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

The Utility of the Vasoactive-Inotropic Score and Its Nomogram in Guiding Postoperative Management in Heart Transplant Recipients

Background

In the early postoperative stage after heart transplantation, there is a lack of predictive tools to guide postoperative management. Whether the vasoactive-inotropic score (VIS) can aid this prediction is not well illustrated.

Methods

In total, 325 adult patients who underwent heart transplantation at our center between January 2015 and December 2018 were included. The maximum VIS (VISmax) within 24 h postoperatively was calculated. The Kaplan-Meier method was used for survival analysis. A logistic regression model was established to determine independent risk factors and to develop a nomogram for a composite severe adverse outcome combining early mortality and morbidity.

Results

VISmax was significantly associated with extensive early outcomes such as early death, renal injury, cardiac reoperation and mechanical circulatory support in a grade-dependent manner, and also predicted 90-day and 1-year survival (p < 0.05). A VIS-based nomogram for the severe adverse outcome was developed that included VISmax, preoperative advanced heart failure treatment, hemoglobin and serum creatinine. The nomogram was well calibrated (Hosmer-Lemeshow p = 0.424) with moderate to strong discrimination (C-index = 0.745) and good clinical utility.

Conclusion

VISmax is a valuable prognostic index in heart transplantation. In the early post-transplant stage, this VIS-based nomogram can easily aid intensive care clinicians in inferring recipient status and guiding postoperative management.

The IMPACT Score: Does Sex Matter?

OBJECTIVE

The Index for Mortality Prediction After Cardiac Transplantation (IMPACT) score is a quantitative risk index that predicts 1-year mortality risk, derived from United Network for Organ Sharing data in which women are underrepresented. The validity of the IMPACT score in 1-year mortality risk after OHT in women is unknown. The objective of this study was to assess differences in score performance by sex. We hypothesized that the IMPACT score is a poor predictor of 1-year mortality risk after orthotopic heart transplantation (OHT) in women.

DESIGN

In this external validation study, demographic and clinical characteristics were compared by sex. The IMPACT score was calculated and regression models were constructed for the entire sample and stratified by sex. Model discrimination was assessed with the area under the receiver operating characteristic curve, and calibration was assessed graphically.

PARTICIPANTS

Patients 18 years and older who were first-time single OHT recipients from the International Society for Heart and Lung Transplantation registry from 2009 to 2018.

MEASUREMENTS AND MAIN RESULTS

For 1-year mortality, the area under the receiver operating characteristic curve (95% confidence interval) for the full sample was 0.59 (0.57-0.60): 0.58 (0.55-0.61) for women and 0.59 (0.58-0.61) for men. The 1-year mortality was 9.4% in the overall cohort, with no difference in mortality by sex (9.0% v 9.6% women v men, p = 0.22).

CONCLUSIONS

The IMPACT score exhibited poor discrimination and calibration in the International Society for Heart and Lung Transplantation 2009-2019 cohort, overall and by sex. There was no difference in 1-year mortality between women and men.

Importance of Transplant Era on Post-Heart Transplant Predictive Models: A UNOS Cohort Analysis

BACKGROUND

The application of posttransplant predictive models is limited by their poor statistical performance. Neglecting the dynamic evolution of demographics and medical practice over time may be a key issue.

OBJECTIVES

Our objective was to develop and validate era-specific predictive models to assess whether these models could improve risk stratification compared to non-era-specific models.

METHODS

We analyzed the United Network for Organ Sharing (UNOS) database including first noncombined heart transplantations (2001-2018, divided into four transplant eras: 2001-2005, 2006-2010, 2011-2015, 2016-2018). The endpoint was death or retransplantation during the 1st-year posttransplant. We analyzed the dynamic evolution of major predictive variables over time and developed era-specific models using logistic regression. We then performed a multiparametric evaluation of the statistical performance of era-specific models and compared them to non-era-specific models in 1000 bootstrap samples (derivation set, 2/3; test set, 1/3).

RESULTS

A total of 34 738 patients were included, 3670 patients (10.5%) met the composite endpoint. We found a significant impact of transplant era on baseline characteristics of donors and recipients, medical practice, and posttransplant predictive models, including significant interaction between transplant year and major predictive variables (total serum bilirubin, recipient age, recipient diabetes, previous cardiac surgery). Although the discrimination of all models remained low, era-specific models significantly outperformed the statistical performance of non-era-specific models in most samples, particularly concerning discrimination and calibration.

