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Performance‐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. [ABSTRACT FROM AUTHOR]

Aims: Graft dysfunction (GD) after heart transplantation (HTx) can develop without evidence of cell‐ or antibody‐mediated rejection. Cardiac magnetic resonance imaging (CMR) has an evolving role in detecting rejection; however, its role in biopsy‐negative GD has not been described. This study examines CMR findings, evaluates outcomes based on CMR results, and seeks to identify the possibility of rejection missed through endomyocardial biopsy by using CMR in HTx recipients with biopsy‐negative GD. Methods and results: HTx recipients with GD [defined as a decrease in left ventricular ejection fraction (LVEF) by >5% and LVEF < 50%] in the absence of rejection by biopsy or allograft vasculopathy and who underwent CMR were included in the study. The primary outcome was a composite of all‐cause mortality, re‐transplantation, or persistent LVEF < 50%. Overall, 34 HTx recipients developed biopsy‐negative GD and underwent CMR. Left ventricular late gadolinium enhancement (LGE) on CMR was observed in 16 patients with two distinct patterns: diffuse epicardial (n = 13) and patchy (n = 3) patterns. Patients with LGE developed GD later after HTx [4 (1.4–6.8) vs. 0.8 (0.3–1.2) years, P < 0.001], were more often symptomatic (88% vs. 56%, P = 0.06), and had greater haemodynamic derangement (pulmonary capillary wedge pressure: 19 ± 7 vs. 13 ± 3 mmHg, P = 0.002) as compared with those without LGE. No significant difference was observed in the primary composite outcome between patients with LGE and those without LGE (50% vs. 38% of patients with events, P = 0.515). During a median follow‐up of 3.8 years, mean LVEF improved similarly in the LGE‐negative (37–55%) and LGE‐positive groups (32–55%) (P = 0.16). Conclusions: Biopsy‐negative GD occurs with and without LGE when assessed by CMR, indicative of possible rejection/inflammation occurring only in a subset of patients. Irrespective of LGE, LVEF improvement occurs in most GD patients, suggesting that other neurohormonal or immunomodulatory mechanisms may also contribute to GD development. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no funding or conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Determine if timing of transplantation affects patient mortality., Background: Neoadjuvant therapy and liver transplantation has 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 - 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 drop out was 5.74 (3.25-7.06) months. Compared to a short wait time (0-3 months), longer waiting times did not affect all-cause mortality: (3-6 months) hazard ratio (HR) 0.98; 95% CI 0.52-1.84; (6-9 months) HR 0.80; 95% CI 0.39-1.65; (9-12 months) 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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)