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\begin{document}

\title{Using Machine Learning to Improve Survival Prediction After Heart
Transplantation}



\author[1]{Brian Ayers}%
\author[2]{Tuomas Sandholm}%
\author[3]{Igor Gosev}%
\author[3]{Sunil Prasad}%
\author[4]{Arman Kilic}%
\affil[1]{Massachusetts General Hospital}%
\affil[2]{Carnegie Mellon University}%
\affil[3]{University of Rochester Medical Center}%
\affil[4]{Medical University of South Carolina}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
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 2000-2019 as identified in the
United Network for Organ Sharing (UNOS) registry. The primary outcome
was one-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. Discriminatory capability was assessed using 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=3,738). 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\textless{}0.001). The ensemble
ML model improved predictive performance by 72.9\% \selectlanguage{ngerman}±3.8\%
(p\textless{}0.001) 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\textless{}0.001). 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.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Using Machine Learning to Improve Survival Prediction After
Heart Transplantation}

\textbf{Authors:} Brian Ayers MD MBA\textsuperscript{1}, Tuomas Sandholm
PhD\textsuperscript{2}, Igor Gosev MD PhD\textsuperscript{3}, Sunil
Prasad MD\textsuperscript{3}, Arman Kilic MD\textsuperscript{4}

\textbf{Affiliations:}

\textsuperscript{1} Department of Surgery, The Massachusetts General
Hospital, Boston, MA

\textsuperscript{2} Carnegie Mellon University, Pittsburgh, PA

\textsuperscript{3} Division of Cardiac Surgery, The University of
Rochester Medical Center, Rochester, NY

\textsuperscript{4} Division of Cardiothoracic Surgery, Medical
University of South Carolina, Charleston, SC

\textbf{Short Title:} Machine Learning Heart Transplant

\textbf{Word Count:} 3,516

\textbf{Subject Terms:} heart failure; transplantation; complications;
mortality/survival

\textbf{Meeting Presentation:} American Heart Association Scientific
Sessions 2020 (virtual meeting)

\textbf{Funding:} None

\textbf{Address for correspondence:}

Arman Kilic, M.D.

Division of Cardiothoracic Surgery

Medical University of South Carolina

30 Courtenay Drive

MSC 295, Suite BM279

Charleston, SC 29425

Email: kilica@musc.edu

\textbf{Abstract:}

\textbf{Background:} This study investigates the use of modern machine
learning (ML) techniques to improve prediction of survival after
orthotopic heart transplantation (OHT).

\textbf{Methods:} Retrospective study of adult patients undergoing
primary, isolated OHT between 2000-2019 as identified in the United
Network for Organ Sharing (UNOS) registry. The primary outcome was
one-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.
Discriminatory capability was assessed using area under
receiver-operating-characteristic curve (AUROC), net reclassification
index (NRI), and decision curve analysis (DCA).

\textbf{Results:} A total of 33,657 OHT patients were evaluated.
One-year mortality was 11\% (n=3,738). 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\textless{}0.001). The ensemble ML model improved
predictive performance by 72.9\% \selectlanguage{ngerman}±3.8\% (p\textless{}0.001) 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\textless{}0.001).

\textbf{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.

\emph{Words: 248}

\textbf{Non-Standard Abbreviations and Acronyms}

AUROC: area under receiver-operating-characteristic curve

DCA: decision curve analysis

DRI: donor risk index

IHTSA: international heart transplant survival algorithm

IMPACT: index for mortality prediction after cardiac transplantation

ML: machine learning

NRI: net reclassification index

OHT: orthotopic heart transplantation

RSS: risk stratification score

STS: Society of Thoracic Surgeons

UNOS: United Network for Organ Sharing



\textbf{Introduction}

The clinical management of end-stage heart disease is a continually
evolving practice that has changed drastically over the last decade.
Survival after orthotopic heart transplantation (OHT) has continued to
improve.\textsuperscript{1} Despite improving longevity after
transplant, donor organ supply remains inadequate to meet
demand,\textsuperscript{2} and transplant programs face increased public
and private scrutiny of their
outcomes.\textsuperscript{3}Simultaneously, technologic innovations in
mechanical circulatory support platforms have demonstrated parallel
improvement in clinical outcomes,\textsuperscript{4-6} thus increasing
the potential alternative viable treatment options for heart failure
patients. Therefore, an accurate prognostic model using pre-operative
data for individualized donor and recipient selection would be of
profound clinical utility in OHT.