CONCLUSIONS

Era-specific models achieved better statistical performance than non-era-specific models. A regular update of predictive models may be considered if they were to be applied for clinical decision-making and allograft allocation.

Pancreas Transplantation Outcome Predictions-PTOP: A Risk Prediction Tool for Pancreas and Pancreas-Kidney Transplants Based on a European Cohort

Background

For patients with complicated type 1 diabetes having, for example, hypoglycemia unawareness and end-stage renal disease because of diabetic nephropathy, combined pancreas and kidney transplantation (PKT) is the therapy of choice. However, the shortage of available grafts and complex impact of risk factors call for individualized, impartial predictions of PKT and pancreas transplantation (PT) outcomes to support physicians in graft acceptance decisions.

Methods

Based on a large European cohort with 3060 PKT and PT performed between 2006 and 2021, the 3 primary patient outcomes time to patient mortality, pancreas graft loss, and kidney graft loss were visualized using Kaplan-Meier survival curves. Multivariable Cox proportional hazards models were developed for 5- and 10-y prediction of outcomes based on 26 risk factors.

Results

Risk factors associated with increased mortality included previous kidney transplants, rescue allocations, longer waiting times, and simultaneous transplants of other organs. Increased pancreas graft loss was positively associated with higher recipient body mass index and donor age and negatively associated with simultaneous transplants of kidneys and other organs. Donor age was also associated with increased kidney graft losses. The multivariable Cox models reported median C-index values were 63% for patient mortality, 62% for pancreas loss, and 55% for kidney loss.

Conclusions

This study provides an online risk tool at https://riskcalc.org/ptop for individual 5- and 10-y post-PKT and PT patient outcomes based on parameters available at the time of graft offer to support critical organ acceptance decisions and encourage external validation in independent populations.

Molecular states associated with dysfunction and graft loss in heart transplants

BACKGROUND

We explored the changes in gene expression correlating with dysfunction and graft failure in endomyocardial biopsies.

METHODS

Genome-wide microarrays (19,462 genes) were used to define mRNA changes correlating with dysfunction (left ventricular ejection fraction [LVEF] ≤ 55) and risk of graft loss within 3 years postbiopsy. LVEF data was available for 1,013 biopsies and survival data for 779 patients (74 losses). Molecular classifiers were built for predicting dysfunction (LVEF ≤ 55) and postbiopsy 3-year survival.

RESULTS

Dysfunction is correlated with dedifferentiation-decreased expression of normal heart transcripts, for example, solute carriers, along with increased expression of inflammation genes. Many genes with reduced expression in dysfunction were matrix genes such as fibulin 1 and decorin. Gene ontology (GO) categories suggested matrix remodeling and inflammation, not rejection. Genes associated with the risk of failure postbiopsy overlapped dysfunction genes but also included genes affecting microcirculation, for example, arginase 2, which reduces NO production, and endothelin 1. GO terms also reflected increased glycolysis and response to hypoxia, but decreased VEGF and angiogenesis pathways. T cell-mediated rejection was associated with reduced survival and antibody-mediated rejection with relatively good survival, but the main determinants of survival were features of parenchymal injury. Both dysfunction and graft loss were correlated with increased biopsy expression of BNP (gene NPPB). Survival probability classifiers divided hearts into risk quintiles, with actuarial 3-year postbiopsy survival >95% for the highest versus 50% for the lowest.

CONCLUSIONS

Dysfunction in transplanted hearts reflects dedifferentiation, decreased matrix genes, injury, and inflammation. The risk of short-term loss includes these changes but is also associated with microcirculation abnormalities, glycolysis, and response to hypoxia.

