Preoperative assessment of high-risk candidates to predict survival after heart transplantation

Association between early postoperative hypoalbuminaemia and outcome after orthotopic heart transplantation

OBJECTIVES

In patients undergoing heart transplantation (HTX), preoperative liver impairment and consecutive hypoalbuminaemia are associated with increased mortality. The role of early postoperative hypoalbuminaemia after HTX is unclear. This study investigated the association between early postoperative hypoalbuminaemia and 1-year mortality as well as 'days alive and out of hospital' (DAOH) after HTX.

METHODS

This retrospective cohort study included patients who underwent HTX at the University Hospital Duesseldorf, Germany, between 2010 and 2022. The main exposure was serum albumin concentration at intensive care unit (ICU) arrival. The primary endpoints were mortality and DAOH within 1 year after surgery. Receiver operating characteristic (ROC) curve analysis was performed and logistic and quantile regression models with adjustment for 13 a priori defined clinical risk factors were conducted.

RESULTS

Out of 241 patients screened, 229 were included in the analysis (mean age 55 ± 11 years, 73% male). ROC analysis showed moderate discrimination for 1-year mortality by postoperative serum albumin after HTX [AUC = 0.74; 95% confidence interval (CI): 0.66-0.83]. The cutoff for serum albumin at ICU arrival was 3.0 g/dl. According to multivariate logistic and quantile regression, there were independent associations between hypoalbuminaemia and mortality/DAOH [odds ratio of 4.76 (95% CI: 1.94-11.67) and regression coefficient of -46.97 (95% CI: -83.81 to -10.13)].

CONCLUSIONS

Postoperative hypoalbuminaemia <3.0 g/dl is associated with 1-year mortality and reduced DAOH after HTX and therefore might be used for early postoperative risk re-assessment in clinical practice.

Association of frailty with postoperative outcomes following thoracic transplantation: A national analysis

Objective

Frailty has been repeatedly associated with inferior outcomes after surgical hospitalizations. However, a thorough evaluation of the impact of frailty on the clinical and financial outcomes of patients undergoing solid-organ thoracic transplantation is sparse in the literature. We evaluated the association of frailty, as determined by an administrative tool, with postoperative outcomes and healthcare resource use after heart or lung transplantation.

Methods

The Nationwide Readmissions Database was used to identify all adult hospitalizations for heart or lung transplant from 2014 to 2020. Patients were grouped as frail or nonfrail using International Classification of Diseases codes associated with conditions in the Johns Hopkins Adjusted Clinical Groups cluster. Multivariable regression models were developed to evaluate the association of frailty status on in-hospital mortality, complications, length of stay, costs, and unplanned readmissions.

Results

Of an estimated 35,862 heart or lung transplant recipients, 7316 (20.4%) were considered frail. After multivariable adjustment, frailty in heart transplantation was associated with greater odds of in-hospital mortality (adjusted odds ratio, 1.54; 95% CI, 1.19-1.99) and infectious complications (adjusted odds ratio, 1.77; 95% CI, 1.45-2.15; P < .001). Frailty in lung transplantation was also associated with higher odds of in-hospital mortality (adjusted odds ratio, 1.38; 95% CI, 1.11-1.69) and infectious complications (adjusted odds ratio, 1.93; 95% CI, 1.60-2.31). In addition, frailty in both heart transplantation and lung transplantation was associated with increased postoperative length of stay and greater costs.

Conclusions

Among transplant recipients, those classified as frail were associated with increased in-hospital mortality, perioperative complications, and resource use.

Association of high-priority exceptions with waitlist mortality among heart transplant candidates

BACKGROUND

The US heart allocation system ranks candidates using six categorical status levels. Transplant programs can request exceptions to increase a candidate's status level if they believe their candidate has the same medical urgency as candidates who meet the standard criteria for that level. We aimed to determine if exception candidates have the same medical urgency as standard candidates.

METHODS

Using the Scientific Registry of Transplant Recipients, we constructed a longitudinal waitlist history dataset of adult heart-only transplant candidates listed between October 18, 2018 and December 1, 2021. We estimated the association between exceptions and waitlist mortality with a mixed-effects Cox proportional hazards model that treated status and exceptions as time-dependent covariates.

