Smith, Byron H.

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.

Key Points Morphometry allows for a more prognostic multidimensional quantification of interstitial fibrosis and tubular atrophy (IFTA) in kidneys than does visual inspection. The density of IFTA foci is determined by dividing the number of contiguous IFTA patches in the kidney cortex by the area of cortex. Higher density of IFTA foci significantly predicted renal allograft failure beyond %IFTA and other biopsy and clinical characteristics. Background Contraction of interstitial fibrosis and tubular atrophy (IFTA) may cause %IFTA to under-represent the severity of nephron loss. Higher density of IFTA foci is an important predictor of progressive CKD in native kidneys independent of %IFTA. Methods We studied kidney transplant recipients transplanted between 2000 and 2013 who had a 5-year surveillance kidney biopsy and subsequent follow-up. Banff ci score (interstitial fibrosis) was obtained from the pathology reports. After digitizing the biopsies, we traced cortex area and each distinct IFTA focus on a single trichrome-stained section. Percent IFTA area and IFTA foci density (count of IFTA foci/cortex area) were calculated. Cox models assessed the risk of death-censored graft failure after the 5-year biopsy with Banff ci score, morphometric %IFTA, and IFTA foci density. Results There were 58 death-censored allograft failures among 835 kidney recipients during the 5 years of follow-up. Biopsies from grafts that failed had higher mean Banff ci score (1.5 versus 0.7, P < 0.0001), %IFTA (22.6% versus 7.0%, P < 0.0001), and IFTA foci density (1.3/mm 2 versus 0.4/mm 2 , P < 0.0001). After adjusting for other Banff scores or clinical variables, Banff ci did not correlate with allograft failure, but both higher %IFTA (hazard ratio = 1.56, P < 0.0001) and higher IFTA foci density (hazard ratio = 2.34, P < 0.0001) did. All but four allograft failures by 10 years had biopsies in the top quartile of either %IFTA or IFTA foci density at 5 years. A model using just these two morphometric measures without clinical characteristics resulted in a c-statistic of 0.891 with respect to allograft failure. Conclusions Morphometric characterization of IFTA foci density is a strong predictor of death-censored allograft failure not captured in current Banff classification for grading of kidney fibrosis.

Objectives: Determine whether the timing of transplantation affects patient mortality. Background: Neoadjuvant therapy and liver transplantation have emerged as an excellent treatment option for select patients with perihilar cholangiocarcinoma (pCCA). However, the optimal timing of transplantation is not known. Methods: We reviewed all patients registered for a standardized pCCA protocol between 1996 and 2020 at our center. After adjusting for confounders, we examined the association of waiting time with patient mortality in an intention-to-treat cohort (n=392) and those who received a liver transplant (n=256). Results: The median (interquartile range) time from registration to transplant or dropout was 5.74 (3.25–7.06) months. Compared with a short wait time (0–3 mo), longer waiting times did not affect all-cause mortality: (3–6 mo) hazard ratio (HR) 0.98; 95% CI: 0.52–1.84; (6–9 mo) HR: 0.80; 95% CI: 0.39–1.65; (9–12 mo) HR: 0.56; 95% CI: 0.26–1.22. Subgroups with a shorter waiting time had similar survival to those with long waiting times: living donor available HR: 0.97; 95% CI: 0.67–1.42; AB or B blood group HR: 0.93; 95% CI: 0.62–1.39. Longer waiting times were associated with decreased all-cause mortality after transplantation (HR: 0.92; 95% CI: 0.87–0.97). This benefit began after a 6-month waiting time minimum (HR: 0.53; 95% CI: 0.26–1.10) and increased further after 9 months (HR: 0.43 95% CI: 0.20–0.93). Waiting time was not associated with residual adenocarcinoma in the explant (odds ratio 0.99; 95% CI: 0.98–1.00). Conclusions: A waiting time of at least 6 months will optimize results with transplantation without affecting overall (intention-to-treat) patient survival.