Predictors of Survival After Heart Transplant in the New Allocation System: A UNOS Database Analysis

Clinical predictors of posttransplant graft loss since the United Network for Organ Sharing (UNOS) heart allocation system change have not been well characterized. Single organ adult heart transplants from the UNOS database were identified (n = 10,252) and divided into a test cohort (n = 6,869, 67%) and validation cohort (n = 3,383, 33%). A Cox regression analysis was performed on the test cohort to identify recipient and donor risk factors for posttransplant graft loss. Based on the risk factors, a score (max 16) was developed to classify patients in the validation cohort into risk groups of low (≤1), mid (2-3), high (≥4) risk. Recipient factors of advanced age, Black race, recipient blood group O, diabetes, etiology of heart failure, renal dysfunction, elevated bilirubin, redo-transplantation, elevated pulmonary artery pressure, transplant with a durable ventricular assist device, or transplant on extracorporeal membrane oxygenation (ECMO) or ventilator were associated with more posttransplant graft loss. Donor factors of ischemic time and donor age were also associated with outcomes. One year graft survival for the low-, mid-, high-risk groups was 94%, 91%, and 85%, respectively. In conclusion, easily obtainable clinical characteristics at time of heart transplant can predict posttransplant outcomes in the current era.

The Ability of Comorbidity Indices to Predict Mortality After Heart Transplantation: A Validation of the Danish Comorbidity Index for Acute Myocardial Infarction, Charlson Comorbidity Index, and Elixhauser Comorbidity Index

Advanced heart failure patients often have comorbidities of prognostic importance. However, whether total pretransplantation comorbidity burden predicts mortality in patients treated with heart transplantation (HTx) is unknown. We used population-based hospital and prescription data to examine the ability of the Danish Comorbidity Index for Acute Myocardial Infarction (DANCAMI), DANCAMI restricted to noncardiovascular diseases, Charlson Comorbidity Index, and Elixhauser Comorbidity Index to predict 30-d, 1-y, 5-y, and 10-y all-cause and cardiovascular mortality after HTx.

Methods

We identified all adult Danish patients with incident HTx from the Scandiatransplant Database between March 1, 1995, and December 31, 2018 (n = 563). We calculated Harrell's C-Statistics to examine discriminatory performance.

Results

The C-Statistic for predicting 1-y all-cause mortality after HTx was 0.58 (95% confidence interval [CI], 0.50-0.65) for a baseline model including age and sex. Adding comorbidity score to the baseline model did not increase the C-Statistics for DANCAMI (0.58; 95% CI, 0.50-0.65), DANCAMI restricted to noncardiovascular diseases (0.57; 95% CI, 0.50-0.64), Charlson Comorbidity Index (0.59; 95% CI, 0.51-0.66), or Elixhauser Comorbidity Index (0.58; 95% CI, 0.51-0.65). The results for 30-d, 5-y, and 10-y all-cause and cardiovascular mortality were consistent.

Conclusions

After accounting for patient age and sex, none of the commonly used comorbidity indices added predictive value to short- or long-term all-cause or cardiovascular mortality after HTx.

A machine learning model for prediction of 30-day primary graft failure after heart transplantation

Background

Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry.

Methods

We developed a non-linear artificial neural networks (ANN) model to evaluate the association between recipient-donor variables and post-transplant PGF. Patients in the ISHLT registry were randomly divided into derivation and an independent internal validation cohort. The primary endpoint was PGF defined as death within 30 days due to Graft failure or Cardiovascular causes or retransplant within 30 days for causes other than rejection.

Results

Among 64,964 adult recipients transplanted between 1994 and 2013, mean age was 51 years and 22% were female. The incidence of PGF up to 30 days was 3.7%. The ANN model selected 33 of 77 risk variables as relevant for PGF prediction. The C-index in the test cohort was 0.70 (95% CI: 0.68-0.71). The risk variables which most influenced the PGF were underlying HF diagnosis, ischemia time and sex, while renal function had a lower influence.

Conclusion

An ANN model to predict primary graft dysfunction was derived and independently validated. The good discrimination of the ANN model likely results from its flexibility to model potentially non-linear relationships and interactions. Whether this model with improved discrimination can assist in clinical decisions at the time of transplant should be tested.

Impact of the 2018 French two-score allocation scheme on the profile of heart transplantation candidates and recipients: Insights from a high-volume centre

BACKGROUND

In 2018, a new cardiac allograft allocation scheme, based on an individual scoring system, considering the risk of death both on the waiting list and after heart transplantation, was implemented in France.

AIM

To assess the impact of this new scheme on the profile of transplantation candidates and recipients.

