Faculty Bibliography

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.

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 & 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.

Organ transplant patients have poor immune responses to COVID-19 vaccines; thus designing vaccine strategies to protect this vulnerable population from SARS-CoV-2 infection is crucial.

In the seven years since the HIV Organ Policy Equity (HOPE) Act made HIV-positive organ donation to HIV-positive recipients legally permissible in the United States, there have been fewer HIV-positive organ donations than expected. Organ procurement organizations (OPOs) play a key role in the transplant system and barriers at OPOs may be partly responsible for the relatively low number of HIV-positive donors. To understand potential OPO barriers, we conducted semi-structured interviews with 20 OPO staff members. Interviews were recorded, transcribed, and analyzed using a conventional content analytic approach with two coders. OPO staff had high levels of knowledge about HOPE. Many had evaluated referrals of HIV-positive donors and approached families for authorization. Barriers to HIV-positive organ recovery identified included obtaining authorization for donation, potentially disclosing HIV status to next-of-kin, and fear of HIV infection among those engaged in organ recovery. Strategies to overcome these barriers include providing continuing education about the specific tasks required to procure organs from HIV-positive donors, implementing targeted interventions to reduce fear of infection, and developing partnerships with HIV advocacy and care organizations. Given the central role OPOs play, HIV-positive donations are unlikely to occur in significant numbers unless these barriers can be overcome.

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.

Transplant recipients were excluded from the initial clinical trials determining safety and efficacy of the landmark COVID-19 vaccines. Further, there is increasing evidence that immunosuppressed transplant recipients have a blunted antibody response to COVID-19 vaccination. In a concerning report by Sattler et al. in this issue of the JCI, kidney transplant recipients not only lacked a humoral response following two doses of Pfizer BNT162b2, but also displayed substantial impairment of the cellular response to SARS-CoV-2 antigens. This Commentary addresses potential strategies for transplant providers to evaluate and augment vaccine immunogenicity given the likelihood that COVID-19 will remain a world-wide threat to the health of transplant recipients.

Recently, model for end-stage liver disease (MELD)-based liver allocation in the United States has been questioned based on concerns that waitlist mortality for a given biologic MELD (bMELD), calculated using laboratory values alone, might be higher at certain centers in certain locations across the country. Therefore, we aimed to quantify the center-level variation in bMELD-predicted mortality risk. Using Scientific Registry of Transplant Recipients (SRTR) data from January 2015 to December 2019, we modeled mortality risk in 33 260 adult, first-time waitlisted candidates from 120 centers using multilevel Poisson regression, adjusting for sex, and time-varying age and bMELD. We calculated a "MELD correction factor" using each center's random intercept and bMELD coefficient. A MELD correction factor of +1 means that center's candidates have a higher-than-average bMELD-predicted mortality risk equivalent to 1 bMELD point. We found that the "MELD correction factor" median (IQR) was 0.03 (-0.47, 0.52), indicating almost no center-level variation. The number of centers with "MELD correction factors" within ±0.5 points, and between ±0.5-± 1, ±1.0-±1.5, and ±1.5-±2.0 points was 62, 41, 13, and 4, respectively. No centers had waitlisted candidates with a higher-than-average bMELD-predicted mortality risk beyond ±2 bMELD points. Given that bMELD similarly predicts waitlist mortality at centers across the country, our results support continued MELD-based prioritization of waitlisted candidates irrespective of center.

In this study of 12 people with HIV (PWH) who received the first dose of SARS-CoV-2 mRNA vaccination, anti-SARS-CoV-2 receptor-binding domain antibodies were detectable in all participants; lower antibody levels were seen in those with lower CD4+ counts, and vaccine reactions were generally mild.