Prior risk models for predicting survival after OHT have displayed only
modest discriminatory capability. Examples of such algorithms include
The Donor Risk Index (DRI),\textsuperscript{7} Risk Stratification Score
(RSS),\textsuperscript{8} Index for Mortality Prediction After Cardiac
Transplantation (IMPACT)\textsuperscript{9} and International Heart
Transplant Survival Algorithm (IHTSA)\textsuperscript{10}. With
increasing interest in the application of machine learning (ML) to
predictive analytics in clinical medicine,\textsuperscript{11} we aimed
to evaluate whether modern ML techniques could improve risk prediction
in OHT.

\textbf{Material and Methods}

\emph{Patient Population}

Because of the sensitive nature of the data collected for this study,
requests to access the dataset from qualified researchers trained in
human subject confidentiality protocols may be sent to the United
Network for Organ Sharing (UNOS). We conducted a retrospective review of
prospectively collected data in the UNOS database. The database was
queried for all patients that underwent orthotopic heart transplantation
(OHT) between 2000-2019. Patients were excluded if they underwent
transplantation before the year 2000, were less than 18 years old, had a
history of prior heart transplantation, underwent multiorgan transplant,
or had incomplete survival status at one year. This retrospective
analysis of deidentified data was deemed except from Institutional
Review Board approval and patient consent was not required.

\emph{Training and Validation Cohorts}

Patients were randomly split into training (80\%) and validation (20\%)
cohorts, ensuring equal distribution of the primary outcome in each
cohort. This method of stratifying by primary outcome before randomly
assigning patients to a cohort ensures an equal distribution of
mortality between training and validation datasets to avoid biasing
final model performance. The training cohort was used for feature
selection, dimensionality reduction, and machine learning model
development, keeping the validation cohort entirely separate and unseen
until assessment of the final model performance.

\emph{Data Preparation and Feature Selection}

All variables in the UNOS database available for the OHT patients were
manually reviewed by two independent clinicians (N=525 variables).
Variables were excluded if they were redundant, free text, or would not
be available in the preoperative setting. Variables with more than 20\%
missing data were also excluded. The distribution of data for each
remaining categorical variable were again manually reviewed and grouped
into clinically meaningful categories for each variable by two
independent physicians. This step decreases data sparsity by grouping
low incidence characteristics into fewer, clinically meaningful
categories. Missing continuous variable data were imputed using feature
median and missing categorical data were imputed with the feature mode.
Continuous variables were standardized to have a mean of zero and
standard deviation of one. Categorical variables were one-hot encoded
ensuring no linear dependencies between columns.

\emph{Feature Importance and Dimensionality Reduction}

In order to increase the interpretability of the model, we first
performed a univariate estimate of feature importance. A random forest
classifier was trained with 10-fold cross-validation repeated three
times. Univariate feature importance was estimated using the mean
decrease in accuracy. Three separate feature importance models were
developed first with continuous variables only (N=37), then with
categorical variables (N=195), and then with all the variables (N=232).
This was done to account for the known tendency for random forest
classifiers to more heavily weight continuous variables in feature
importance estimates. The 20 most important features from each model
were combined into a single dataset, consisting of 47 variables after
excluding duplicates. This variable set was subsequently used to develop
the machine learning algorithms. Overall, this methodology of feature
selection allows us to combine the best of both manual filtering based
on clinical acumen and automated methods using machine learning
techniques. Decreasing the input space used for the final machine
learning model is a critical step that has been shown to improve overall
model performance by decreasing tendency to overfit and increasing
training efficiency.\textsuperscript{12}

\emph{Training Prediction Models}

An ensemble approach was employed in order to create a more stable and
reliable model resistant to outliers. Four different types of algorithms
were used: deep neural network, logistic regression, adaboost, and
random forest. We employed over-sampling (SVM Smote) and under-sampling
(Repeated Edited Nearest Neighbors) of the training data in order to
better balance the primary outcome within the dataset. For each type of
algorithm, 100 different models were trained using varying degrees of
data re-sampling to produce variability in each model's underlying
training data. The resulting 400 algorithms were subsequently combined
into the final ensemble prognostic model.

\emph{Validation and Comparison}

Once the models were trained, discriminatory capability was assessed
using the previously unseen validation data. The performance of the full
ensemble model (400 algorithms) was compared to that of each type of
algorithm individually (100 algorithms each), as well as a single
logistic regression on its own. Model capability was assessed using area
under receiver-operating-characteristic curve (AUROC), net
reclassification index (NRI), and decision curve analysis (DCA).
Calibration of the model was evaluated using visual plots of predicted
risk based on the training cohort versus observed risk in the validation
cohort stratified by decile of risk. Two-sided p-value of less than 0.05
was considered significant for all comparisons. All models were trained
in python using Keras with Tensorflow.\textsuperscript{13} Performance
outcome comparisons were conducted with Stata (StataCorp. 2015. Stata
Statistical Software: Release 14. College Station, TX: StataCorp LP).