RESULTS

Out of 12,458 candidates listed during the study period, 2273 (18.2%) received an exception at listing and 1957 (15.7%) received an exception after listing. After controlling for status, exception candidates had approximately half the risk of waitlist mortality as standard candidates (hazard ratio [HR] 0.55, 95% confidence interval [CI] [0.41, 0.73], p < .001). Exceptions were associated with a 51% lower risk of waitlist mortality among Status 1 candidates (HR 0.49, 95% CI [0.27, 0.91], p = .023) and a 61% lower risk among Status 2 candidates (HR 0.39, 95% CI [0.24, 0.62], p < .001).

CONCLUSIONS

Under the new heart allocation policy, exception candidates had significantly lower waitlist mortality than standard candidates, including exceptions for the highest priority statuses. These results suggest that candidates with exceptions, on average, have a lower level of medical urgency than candidates who meet standard criteria.

Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support

Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to train the model. Shapley Additive Explanations was used to identify and interpret the most important features for XGBoost predictions. Results: A total of 1584 patients were included, with a median age of 56 (interquartile range: 46-62) and 74% male. Actual 1-year mortality was 12.1%. Out of 498 available variables, 43 were selected for the final model. The area under the receiver operator characteristics curve (AUC) for the XGBoost model was 0.71 (95% CI: 0.62-0.78). The most important variables predictive of 1-year mortality included recipient functional status, age, pulmonary capillary wedge pressure (PCWP), cardiac output, ECMO usage, and serum creatinine. Conclusions: An interpretable machine learning model trained on a large clinical database demonstrated good performance in predicting 1-year mortality for patients bridged to heart transplantation with tMCS. Machine learning may be used to enhance clinician judgement in the care of markedly high-risk transplant recipients.

Development and validation of specific post-transplant risk scores according to the circulatory support status at transplant: A UNOS cohort analysis

BACKGROUND

The clinical use of post-transplant risk scores is limited by their poor statistical performance. We hypothesized that developing specific prognostic models for each type of circulatory support at transplant may improve risk stratification.

METHODS

We analyzed the UNOS database including contemporary, first, non-combined heart transplantations (2013-2018). The endpoint was death or retransplantation during the first year post-transplant. Three different circulatory support statuses at transplant were considered: no support, durable mechanical support and temporary support (inotropes, temporary mechanical support). We generated 1,000 bootstrap samples that we randomly split into derivation and test sets. In each sample, we derived an overall model and 3 specific models (1 for each type of circulatory support) using Cox regressions, and compared, in the test set, their statistical performance for each type of circulatory support.

RESULTS

A total of 13,729 patients were included; 1,220 patients (8.9%) met the composite endpoint. Circulatory support status at transplant was associated with important differences in baseline characteristics and distinct prognosis (p = 0.01), interacted significantly with important predictive variables included in the overall model, and had a major impact on post-transplant predictive models (type of variables included and their corresponding hazard ratios). However, specific models suffered from poor discriminative performance and significantly improved risk stratification (discrimination, reclassification indices, calibration) compared to overall models in a very limited proportion of bootstrap samples (<15%). These results were consistent across several sensitivity analyzes.

CONCLUSION

Circulatory support status at transplant reflected different disease states that influenced predictive models. However, developing specific models for each circulatory support status did not significantly improve risk stratification.

Statistical performance of 16 posttransplant risk scores in a contemporary cohort of heart transplant recipients

Accurate risk stratification of early heart transplant failure is required to avoid futile transplants and rationalize donor selection. We aimed to evaluate the statistical performance of existing risk scores on a contemporary cohort of heart transplant recipients. After an exhaustive search, we identified 16 relevant risk scores. From the UNOS database, we selected all first noncombined adult heart transplants performed between 2014 and 2017 for validation. The primary endpoint was death or retransplant during the first year posttransplant. For all scores, we analyzed their association with outcomes, sensitivity, specificity, likelihood ratios, and discrimination (concordance index and overlap of individual scores). The cohort included 9396 patients. All scores were significantly associated with the primary outcome (P < .001 for all scores). Their likelihood ratios, both negative and positive, were poor. The discriminative performance of all scores was limited, with concordance index ranging from 0.544 to 0.646 (median 0.594) and an important overlap of individual scores between patients with or without the primary endpoint. Subgroup analyses revealed important variation in discrimination according to donor age, recipient age, and the type of assist device used at transplant. Our findings raise concerns about the use of currently available scores in the clinical field.