METHODS

In this single-centre retrospective study, we included consecutive patients listed and/or transplanted between 01 January 2012 and 30 September 2021 at La Pitié-Salpêtrière Hospital. Baseline characteristics of patients were retrieved from the national CRISTAL registry and were compared according to the type of allocation scheme (before or after 2018).

RESULTS

A total of 1098 newly listed transplantation candidates and 855 transplant recipients were included. One-year mortality rates after listing and after transplantation were 12.4% and 20%, respectively. At listing, the proportion of candidates on inotropes significantly declined following the scheme update (26.3 versus 20.9%; P=0.038), reflecting a change in medical practice. At transplantation, recipients had worse kidney function (estimated glomerular filtration rate<60mL/min/1.73 m²: old scheme, 29.7%; new scheme, 46.4%; P<0.001) and were more likely to be on extracorporeal membrane oxygenation support (33.5% versus 28.1%; P=0.080) under the new scheme, reflecting the prioritization of more severe patients. Outcomes after transplantation were not significantly influenced by the allocation system.

CONCLUSIONS

The implementation of the 2018 French allocation scheme had a limited impact on the profile of transplantation candidates, but selected more severe patients for transplantation without significant impact on outcomes after transplantation.

Postoperative high-sensitivity troponin T predicts 1-year mortality and days alive and out of hospital after orthotopic heart transplantation

BACKGROUND

Orthotopic heart transplantation (HTX) is the gold standard to treat end-stage heart failure. Numerous risk stratification tools have been developed in the past years. However, their clinical utility is limited by their poor discriminative ability. High sensitivity troponin T (hsTnT) is the most specific biomarker to detect myocardial cell injury. However, its prognostic relevance after HTX is not fully elucidated. Thus, this study evaluated the predictive value of postoperative hsTnT for 1-year survival and 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 2011 and 2021. The main exposure was hsTnT concentration at 48 h after HTX. The primary endpoints were mortality and DAOH within 1 year after surgery. Receiver operating characteristic (ROC) curve analysis, logistic regression model and linear regression with adjustment for risk index for mortality prediction after cardiac transplantation (IMPACT) were performed.

RESULTS

Out of 231 patients screened, 212 were included into analysis (mean age 55 ± 11 years, 73% male). One-year mortality was 19.7% (40 patients) and median DAOH was 298 days (229-322). ROC analysis revealed strongest discrimination for mortality by hsTnT at 48 h after HTX [AUC = 0.79 95% CI 0.71-0.87]. According to Youden Index, the cutoff for hsTnT at 48 h and mortality was 1640 ng/l. After adjustment for IMPACT score multivariate logistic and linear regression showed independent associations between hsTnT and mortality/DAOH with odds ratio of 8.10 [95%CI 2.99-21.89] and unstandardized regression coefficient of -1.54 [95%CI -2.02 to -1.06], respectively.

CONCLUSION

Postoperative hsTnT might be suitable as an early prognostic marker after HTX and is independently associated with 1-year mortality and poor DAOH.

Donor heart selection: Evidence-based guidelines for providers

The proposed donor heart selection guidelines provide evidence-based and expert-consensus recommendations for the selection of donor hearts following brain death. These recommendations were compiled by an international panel of experts based on an extensive literature review.

Enhanced survival prediction using explainable artificial intelligence in heart transplantation

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.

Hemodynamic markers of pulmonary vasculopathy for prediction of early right heart failure and mortality after heart transplantation

BACKGROUND

Elevated pulmonary vascular resistance (PVR) is broadly accepted as an imminent risk factor for mortality after heart transplantation (HTx). However, no current HTx recipient risk score includes PVR or other hemodynamic parameters. This study examined the utility of various hemodynamic parameters for risk stratification in a contemporary HTx population.

METHODS

Patients from seven German HTx centers undergoing HTx between 2011 and 2015 were included retrospectively. Established risk factors and complete hemodynamic datasets before HTx were analyzed. Outcome measures were overall all-cause mortality, 12-month mortality, and right heart failure (RHF) after HTx.

