort (21/37) had precapillary PH, while 43.2% (16/37) had combined precapillary postcapillary PH (Cpc‐PH). Median survival was 3.1 years after RHC. iNO was associated with a significant decrease in both mPAP and PVR. Hemodynamic changes in mPAP and PVR were similar in precapillary and Cpc‐PH groups. Among a small subset (n = 14) who were subsequently treated with PAH‐targeted therapy, treatment response was mixed and did not reveal significant benefit. Further studies are warranted to better define the potential role of PAH therapy in PH‐CKD.

Despite significant advancements in immunosuppressive therapies, kidney transplant rejection continues to pose a substantial challenge, impacting the long-term survival of grafts. This article provides an overview of the diagnosis, current therapies, and management strategies for acute T-cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR). TCMR is diagnosed through histological examination of kidney biopsy samples, which reveal the infiltration of mononuclear cells into the allograft tissue. Corticosteroids serve as the primary treatment for TCMR, while severe or steroid-resistant cases may require T-cell-depleting agents, like Thymoglobulin. ABMR occurs due to the binding of antibodies to graft endothelial cells. The most common treatment for ABMR is plasmapheresis, although its efficacy is still a subject of debate. Other current therapies, such as intravenous immunoglobulins, anti-CD20 antibodies, complement inhibitors, and proteasome inhibitors, are also utilized to varying degrees, but their efficacy remains questionable. Management decisions for ABMR depend on the timing of the rejection episode and the presence of chronic changes. In managing both TCMR and ABMR, it is crucial to optimize immunosuppression and address adherence. However, further research is needed to explore newer therapeutics and evaluate their efficacy.

Improving long-term allograft survival and minimizing recipient morbidity is of key importance in all of transplantation. Improved matching of classical HLA molecules and avoiding HLA donor-specific antibody has been a major focus; however, emerging data suggest the relevance of nonclassical HLA molecules, major histocompatibility complex class I chain-related gene A (MICA) and B, in transplant outcomes. The purpose of this review is to discuss the structure, function, polymorphisms, and genetics of the MICA molecule and relates this to clinical outcomes in solid organ and hematopoietic stem cell transplantation. The tools available for genotyping and antibody detection will be reviewed combined with a discussion of their shortcomings. Although data supporting the relevance of MICA molecules have accumulated, key knowledge gaps exist and should be addressed before widespread implementation of MICA testing for recipients pre- or posttransplantation.

Background. Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. Methods. We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. Results. Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). Conclusions. ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.