\textbf{Results}

\emph{Study Cohort}

Of the 33,657 patients included in the study, 3,728 (11\%) experienced
the primary outcome of death within one year (\textbf{Figure 1} ). The
majority of study participants were male (75\%) with an average age of
52.8 ±12.4 years. Balancing by primary outcome, patients were randomly
split into 80\% training cohort and 20\% validation cohort. Patient
characteristics were similar between cohorts, including age, sex,
etiology of heart failure, end-organ function, pre-transplant mechanical
circulatory support, and days spent on waiting list (\textbf{Table 1} ).

\emph{Feature Importance and Dimensionality Reduction}

After manual and automated variable filtering, the relative importance
of each feature in predicting the primary outcome was assessed using a
random forest classifier. Continuous and categorical variables were
assessed both separately and together to reduce biases
(\textbf{Supplemental Figure 1} ). In the combined assessment, the most
important features included total bilirubin, mechanical ventilation, no
ventricular assist device at transplant, serum creatinine, donor age,
recipient height, donor cerebrovascular mechanism of death, prior
cardiac surgery, and Karnofsky functional status. The top 20 most
important features from each individual assessment were then combined
excluding duplicates, resulting in 47 training variables that were used
for subsequent model training (\textbf{Supplemental} \textbf{Table 1} ).

\emph{Model Training and Performance Comparison}

A total of 400 algorithms were trained using varying subsets of training
data based on randomly stratified levels of over and under resampling of
the training dataset (\textbf{Figure 2} ). The final ensemble ML model
contained all 400 underlying algorithms, while smaller sized ensemble
models were also combined using the 100 iterations of each type of
algorithm individually for comparison. The optimal model performance was
the complete ensemble ML model (\textbf{Figure 3} ), outperforming all
other models with an AUROC of 0.764 (95\% CI, 0.745-0.782)
(p\textless{}0.001). By comparison, the singular logistic regression
model had an AUROC of 0.649 (95\% CI, 0.628-0.670). Additionally, the
final ensemble ML model demonstrated an improvement of 72.9\% ±3.8\%
(p\textless{}0.001) in predictive performance as assessed by net
reclassification index compared to logistic regression. The decision
curve analysis showed the final ensemble method improved risk prediction
across the entire spectrum of predicted risk as compared to all other
models (\textbf{Figure 4} , p\textless{}0.001). The final ensemble ML
model was well-calibrated, with the majority of observed risk in the
validation cohort falling within range of predicted risk based on the
training cohort after stratifying into deciles of risk
(\textbf{Supplemental Figure 2} ).

\textbf{Conclusions}

Prognostication of clinical outcomes after OHT has profound importance
in patient selection and organ allocation. The present study
demonstrates the potential utility of employing modern ML techniques to
improve prognostic model performance at an individual patient level. A
final ensemble ML model using only preoperative variables outperformed
all other comparison algorithms in predicting one-year survival,
achieving improved performance by a variety of metrics including AUROC,
net reclassification index, and decision curve analysis. Further, the
model demonstrated appropriate calibration.

Cardiac surgery as a field has historically been an early adopter of
clinical prognostic models,\textsuperscript{14} most notably the widely
used Society of Thoracic Surgeons (STS) Short-Term Risk
Calculators.\textsuperscript{15} However, predicting one-year mortality
after OHT has remained a persistent challenge. Early OHT risk models
incorporated a select number of variables with only modest overall
performance.\textsuperscript{7,8,16} More recent models have added an
increasing number of variables into more robust models, such as the
IMPACT\textsuperscript{9} and IHTSA\textsuperscript{10}, but have been
able to achieve only slight improvements in discriminatory performance.
In part, this may relate to the challenges in capturing all granular and
potentially predictive elements of post-transplant survival in a
multicenter registry. For example, factors such as anti-rejection
medication compliance are not assessed but can have important
implications in survival following transplant. Also, there is a
trade-off in assessing longer term outcomes, such that the event rate
will be higher but the impact of pre-operative risk factors on that
outcome will likely diminish as longer-term factors weight more heavily
into outcome prediction.