Strategies of Wait-listing for Heart Transplant vs Durable Mechanical Circulatory Support Alone for Patients With Advanced Heart Failure

Importance

Given the shortage of donor hearts and improvement in outcomes with left ventricular assist device (LVAD) therapy, a relevant but, to date, unanswered question is whether select patients with advanced heart failure should receive LVAD destination therapy as an alternative to heart transplant.

Objective

To determine whether a strategy of LVAD destination therapy is associated with similar survival benefit as wait-listing for heart transplant with or without LVAD therapy among patients with advanced heart failure.

Design, Setting, and Participants

This retrospective propensity-matched cohort analysis used data on heart transplants from the United Network for Organ Sharing registry and LVAD implants from the Interagency Registry for Mechanically Assisted Circulatory Support from January 1, 2010, to December 31, 2014. The matched LVAD destination therapy cohort included 3411 patients. Data analysis for this study was conducted from December 22, 2017, to May 24, 2019.

Main Outcomes and Measures

Survival at 5 years was analyzed using Cox proportional hazards models.

Results

In total, 8281 patients had albumin level, creatinine level, and BMI data recorded and were included in the analysis. Despite propensity score matching, the 3411 patients receiving LVAD destination therapy still tended to be slightly older than the 3411 patients wait-listed for heart transplant (64.0 years [interquartile range, 55.0-70.0 years] vs 60.0 [interquartile range, 54.0-65.0 years]; P < .001), but there was no significant difference in sex (2701 men [79.2%] vs 2648 men [77.6%]; P = .13). After propensity score matching for age, sex, body mass index, renal function, and albumin level, 3411 patients were wait-listed for heart transplant. This included 1607 patients with bridge to transplant LVAD therapy and 1804 patients without LVAD. The strategy of wait-listing for heart transplant was associated with better 5-year survival than LVAD destination therapy (risk ratio, 0.42; 95% CI, 0.38-0.46) after matching and adjusting for key clinical factors. This survival advantage was associated with heart transplant (adjusted risk ratio for time-dependent transplant status, 0.27; 95% CI, 0.24-0.32).

Conclusions and Relevance

The present analysis suggests that heart transplant with or without bridge to transplant LVAD therapy was associated with superior 5-year survival compared with LVAD destination therapy among patients matched on several relevant clinical factors. Continued improvement in LVAD technology, along with prospective comparative research, appears to be needed to amend this strategy.

Long-term Survival After Heart Transplantation: A Population-based Nested Case-Control Study

BACKGROUND

Heart transplantation is the mainstay of treatment for patients in end-stage heart failure. This study sought to contrast survival after transplantation with that of the general population to quantify standardized mortality rates using a nested case-control study design.

METHODS

Control subjects were noninstitutionalized inhabitants of the United States identified through the National Longitudinal Mortality study. Case subjects were adults who underwent heart transplantation between 1990 and 2007 and identified through the Organ Procurement and Transplantation Network. Propensity-matching (5:1, nearest neighbor, caliper = 0.1) was utilized to identify suitable control subjects based on age, sex, race, and state of permanent residency. The primary study endpoint was 10-year survival.

RESULTS

In all, 31,883 heart transplant recipients were matched to 159,415 noninstitutionalized residents of the United States. The 10-year survival of heart transplant recipients was 53%. The population expected mortality rate was 15.9 deaths per 100 person-years with an observed rate of 45.1 deaths per 100 person-years (standardized mortality rate [SMR] 2.84; 95% confidence interval, 2.82 to 2.87). The broadest gaps between observed and expected survival were evident in female (SMR 3.63), black (SMR 3.67), and Hispanic (SMR 4.12) recipients. Standardized mortality ratios declined over time (1990 to 1995, 3.09; 1996 to 2000, 2.90; 2001 to 2007, 2.58). The long-term standardized survival of older recipients was closest to that expected for their age.