RESULTS

The final analysis included 333 patients (28% female) with a median age of 54 (IQR 46-60) years. The median mean pulmonary artery pressure was 30 (IQR 23-38) mm Hg, transpulmonary gradient 8 (IQR 5-10) mm Hg, and PVR 2.1 (IQR 1.5-2.9) Wood units. Overall mortality was 35.7%, 12-month mortality was 23.7%, and the incidence of early RHF was 22.8%, which was significantly associated with overall mortality (log-rank HR 4.11, 95% CI 2.47-6.84; log-rank p < .0001). Pulmonary arterial elastance (Ea) was associated with overall mortality (HR 1.74, 95% CI 1.25-2.30; p < .001) independent of other non-hemodynamic risk factors. Ea values below a calculated cutoff represented a significantly reduced mortality risk (HR 0.38, 95% CI 0.19-0.76; p < .0001). PVR with the established cutoff of 3.0 WU was not significant. Ea was also significantly associated with 12-month mortality and RHF.

CONCLUSIONS

Ea showed a strong impact on post-transplant mortality and RHF and should become part of the routine hemodynamic evaluation in HTx candidates.

Alterations in the kallikrein-kinin system predict death after heart transplant

Heart transplantation remains the definitive treatment for end stage heart failure. Because availability is limited, risk stratification of candidates is crucial for optimizing both organ allocations and transplant outcomes. Here we utilize proteomics prior to transplant to identify new biomarkers that predict post-transplant survival in a multi-institutional cohort. Microvesicles were isolated from serum samples and underwent proteomic analysis using mass spectrometry. Monte Carlo cross-validation (MCCV) was used to predict survival after transplant incorporating select recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. We identified six protein markers with prediction performance above AUROC of 0.6, including Prothrombin (F2), anti-plasmin (SERPINF2), Factor IX, carboxypeptidase 2 (CPB2), HGF activator (HGFAC) and low molecular weight kininogen (LK). No clinical characteristics demonstrated an AUROC > 0.6. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA). Differential expression analysis identified enriched pathways prior to transplant that were associated with post-transplant survival including activation of platelets and the coagulation pathway prior to transplant. Specifically, upregulation of coagulation cascade components of the kallikrein-kinin system (KKS) and downregulation of kininogen prior to transplant were associated with survival after transplant. Further prospective studies are warranted to determine if alterations in the KKS contributes to overall post-transplant survival.

Assessing the Relationship Between Molecular Rejection and Parenchymal Injury in Heart Transplant Biopsies

BACKGROUND

The INTERHEART study (ClinicalTrials.gov #NCT02670408) used genome-wide microarrays to detect rejection in endomyocardial biopsies; however, many heart transplants with no rejection have late dysfunction and impaired survival. We used the microarray measurements to develop a molecular classification of parenchymal injury.

METHODS

In 1320 endomyocardial biopsies from 645 patients previously studied for rejection-associated transcripts, we measured the expression of 10 injury-induced transcript sets: 5 induced by recent injury; 2 reflecting macrophage infiltration; 2 normal heart transcript sets; and immunoglobulin transcripts, which correlate with time. We used archetypal clustering to assign injury groups.

RESULTS

Injury transcript sets correlated with impaired function. Archetypal clustering based on the expression of injury transcript sets assigned each biopsy to 1 of 5 injury groups: 87 Severe-injury, 221 Late-injury, and 3 with lesser degrees of injury, 376 No-injury, 526 Mild-injury, and 110 Moderate-injury. Severe-injury had extensive loss of normal transcripts (dedifferentiation) and increase in macrophage and injury-induced transcripts. Late-injury was characterized by high immunoglobulin transcript expression. In Severe- and Late-injury, function was depressed, and short-term graft failure was increased, even in hearts with no rejection. T cell-mediated rejection almost always had parenchymal injury, and 85% had Severe- or Late-injury. In contrast, early antibody-mediated rejection (ABMR) had little injury, but late ABMR often had the Late-injury state.

CONCLUSION

Characterizing heart transplants for their injury state provides new understanding of dysfunction and outcomes and demonstrates the differential impact of T cell-mediated rejection versus ABMR on the parenchyma. Slow deterioration from ABMR emerges as a major contributor to late dysfunction.