Machine learning techniques have demonstrated clinical utility in a
number of different fields.\textsuperscript{17-21} Within OHT, the IHTSA
score itself employs an artificial neural network approach, and has
consistently demonstrated some of the highest discriminatory values out
of all current models in recent studies.\textsuperscript{22} Moreover, a
recent study recalibrated both the IMPACT and IHTSA models to use only
the same subset of variables, and found the deep learning approach was
superior.\textsuperscript{23} The recent Trees of Predictors model is
also an innovative approach that identifies clusters of patients with
similar characteristics, and develops machine learning predictive models
specifically for each cluster.\textsuperscript{24} The success of this
approach demonstrates the potential for developing very individualized
prognostic scores, at the risk of overfitting the model to specific
retrospective cohorts that may not translate to prospective clinical
practice.

The final ensemble ML model we developed in the present study is an
example of using both clinical acumen and automated machine learning to
develop a robust model from a large clinical registry. The statistical
adage of ``garbage in produces garbage out'' remains especially true for
machine learning approaches.\textsuperscript{25} It is particularly
relevant for black box algorithms when used clinically, as there is low
interpretability for clinicians in terms of how the algorithm arrives at
its final prognosis. Moreover, registry data is particularly prone to
reporting inaccuracies and missing data, resulting in poor prognostic
ability if machine learning approaches are applied without sufficient
data preparation.\textsuperscript{26} Our approach was to combine both
expert clinician manual review of the variables with automated feature
selection techniques in order to arrive at the final set of variables.
While time-consuming, we believe this collaborative approach is
necessary in order to derive utility from registry level data. Moreover,
while computationally more expensive, the ensemble machine learning
approach allows for the integration of multiple types of algorithms into
one cohesive model, which has been suggested to produce a more robust
final product.\textsuperscript{27}

While this is not the first study to employ machine learning techniques
for OHT prediction, it describes the use of more robust feature
selection techniques and the development of a larger scale ensemble ML
model than has been previously reported. This example of applying modern
techniques may help to overcome the registry-level data limitations that
have hindered prior studies.

This study has several limitations that need to be considered when
interpreting the results. First, it is retrospective in nature and
subject to all inherent limitations of such studies. Most notably, there
have been a number of substantial changes in the allocation system and
clinical management of OHT patients over the timeframe encapsulated by
the study period. As such, there is associated bias as risk models
including the one developed in the current study cannot account for
individual provider or transplant program decision-making. Second, the
UNOS database, similar to other multicenter registries, has a number of
limitations including variability in data reporting and quality. As
such, assumptions are made for missing data that may introduce bias and
there may be clinically important variables not captured in the
available dataset. Finally, while we created a randomly selected
validation cohort at the outset of the study, an independent validation
cohort separate from the UNOS database was not available for testing.
Further study is warranted on independent, prospective data not present
in the current dataset in order to provide more comprehensive validation
testing of the final model.

In conclusion, an ensemble ML model was able to achieve greater
predictive performance as compared to individual ML models and logistic
regression in predicting survival after OHT. This analysis demonstrates
the potential of modern ML techniques in risk prediction for OHT. These
approaches may have important implications in patient selection,
programmatic evaluation, policy-making, and patient counseling in OHT.

\textbf{Acknowledgements and Disclosures}

The data reported here have been supplied by the United Network for
Organ Sharing as the contractor for the Organ Procurement and
Transplantation Network. The interpretation and reporting of these data
are the responsibility of the authors and in no way should be seen as an
official policy of or interpretation by the OPTN or the U.S. Government.
Arman Kilic is on the Medical Advisory Board for Medtronic, Inc. Igor
Gosev is a consultant for Abbott, Inc. Sunil Prasad is on the Scientific
Advisory Board for Abbott, Inc.

\textbf{References:}

1. Chambers DC, Cherikh WS, Harhay MO, et al. The International Thoracic
Organ Transplant Registry of the International Society for Heart and
Lung Transplantation: Thirty-sixth adult lung and heart-lung
transplantation Report-2019; Focus theme: Donor and recipient size
match. \emph{J Heart Lung Transplant.} 2019;38(10):1042-1055.

2. Hsich EM. Matching the Market for Heart Transplantation. \emph{Circ
Heart Fail.} 2016;9(4):e002679.

3. Jay C, Schold JD. Measuring transplant center performance: The goals
are not controversial but the methods and consequences can be.\emph{Curr
Transplant Rep.} 2017;4(1):52-58.

4. Mehra MR, Uriel N, Naka Y, et al. A Fully Magnetically Levitated Left
Ventricular Assist Device - Final Report. \emph{N Engl J
Med.}2019;380(17):1618-1627.

5. Fernandez FG, Shahian DM, Kormos R, et al. The Society of Thoracic
Surgeons National Database 2019 Annual Report. \emph{Ann Thorac
Surg.}2019.

6. Milano CA, Rogers JG, Tatooles AJ, et al. HVAD: The ENDURANCE
Supplemental Trial. \emph{JACC Heart Fail.} 2018;6(9):792-802.