CONCLUSIONS

Heart transplant recipients have considerable long-term survival and have a threefold higher standardized long-term mortality rate than that of the noninstitutionalized population. Long-term mortality rates have consistently declined over time and will likely continue to decrease.

A Systematic Review for Variables to Be Collected in a Transplant Database for Improving Risk Prediction

BACKGROUND

This systematic review was commissioned to identify new variables associated with transplant outcomes that are not currently collected by the Organ Procurement and Transplantation Network (OPTN).

METHODS

We identified 81 unique studies including 1 193 410 patients with median follow-up of 36 months posttransplant, reporting 108 unique risk factors.

RESULTS

Most risk factors (104) were recipient related; few (4) were donor related. Most risk factors were judged to be practical and feasible to routinely collect. Relative association measures were small to moderate for most risk factors (ranging between 1.0 and 2.0). The strongest relative association measure for a heart transplant outcome with a risk factor was 8.6 (recipient with the previous Fontan operation), for a kidney transplant 2.8 (sickle cell nephropathy as primary cause of end-stage renal disease), for a liver transplant 14.3 (recipient serum ferritin >500 µg/L), and for a lung transplant 6.3 (Burkholderia cepacia complex infection for 1 y or less). OPTN may consider some of these 108 variables for future collection to enhance transplant research and clinical care.

CONCLUSIONS

Evidence-based approaches can be used to determine variables collected in databases and registries. Several candidate variables have been identified for OPTN.

Risk prediction models for survival after heart transplantation: A systematic review

Risk prediction scores have been developed to predict survival following heart transplantation (HT). Our objective was to systematically review the model characteristics and performance for all available scores that predict survival after HT. Ovid Medline and Epub Ahead of Print and In-Process & Other Non-Indexed Citations, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Clinical Trials were searched to December 2018. Eligible articles reported a score to predict mortality following HT. Of the 5392 studies screened, 21 studies were included that derived and/or validated 16 scores. Seven (44%) scores were validated in external cohorts and 8 (50%) assessed model performance. Overall model discrimination ranged from poor to moderate (C-statistic/area under the receiver operating characteristics 0.54-0.77). The IMPACT score was the most widely validated, was well calibrated in two large registries, and was best at discriminating 3-month survival (C-statistic 0.76). Most scores did not perform particularly well in any cohort in which they were assessed. This review shows that there are insufficient data to recommend the use of one model over the others for prediction of post-HT outcomes.

Mechanical Circulatory Support Device Utilization and Heart Transplant Waitlist Outcomes in Patients With Restrictive and Hypertrophic Cardiomyopathy

BACKGROUND

Patients with restrictive cardiomyopathy (RCM) and hypertrophic cardiomyopathy (HCM) generally are considered poor candidates for mechanical circulatory support devices (MCSDs) and often not able to be bridged mechanically to heart transplantation. This study characterized MCSD utilization and transplant waitlist outcomes in patients with RCM/HCM under the current allocation system and discusses changes in the era of the new donor allocation system.

METHODS AND RESULTS

Patients waitlisted from 2006 to 2016 in the United Network for Organ Sharing registry were stratified by RCM/HCM versus other diagnoses. MCSD utilization and waitlist duration were analyzed by propensity score models. Waitlist outcomes were assessed by cumulative incidence functions with competing events. Predictors of waitlist mortality or delisting for worsening status in patients with RCM/HCM were identified by proportional hazards model. Of 30 608 patients on the waitlist, 5.1% had RCM/HCM. Patients with RCM/HCM had 31 fewer waitlist days (P<0.01) and were ≈26% less likely to receive MCSD (P<0.01). Cumulative incidence of waitlist mortality was similar between cohorts; however, patients with RCM/HCM had higher incidence of heart transplantation. Predictors of waitlist mortality or delisting for worsening status in patients with RCM/HCM without MCSD support included estimated glomerular filtration rate <60 mL/min per 1.73 m², pulmonary capillary wedge pressure >20 mm Hg, inotrope use, and subjective frailty.