Utility of the IMPACT score for predicting heart transplant mortality. Analysis on a contemporary cohort of the Spanish Heart Transplant Registry

INTRODUCTION AND OBJECTIVES

The Index for Mortality Prediction After Cardiac Transplantation (IMPACT) score was derived and validated as a predictor of mortality after heart transplantation (HT). The primary objective of this work is to externally validate the IMPACT score in a contemporary Spanish cohort.

METHODS

Spanish Heart Transplant Registry data were used to identify adult (>16 years) HT patients between January 2000 and December 2015. Retransplantation, multiorgan transplantation and patients in whom at least one of the variables required to calculate the IMPACT score was missing were excluded from the analysis (N = 2,810).

RESULTS

Median value of the IMPACT score was 5 points (IQR: 3, 8). Overall 1-year survival rate was 79.1%. Kaplan-Meier 1-year survival rates by IMPACT score categories (0-2, 3-5, 6-9, 10-14, ≥ 15) were 84.4%, 81.5%, 79.3%, 77.3% and 58.5% respectively (Log-Rank test: p<0.001). Performance analysis showed a good calibration (Hosmer-Lemeshow chi-square for one year was 7.56; p = 0.47) and poor discrimination ability (AUC-ROC 0.59) of the IMPACT score as a predictive model.

CONCLUSIONS

In a contemporary Spanish cohort, the IMPACT score failed to accurately predict the risk of death after HT. This article is protected by copyright. All rights reserved.

Child-Turcotte-Pugh versus MELD-XI identify distinct high-risk populations for heart transplantation following ventricular assist device placement

BACKGROUND

Patients with end-stage heart failure frequently have significant congestive hepatopathy requiring hepatology assessment prior to heart transplantation listing. An elevated Model for End-stage Liver Disease score with modification to exclude INR (MELD-XI) has been associated with increased mortality following heart transplantation (HT). This study's primary aim was to examine whether Child-Turcotte-Pugh (CTP) classification is associated with post-transplant mortality in patients bridged to transplant with left ventricular assist devices.

METHODS AND RESULTS

We conducted a retrospective analysis of 134 patients from our center. Age, CTP class, and MELD-XI at HT were included in the multivariate model for the primary outcome, which demonstrated a significant association between 1-year mortality and CTP class (CTP-A HR: .08, CI .01-.46, P < .01; CTP-B HR: .25, CI .05-1.2, P = .08; reference group CTP-C), and MELD-XI (HR: 1.15; CI: 1.03-1.28; P = .01), but no significant difference for age (HR: .97; CI: .93-1.01; P = .15). Only 13/33 patients with CTP improvement after assist device also had improvement in MELD-XI.

CONCLUSIONS

Patients with relatively low MELD-XI scores with discordantly high CTP classification may be a distinct subset for whom MELD-XI underestimates the risk of mortality after heart transplantation compared to CTP.

Patient selection for heart transplant: balancing risk

PURPOSE OF REVIEW

Heart failure incidence continues to rise despite a relatively static number of available donor hearts. Selecting an appropriate heart transplant candidate requires evaluation of numerous factors to balance patient benefit while maximizing the utility of scarce donor hearts. Recent research has provided new insights into refining recipient risk assessment, providing additional tools to further define and balance risk when considering heart transplantation.

RECENT FINDINGS

Recent publications have developed models to assist in risk stratifying potential heart transplant recipients based on cardiac and noncardiac factors. These studies provide additional tools to assist clinicians in balancing individual risk and benefit of heart transplantation in the context of a limited donor organ supply.

SUMMARY

The primary goal of heart transplantation is to improve survival and maximize quality of life. To meet this goal, a careful assessment of patient-specific risks is essential. The optimal approach to patient selection relies on integrating recent prognostication models with a multifactorial assessment of established clinical characteristics, comorbidities and psychosocial factors.

Donor Cardiac Troponin for Prognosis of Adverse Outcomes in Cardiac Transplantation Recipients: a Systematic Review and Meta-analysis

BACKGROUND

Cardiac troponin is a highly specific and widely available marker of myocardial injury, and elevations in cardiac transplant donors may influence donor selection. We aimed to investigate whether elevated donor troponin has a role as a prognostic biomarker in cardiac transplantation.