Background. Chronic systemic inflammation is associated with mortality in patients with chronic kidney disease, cardiovascular disease, and diabetes. The goal of this study was to examine the relationship between pretransplant inflammatory biomarkers (growth differentiation factor-15 [GDF-15], interleukin-6 [IL-6], soluble tumor necrosis factor receptor-1, monokine induced by gamma interferon/chemokine [C-X-C motif] ligand 9 [MIG/CXCL9], monocyte chemoattractant protein-1, soluble FAS, tumor necrosis factor-α, interleukin-15, and interleukin-1β) and death with function (DWF) after kidney transplantation (KT). Methods. We retrospectively measured inflammatory biomarker levels in serum collected up to 1 y before KT (time from blood draw to KT was 130 ± 110 d) in recipients transplanted between January 2006 and December 2018. Kaplan-Meier estimation, Cox regression, and Gradient Boosting Machine modeling were used to examine the relationship between inflammatory biomarkers and DWF. Results. Our cohort consisted of 1595 KT recipients, of whom 62.9% were male and 83.2% were non-Hispanic White. Over a mean follow-up of 7.4 ± 3.9 y, 21.2% of patients (n = 338) experienced DWF. Patients with the highest quartile levels of GDF-15 (>4766 pg/mL), IL-6 (>6.11 pg/mL), and MIG/CXCL9 (> 5835 pg/mL) had increased rates of DWF, and each predicted mortality independently of the others. When adjusted for clinical factors (age, diabetes, etc), the highest quartile levels of GDF-15 and IL-6 remained independently associated with DWF. Adding inflammatory markers to a clinical Cox model improved the C-statistic for DWF from 0.727 to 0.762 using a Gradient Boosting Machine modeling approach. Conclusions. These findings suggest that pre-KT serum concentrations of GDF-15, IL-6, and MIG/CXCL9 may help to risk stratify and manage patients undergoing KT and suggests that chronic inflammation may play a role in mortality in KT recipients.

Background. Mesangial expansion (ME) is an understudied histologic lesion in renal allografts. The current Banff mm score is not reproducible and may miss important ME features. The study aimed to improve the quantification of ME using morphometry, assess changes over time, and determine its association with allograft loss. Methods. We studied ME in 1-y and 5-y surveillance biopsies in 835 kidney transplants performed between January 2000 and December 2013. ME was assessed using the Banff mm score by a central pathologist and by morphometry. We derived 3 different morphometric measures: (1) %ME mm (%glomeruli with ME in ≥2 lobules, like Banff mm); (2) %MEany (%glomeruli with any ME lesion); and (3) %ME area (sum of all ME areas/all glomerular tuft areas). Unadjusted and adjusted Cox models assessed the risk of death-censored allograft loss. Results. From 1- to 5-y biopsies, the mean Banff mm score increased from 0.18 to 0.34, whereas %ME mm increased from 2.5% to 13.3%. Banff mm score had modest correlations with morphometric ME measures. Moderate-severe %ME mm was present in 20.1% of 5-y biopsies, whereas only 6.6% of Banff mm scores were. In general, higher ME on both 1- and 5-y biopsies was associated with a deceased donor, older recipient age, recipient diabetes/obesity (present in >50% of severely affected biopsies), higher hemoglobin A1c at 5 y posttransplant, and recurrent kidney disease. Higher ME on 5-y biopsies was associated with delayed graft function. A higher Banff mm score at 1-y biopsy and morphometry ME measures at 5-y biopsy were associated with rejection during the first year posttransplant. Morphometric ME measures were associated with allograft loss independent of Banff scores and all clinical characteristics, including kidney function and recurrent disease. The model with %MEany had the highest c-statistic (0.872). Conclusions. Banff mm score underestimates the pervasiveness of ME in 5-y biopsies. ME is common and associated with alloimmune and nonalloimmune causes of graft loss.

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.