CONCLUSIONS

Patients with RCM/HCM are less likely to receive MCSD but have similar waitlist mortality and slightly higher incidence of transplantation compared with other patients. The United Network for Organ Sharing RCM/HCM risk model can help identify patients who are at high risk for clinical deterioration and in need of expedited heart transplantation.

Prediction of mortality following pediatric heart transplant using machine learning algorithms

BACKGROUND

Optimizing transplant candidates' priority for donor organs depends on the accurate assessment of post-transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes are difficult to predict accurately using conventional multivariable regression. Therefore, we evaluated the utility of 3 ML algorithms for predicting mortality after pediatric HTx.

METHODS

We identified patients <18 years of age receiving HTx in 2006-2015 in the UNOS Registry database. Mortality within 1, 3, or 5 years was predicted using classification and regression trees, RFs, and ANN. Each model was trained using cross-validation, then validated in a separate testing set. Model performance was primarily evaluated by the area under the receiver operating characteristic (AUC) curve.

RESULTS

The training set included 2802 patients, whereas 700 were included in the testing set. RF achieved the best fit to the training data with AUCs of 0.74, 0.68, and 0.64 for 1-, 3-, and 5-year mortality, respectively, and performed best in the testing data, with AUCs of 0.72, 0.61, and 0.60, respectively. Nevertheless, sensitivity was poor across models (training: 0.22-0.58; testing: 0.07-0.49).

DISCUSSION

ML algorithms demonstrated fair predictive utility in both training and testing data, but the sensitivity of these algorithms was generally poor. With the registry missing data on many determinants of long-term survival, the ability of ML methods to predict mortality after pediatric HTx may be fundamentally limited.

Systemic inflammation in acute cardiorenal syndrome: an observational pilot study

AIMS

Acute cardiorenal syndrome (CRS) with and without consideration of the volume state was assessed with regard to inflammatory parameters.

METHODS AND RESULTS

Blood samples from patients with acute CRS (Ronco type 1 or 3, Group 1, n = 15), end-stage renal disease (Group 2, n = 12), hypertension (Group 3, n = 15), and, in a second cohort, with acute CRS and hypervolemia (Group 4, n = 9) and hypertension (Group 5, n = 10) were analysed with regard to lipopolysaccharide-binding protein (LBP), interleukins (ILs), and monocyte function (flow cytometry) both on admission (all groups) and on discharge (Groups 1 and 4). By discharge, one Group 1 patient died. LBP (ANOVA for Groups 1-3: P = 0.001) and IL-6 (Kruskal-Wallis for Groups 1-3: P < 0.0001) were higher in Group 1 (LBP: 11.7 ± 2.0 μg/mL; IL-6: 15.0 ± 6.1 pg/mL) and in Group 2 (LBP: 10.4 ± 1.4 μg/mL; IL-6: 14.6 ± 3.8 pg/mL) than in Group 3 (LBP: 5.8 ± 0.4 μg/mL; IL-6: 1.8 ± 0.4 pg/mL). In a direct comparison, the proportion of activated monocytes (CD14 and CD16 positive) was higher in Group 1 (6.9% ± 0.7%) vs. Group 3 (5.1% ± 0.6%; P = 0.018). Group 4 patients had higher IL-6 plasma levels (34.2 ± 10.1 pg/mL) than Group 1 patients (15.0 ± 6.1 pg/mL; P = 0.03). All other findings obtained in CRS groups (Groups 1 and 4) were comparable.

CONCLUSIONS

In acute CRS, a state of systemic inflammation was found, which is comparable with the end-stage renal disease situation. In comparison with hypertensive controls, a monocytic activation was found in acute CRS regardless of volume state.