METHODS

In a systematic review and meta-analysis, we searched MEDLINE, Embase, and the Cochrane Library, without language restriction, from inception to December 2020. We included studies reporting the association of elevated donor troponin with recipient outcome after cardiac transplant. We generated summary odds ratios and hazard ratios for the association of elevated donor troponin with short- and long-term adverse outcomes. Methodological quality was monitored using the Quality In Prognosis Studies tool, and interstudy heterogeneity was assessed using a series of sensitivity and subgroup analyses.

RESULTS

We included 17 studies involving 15 443 patients undergoing cardiac transplantation. Elevated donor troponin was associated with increased odds of graft rejection at 1 y (odds ratio, 2.54; 95% confidence interval, 1.22-5.28). No significant prognostic relationship was found between donor troponin and primary graft failure, short- to long-term mortality, cardiac allograft vasculopathy, and pediatric graft loss.

CONCLUSIONS

Elevated donor troponin is not associated with an increased short- or long-term mortality postcardiac transplant despite increasing the risk of graft rejection at 1 y. Accordingly, an elevated donor troponin in isolation should not exclude donation.

Donor-recipient risk assessment tools in heart transplant recipients: the Bad Oeynhausen experience

AIMS

Some risk assessment tools have been developed to categorize mortality risk in heart transplant recipients, but it is unclear whether these tools can be used interchangeable in different transplant regions.

METHODS AND RESULTS

We performed a retrospective single-centre study in 1049 adult German heart transplant recipients under jurisdiction of Eurotransplant. Univariable and multivariable Cox regression analysis was used to generate a risk scoring system. C-statistics were used to compare our score with a US score and a French score regarding their ability to discriminate between 1 year survivors and non-survivors within our study cohort. Of 38 parameters assessed, seven recipient-specific parameters [age, height, dilated cardiomyopathy (DCM), ischaemic cardiomyopathy (ICM), total bilirubin, extracorporeal membrane oxygenation (ECMO), and biventricular assist device/total artificial heart (BVAD/TAH) implant], one donor-specific parameter (cold ischaemic time), and one recipient-independent and donor-independent other parameter (late transplant era) were statistically significant in predicting 1 year mortality. The initial score was generated by using the regression coefficients from the multivariable analysis as follows: 1.70 * ln age - 4.0 * ln height - 0.9 * diagnosis (= 1 if diagnosis = DCM) - 0.67 * diagnosis (= 1 if diagnosis = ICM) + 0.33 * ln total bilirubin + 1.74 * ln cold ischaemic time + 0.98 * mechanical circulatory support (MCS) implant (= 1 if MCS implant = ECMO) + 0.47 * MCS implant (= 1 of MCS implant = BVAD/TAH) - 0.66 * transplant era (= 1 if transplant era = 2017-2018). The initial score was converted into the Bad Oeynhausen (BO) score as a positive integer variable by means of the following formula: BO score = (initial score + 8) * 3. In patients scoring 2 to <7 points (n = 112), 7 to <11 points (n = 580), 11 to <15 points (n = 339), and 15 to 20 points (n = 18), 1 year survival was 93.1%, 84.2%, 66.9%, and 27.8%, respectively. The c-index of our score was 0.73 [95% confidence interval (CI): 0.69-0.77]. Values were in our cohort for the US and French scores 0.66 (95% CI: 0.62-0.70) and 0.63 (95% CI: 0.59-0.67), respectively.

CONCLUSIONS

Data indicate that our score, but also risk assessment tools from other transplant regions, may be used as a reliable support for risk-adjusted organ allocation and potentially help to improve outcomes in heart transplantation. Further developments will have to include as yet unaccounted risk factors for even more reliable predictions.

Using machine learning to improve survival prediction after heart transplantation

BACKGROUND

This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transplantation (OHT).

METHODS

Retrospective study of adult patients undergoing primary, isolated OHT between 2000 and 2019 as identified in the United Network for Organ Sharing (UNOS) registry. The primary outcome was 1-year post-transplant survival. Patients were randomly divided into training (80%) and validation (20%) sets. Dimensionality reduction and data re-sampling were employed during training. Multiple machine learning algorithms were combined into a final ensemble ML model. The discriminatory capability was assessed using the area under receiver-operating-characteristic curve (AUROC), net reclassification index (NRI), and decision curve analysis (DCA).

