Faculty Bibliography

BACKGROUND:Physical frailty phenotype is characterized by decreased physiologic reserve to stressors and associated with poor outcomes, such as delirium and mortality, that may result from post-kidney transplant (KT) inflammation. Despite a hypothesized underlying pro-inflammatory state, conventional measures of frailty typically do not incorporate inflammatory biomarkers directly. Among KT candidates and recipients, we evaluated the inclusion of inflammatory biomarkers with traditional physical frailty phenotype components. METHODS:Among 1154 KT candidates and recipients with measures of physical frailty phenotype and inflammation (interleukin 6 [IL6], tumor necrosis factor alpha [TNFα], C-reactive protein [CRP]) at 2 transplant centers (2009-2017), we evaluated construct validity of inflammatory-frailty using latent class analysis. Inflammatory-frailty measures combined 5 physical frailty phenotype components plus the addition of an individual inflammatory biomarkers, separately (highest tertiles) as a sixth component. We then used Kaplan-Meier methods and adjusted Cox proportional hazards to assess post-KT mortality risk by inflammatory-frailty (n = 378); Harrell's C-statistics assessed risk prediction (discrimination). RESULTS:Based on fit criteria, a 2-class solution (frail vs nonfrail) for inflammatory-frailty was the best-fitting model. Five-year survival (frail vs nonfrail) was: 81% versus 93% (IL6-frailty), 87% versus 89% (CRP-frailty), and 83% versus 91% (TNFα-frailty). Mortality was 2.07-fold higher for IL6-frail recipients (95% CI: 1.03-4.19, p = .04); there were no associations between the mortality and the other inflammatory-frailty indices (TNFα-frail: 1.88, 95% CI: 0.95-3.74, p = .07; CRP-frail: 1.02, 95% CI: 0.52-2.03, p = .95). However, none of the frailty-inflammatory indices (all C-statistics = 0.71) improved post-KT mortality risk prediction over the physical frailty phenotype (C-statistics = 0.70). CONCLUSIONS:Measurement of IL6-frailty at transplantation can inform which patients should be targeted for pre-KT interventions. However, the traditional physical frailty phenotype is sufficient for post-KT mortality risk prediction.

Infections remain a major threat to successful kidney transplantation (KT). To characterize the landscape and impact of post-KT infections in the modern era, we used United States Renal Data System (USRDS) data linked to the Scientific Registry of Transplant Recipients (SRTR) to study 141 661 Medicare-primary kidney transplant recipients from January 1, 1999 to December 31, 2014. Infection diagnoses were ascertained by International Classification of Diseases, Ninth Revision (ICD-9) codes. The cumulative incidence of a post-KT infection was 36.9% at 3 months, 53.7% at 1 year, and 78.0% at 5 years. The most common infections were urinary tract infection (UTI; 46.8%) and pneumonia (28.2%). Five-year mortality for kidney transplant recipients who developed an infection was 24.9% vs 7.9% for those who did not, and 5-year death-censored graft failure (DCGF) was 20.6% vs 10.1% (P < .001). This translated to a 2.22-fold higher mortality risk (adjusted hazard ratio [aHR]: _2.15 2.22_2.29 , P < .001) and 1.92-fold higher DCGF risk (aHR: _1.84 1.91_1.98 , P < .001) for kidney transplant recipients who developed an infection, although the magnitude of this higher risk varied across infection types (for example, 3.11-fold higher mortality risk for sepsis vs 1.62-fold for a UTI). Post-KT infections are common and substantially impact mortality and DCGF, even in the modern era. Kidney transplant recipients at high risk for infections might benefit from enhanced surveillance or follow-up to mitigate these risks.