Performance of existing risk scores around heart transplantation: validation study in a 4-year cohort

Several risk scores exist to help identify best candidate recipients for heart transplantation (HTx). This study describes the performance of five heart failure risk scores and two post-HTx mortality risk scores in a French single-centre cohort. All patients listed for HTx through a 4-year period were included. Waiting-list risk scores [Heart Failure Survival Score (HFSS), Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC), Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF) and Get With The Guidelines-Heart Failure (GWTG-HF)] and post-HTx scores Index for Mortality Prediction After Cardiac Transplantation (IMPACT and CARRS) were computed. Main outcomes were 1-year mortality on waiting list and after HTx. Performance was assessed using receiver operator characteristic (ROC), calibration and goodness-of-fit analyses. The cohort included 414 patients. Waiting-list mortality was 14.0%, and post-HTx mortality was 16.3% at 1-year follow-up. Heart failure risk scores had adequate discrimination regarding waiting-list mortality (ROC AUC for HFSS = 0.68, SHFM = 0.74, OPTIMIZE-HF = 0.72, MAGGIC = 0.70 and GWTG = 0.77; all P-values <0.05). On the contrary, post-HTx risk scores did not discriminate post-HTx mortality (AUC for IMPACT = 0.58, and CARRS = 0.48, both P-values >0.50). Subgroup analysis on patients undergoing HTx after ventricular assistance device (VAD) implantation (i.e. bridge-to-transplantation) (n = 36) showed an IMPACT AUC = 0.72 (P < 0.001). In this single-centre cohort, existing heart failure risk scores were adequate to predict waiting-list mortality. Post-HTx mortality risk scores were not, except in the VAD subgroup.

Towards a cardiac allocation score: a retrospective calculation for 73 patients from a German transplant center

Background

Due to a growing discrepancy between the transplant waiting list and decreasing numbers of available donor hearts, cardiac transplantation rates in Germany have been declining in the past years. Currently, patients on the waiting list are prioritized by medical urgency and waiting time and therefore a majority of all cardiac transplants is performed in very ill patients. Recently, a different allocation algorithm was proposed that included predicted post-transplant survival as a parameter for organ allocation. So far, little data exists on how such a “Cardiac Allocation Score” (CAS) relates to our current transplant patient population and on how such a change in organ allocation could change clinical practice.

Methods

We calculated a theoretical retrospective Cardiac Allocation Score for 73 patients recruited and transplanted at our medium-volume center in Germany based on a hypothetical scoring algorithm recently published by Eurotransplant.

Results

Overall, 37 patients (50.7%) were transplanted on high urgency status (HU), 27 (37%) were being supported by a VAD at time of transplant. 57 (78.1%) were male. We found a relatively normal distribution of the hypothetical CAS with a median of 32.91 and a mean of 31.95 +/−10.02. Overall, CAS-Scores were lower than previously described for a Eurotransplant patient cohort of high urgency patients, but there was a significant overlap in score values between patients on HU and T status. CAS-values of VAD-supported patients were lower than in patients without mechanical support. The IMPACT-score as part of the CAS was used for prediction of post-transplant survival and seems suitable to predict outcome in our patient population.

Conclusion

In a retrospective analysis, the recently proposed Cardiac Allocation Score seems to show a normal distribution of priority values in our patient cohort. The IMPACT-score predicted outcome after transplantation and could serve as part of the CAS-algorithm to predict post-transplant survival in this single center real-world scenario. Implementation of the CAS could significantly change organ allocation practice, including a potential prioritization of current T-status patients over HU-status patients.

Comparison of 2-Year Outcomes of Extended Criteria Cardiac Transplantation Versus Destination Left Ventricular Assist Device Therapy Using Continuous Flow

Alternatives have emerged for patients ineligible for cardiac transplantation under standard criteria. The purpose of our study was to compare outcomes in patients ineligible for cardiac transplantation under standard criteria, treated either with extended criteria cardiac transplantation (ECCT) or a continuous flow destination therapy left ventricular assist device (CF DT-LVAD). From 2005 to 2012, patients treated with either ECCT or CF DT-LVAD at our institution were retrospectively analyzed. In the overall unmatched cohort, we examined mortality and other outcomes, including index hospitalization length of stay, renal function, stroke, and readmission rates. After propensity score (PS) matching, outcomes were compared between ECCT and CF DT-LVAD recipients. Overall, 62 patients underwent ECCT, and 146 patients were treated with CF DT-LVAD. The 2-year mortality estimate for ECCT recipients was 27.3% (95% confidence interval 15.5% to 39.1%) and for CF DT-LVAD recipients was 11.2% (95% confidence interval 4.8% to 17.6%). After PS matching of 39 patients from each treatment group, there was no significant difference in overall survival after 2 years (p = 0.346). In both unmatched and PS-matched analyses, CF DT-LVAD patients compared with ECCT had a significantly higher estimated glomerular filtration rate at 1 year but also had significantly higher hospital readmission rates. Stroke also more commonly occurred after CF DT-LVAD compared with ECCT (17 vs 5, unmatched; and 2 vs 1, PS matched). However, there was no significant difference between PS-matched groups in 2-year stroke-free survival (p = 0.371). In conclusion, ECCT and CF DT-LVAD in select patients are comparable therapies with respect to 2-year survival.