RESULTS

A total of 33,657 OHT patients were evaluated. One-year mortality was 11% (n = 3738). In the validation cohort, the AUROC of singular logistic regression was 0.649 (95% CI, 0.628-0.670) compared to 0.691 (95% CI, 0.671-0.711) with random forest, 0.691 (95% CI, 0.671-0.712) with deep neural network, and 0.653 (95% CI, 0.632-0.674) with Adaboost. A final ensemble ML model was created that demonstrated the greatest improvement in AUROC: 0.764 (95% CI, 0.745-0.782) (p < .001). The ensemble ML model improved predictive performance by 72.9% ±3.8% (p < .001) as assessed by NRI compared to logistic regression. DCA showed the final ensemble method improved risk prediction across the entire spectrum of predicted risk as compared to all other models (p < .001).

CONCLUSIONS

Modern ML techniques can improve risk prediction in OHT compared to traditional approaches. This may have important implications in patient selection, programmatic evaluation, allocation policy, and patient counseling and prognostication.

Appraising prediction research: a guide and meta-review on bias and applicability assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)

Over the past few years, a large number of prediction models have been published, often of poor methodological quality. Seemingly objective and straightforward, prediction models provide a risk estimate for the outcome of interest, usually based on readily available clinical information. Yet, using models of substandard methodological rigour, especially without external validation, may result in incorrect risk estimates and consequently misclassification. To assess and combat bias in prediction research the prediction model risk of bias assessment tool (PROBAST) was published in 2019. This risk of bias (ROB) tool includes four domains and 20 signalling questions highlighting methodological flaws, and provides guidance in assessing the applicability of the model. In this paper, the PROBAST will be discussed, along with an in‐depth review of two commonly encountered pitfalls in prediction modelling that may induce bias: overfitting and composite endpoints. We illustrate the prevalence of potential bias in prediction models with a meta‐review of 50 systematic reviews that used the PROBAST to appraise their included studies, thus including 1510 different studies on 2104 prediction models. All domains showed an unclear or high ROB; these results were markedly stable over time, highlighting the urgent need for attention on bias in prediction research. This article aims to do just that by providing (1) the clinician with tools to evaluate the (methodological) quality of a clinical prediction model, (2) the researcher working on a review with methods to appraise the included models, and (3) the researcher developing a model with suggestions to improve model quality.

Most published prediction models have limited clinical uptake, are not externally validated and come with methodological issues. The PROBAST (Prediction model Risk Of Bias ASssessment Tool) guides the researcher writing a review, or the clinician interested in a model for risk calculation in a clinical setting. This review examines the aspects of bias in prediction research, and provides information on the prevalence of bias in published models.

Flavin Mononucleotide as a Biomarker of Organ Quality-A Pilot Study

BACKGROUND

Flavin mononucleotide (FMN), released from damaged mitochondrial complex I during hypothermic liver perfusion, has been shown to be predictive of 90-day graft loss. Normothermic machine perfusion (NMP) and normothermic regional perfusion (NRP) are used for organ reconditioning and quality assessment before transplantation. This pilot study aimed to investigate the changes of FMN levels during normothermic reperfusion of kidneys, livers, and lungs and examine whether FMN could serve as a biomarker to predict posttransplant allograft quality.

METHODS

FMN concentrations, in perfusates collected during NMP of kidneys, abdominal NRP, and ex vivo lung perfusion, were measured using fluorescence spectrometry and correlated to the available perfusion parameters and clinical outcomes.

RESULTS

Among 7 transplanted kidneys out of the 11 kidneys that underwent NMP, FMN levels at 60 minutes of NMP were significantly higher in the allografts that developed delayed graft function and primary nonfunction (P = 0.02). Fifteen livers from 23 circulatory death donors that underwent NRP were deemed suitable for transplantation. Their FMN levels at 30 minutes of NRP were significantly lower than those not procured for transplantation (P = 0.004). In contrast, little FMN was released during the 8 lung perfusions.

CONCLUSIONS

This proof of concept study suggested that FMN in the perfusates of kidney NMP has the potential to predict posttransplant renal function, whereas FMN at 30 minutes of NRP predicts whether a liver would be accepted for transplantation. More work is required to validate the role of FMN as a putative biomarker to facilitate safe and reliable decision-making before embarking on transplantation.