BACKGROUND AND AIMS:Frailty, as measured by the Liver Frailty Index (LFI), is associated with liver transplant (LT) waitlist mortality. We sought to identify an optimal LFI cutoff that predicts waitlist mortality. APPROACH AND RESULTS:Adults with cirrhosis awaiting LT without hepatocellular carcinoma at nine LT centers in the United States with LFI assessments were included. Multivariable competing risk analysis assessed the relationship between LFI and waitlist mortality. We identified a single LFI cutoff by evaluating the fit of the competing risk models, searching for the cutoff that gave the best model fit (as judged by the pseudo-log-likelihood). We ascertained the area under the curve (AUC) in an analysis of waitlist mortality to find optimal cutoffs at 3, 6, or 12 months. We used the AUC to compare the discriminative ability of LFI+Model for End Stage Liver Disease-sodium (MELDNa) versus MELDNa alone in 3-month waitlist mortality prediction. Of 1,405 patients, 37 (3%), 82 (6%), and 135 (10%) experienced waitlist mortality at 3, 6, and 12 months, respectively. LFI was predictive of waitlist mortality across a broad LFI range: 3.7-5.2. We identified an optimal LFI cutoff of 4.4 (95% confidence interval [CI], 4.0-4.8) for 3-month mortality, 4.2 (95% CI, 4.1-4.4) for 6-month mortality, and 4.2 (95% CI, 4.1-4.4) for 12-month mortality. The AUC for prediction of 3-month mortality for MELDNa was 0.73; the addition of LFI to MELDNa improved the AUC to 0.79. CONCLUSIONS:LFI is predictive of waitlist mortality across a wide spectrum of LFI values. The optimal LFI cutoff for waitlist mortality was 4.4 at 3 months and 4.2 at 6 and 12 months. The discriminative performance of LFI+MELDNa was greater than MELDNa alone. Our data suggest that incorporating LFI with MELDNa can more accurately represent waitlist mortality in LT candidates.

During the COVID-19 pandemic, there has been wide heterogeneity in the medical management of transplant recipients. We aimed to pragmatically capture immunosuppression practices globally following the early months of the pandemic. From June to September 2020, we surveyed 1267 physicians; 40.5% from 71 countries participated. Management decisions were made on a case-by-case basis by the majority (69.6%) of the programs. Overall, 76.8% performed ≥1 transplantation and many commented on avoiding high-risk transplantations. For induction, 26.5% were less likely to give T-cell depletion and 14.8% were more likely to give non-depleting agents. These practices varied by program-level factors more so than the COVID-19 burden. In patients with mild, moderate and severe COVID-19 symptoms 59.7%, 76.0%, and 79.5% decreased/stopped anti-metabolites, 23.2%, 45.4%, and 68.2% decreased/stopped calcineurin inhibitors, and 25.7%, 43.9%, and 57.7% decreased/stopped mTOR inhibitors, respectively. Also, 2.1%, 30.6%, and 46.0% increased steroids in patients with mild, moderate, and severe COVID-19 symptoms. For prevalent transplant recipients, some programs also reported decreasing/stopping steroids (1.8%), anti-metabolites (10.3%), calcineurin inhibitors (4.1%), and mTOR inhibitors (5.5%). Transplant programs changed immunosuppression practices but also avoided high-risk transplants and increased maintenance steroids. The long-term ramifications of these practices remain to be seen as programs face the aftermath of the pandemic.

BACKGROUND & AIMS:Cirrhosis leads to malnutrition and muscle wasting that manifests as frailty, which may be influenced by cirrhosis aetiology. We aimed to characterize the relationship between frailty and cirrhosis aetiology. METHODS:Included were adults with cirrhosis listed for liver transplantation (LT) at 10 US centrer who underwent ambulatory testing with the Liver Frailty Index (LFI; 'frail' = LFI ≥ 4.4). We used logistic regression to associate aetiologies and frailty, and competing risk regression (LT as the competing risk) to determine associations with waitlist mortality (death/delisting for sickness). RESULTS:Of 1,623 patients, rates of frailty differed by aetiology: 22% in chronic hepatitis C, 31% in alcohol-associated liver disease (ALD), 32% in non-alcoholic fatty liver disease (NAFLD), 21% in autoimmune/cholestatic and 31% in 'other' (P < .001). In univariable logistic regression, ALD (OR 1.53, 95% CI 1.12-2.09), NAFLD (OR 1.64, 95% CI 1.18-2.29) and 'other' (OR 1.58, 95% CI 1.06-2.36) were associated with frailty. In multivariable logistic regression, only ALD (OR 1.40; 95% 1.01-1.94) and 'other' (OR 1.59; 95% 1.05-2.40) remained associated with frailty. A total of 281 (17%) patients died/were delisted for sickness. In multivariable competing risk regression, LFI was associated with waitlist mortality (sHR 1.05, 95% CI 1.03-1.06), but aetiology was not (P > .05 for each). No interaction between frailty and aetiology on the association with waitlist mortality was found (P > .05 for each interaction term). CONCLUSIONS:Frailty is more common in patients with ALD, NAFLD and 'other' aetiologies. However, frailty was associated with waitlist mortality independent of cirrhosis aetiology, supporting the applicability of frailty across all cirrhosis aetiologies.