Combination of liver biopsy with MELD-XI scores for post-transplant outcome prediction in patients with advanced heart failure and suspected liver dysfunction

BACKGROUND

Functional and structural liver abnormalities may be found in patients with advanced heart failure (HF). The Model of End-Stage Liver Disease Excluding INR (MELD-XI) score allows functional risk stratification of HF patients on and off anti-coagulation awaiting heart transplantation (HTx), but these scores may improve or worsen depending on bridging therapies and during time on the waiting list. Liver biopsy is sometimes performed to assess for severity of fibrosis. Uncertainty remains whether biopsy in addition to MELD-XI improves prediction of adverse outcomes in patients evaluated for HTx.

METHODS

Sixty-eight patients suspected of advanced liver disease underwent liver biopsy as part of their HTx evaluation. A liver risk score (fibrosis-on-biopsy + 1) × MELD-XI was generated for each patient.

RESULTS

Fifty-two patients were listed, of whom 14 had mechanical circulatory support (MCS). Thirty-six patients underwent transplantation and 27 patients survived ≥1 year post-HTx (74%, as compared with 88% average 1-year survival in HTx patients without suspected liver disease; p < 0.01). Survivors had a lower liver risk score at evaluation for HTx (31.0 ± 20.4 vs 65.2 ± 28.6, p < 0.01). A cut-point of 45 for liver risk score was identified by receiver-operating-characteristic (ROC) analysis. In the analysis using Cox proportional hazards models, a liver risk score ≥45 at evaluation for HTx was associated with greater risk of death at 1 year post-HTx compared with a score of <45 in both univariable (HR 3.94, 95% CI 1.77-8.79, p < 0.001) and multivariable (HR 4.35, 95% CI 1.77-8.79, p < 0.001) analyses. Patients who died <1 year post-HTx had an increased frequency of acute graft dysfunction (44.4% vs 3.7%, p = 0.009), longer ventilation times (55.6% vs 11.1%, p = 0.013) and severe bleeding events (44.4% vs 11.1%, p = 0.049). The liver risk score at evaluation for HTx also predicted 1-year mortality after HTx listing (p < 0.001).

CONCLUSIONS

Patients with HF and advanced liver dysfunction are high-risk HTx candidates. Liver biopsy in addition to MELD-XI improves risk stratification of patients with advanced HF and suspected irreversible liver dysfunction.

Advanced therapies for end-stage heart failure

Management of the advanced heart failure patient can be complex. Therapies include cardiac transplantation and mechanical circulatory support, as well inotropic agents for the short-term. Despite a growing armamentarium of resources, the clinician must carefully weigh the risks and benefits of each therapy to develop an optimal treatment strategy. While cardiac transplantation remains the only true “cure” for end-stage disease, this resource is limited and the demand continues to far outpace the supply. For patients who are transplant-ineligible or likely to succumb to their illness prior to transplant, ventricular assist device therapy has now become a viable option for improving morbidity and mortality. Particularly for the non-operative pa-tient, intravenous inotropes can be utilized for symptom control. Regardless of the treatments considered, care of the heart failure patient requires thoughtful dialogue, multidisciplinary collaboration, and individualized care. While survival is important, most patients covet quality of life above all outcomes. An often overlooked component is the patient’s control over the dying process. It is vital that clinicians make goals-of-care discussions a priority when seeing patients with advanced heart failure. The use of palliative care consultation is well-validated and facilitates these difficult conversations to ensure that all patient needs are ultimately met.