7. Weiss ES, Allen JG, Kilic A, et al. Development of a quantitative
donor risk index to predict short-term mortality in orthotopic heart
transplantation. \emph{J Heart Lung Transplant.} 2012;31(3):266-273.

8. Hong KN, Iribarne A, Worku B, et al. Who is the high-risk recipient?
Predicting mortality after heart transplant using pretransplant donor
and recipient risk factors. \emph{Ann Thorac Surg.} 2011;92(2):520-527;
discussion 527.

9. Weiss ES, Allen JG, Arnaoutakis GJ, et al. Creation of a quantitative
recipient risk index for mortality prediction after cardiac
transplantation (IMPACT). \emph{Ann Thorac Surg.} 2011;92(3):914-921;
discussion 921-912.

10. Nilsson J, Ohlsson M, Höglund P, Ekmehag B, Koul B, Andersson B. The
International Heart Transplant Survival Algorithm (IHTSA): a new model
to improve organ sharing and survival. \emph{PLoS
ONE.}2015;10(3):e0118644.

11. Obermeyer Z, Emanuel EJ. Predicting the Future --- Big Data, Machine
Learning, and Clinical Medicine. \emph{N Engl J
Med.}2016;375(13):1216-1219.

12. Cai J, Luo J, Wang S, Yang S. Feature selection in machine learning:
A new perspective. \emph{Neurocomputing.} 2018;300:70-79.

13. Gulli A, Pal S. \emph{Deep Learning with Keras.} Packt Publishing;
2017.

14. Geissler HJ, Hölzl P, Marohl S, et al. Risk stratification in heart
surgery: comparison of six score systems. \emph{Eur J Cardiothorac
Surg.} 2000;17(4):400-406.

15. O'Brien SM, Feng L, He X, et al. The Society of Thoracic Surgeons
2018~Adult Cardiac Surgery Risk Models:~Part~2-Statistical Methods
and~Results. \emph{Ann Thorac Surg.} 2018;105(5):1419-1428.

16. Kilic A, Weiss ES, Allen JG, et al. Simple score to assess the risk
of rejection after orthotopic heart transplantation.
\emph{Circulation.}2012;125(24):3013-3021.

17. Cucchetti A, Vivarelli M, Heaton ND, et al. Artificial neural
network is superior to MELD in predicting mortality of patients with
end-stage liver disease. \emph{Gut.} 2007;56(2):253-258.

18. Ayers B, Wood K, Gosev I, Prasad S. Predicting Survival after
Extracorporeal Membrane Oxygenation using Machine Learning. \emph{Ann
Thorac Surg.} 2020.

19. Kilic A. Artificial Intelligence and Machine Learning in
Cardiovascular Health Care. \emph{Ann Thorac
Surg.}2020;109(5):1323-1329.

20. Kilic A, Goyal A, Miller JK, et al. Predictive Utility of a Machine
Learning Algorithm in Estimating Mortality Risk in Cardiac
Surgery.\emph{Ann Thorac Surg.} 2020;109(6):1811-1819.

21. Kilic A, Goyal A, Miller JK, Gleason TG, Dubrawksi A. Performance of
a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve
Replacement. \emph{Ann Thorac Surg.} 2020.

22. Aleksova N, Alba AC, Molinero VM, et al. Risk prediction models for
survival after heart transplantation: A systematic review. \emph{Am J
Transplant.} 2020;20(4):1137-1151.

23. Medved D, Ohlsson M, Höglund P, Andersson B, Nugues P, Nilsson J.
Improving prediction of heart transplantation outcome using deep
learning techniques. \emph{Sci Rep.} 2018;8(1):3613.

24. Yoon J, Zame WR, Banerjee A, Cadeiras M, Alaa AM, van der Schaar M.
Personalized survival predictions via Trees of Predictors: An
application to cardiac transplantation. \emph{PLoS
ONE.}2018;13(3):e0194985.

25. Adibuzzaman M, DeLaurentis P, Hill J, Benneyworth BD. Big data in
healthcare - the promises, challenges and opportunities from a research
perspective: A case study with a model database. \emph{AMIA Annu Symp
Proc.} 2017;2017:384-392.

26. Miller PE, Pawar S, Vaccaro B, et al. Predictive Abilities of
Machine Learning Techniques May Be Limited by Dataset Characteristics:
Insights From the UNOS Database. \emph{J Card Fail.} 2019;25(6):479-483.

27. Granitto PM, Verdes PF, Ceccatto HA. Neural network ensembles:
evaluation of aggregation algorithms. \emph{Artificial
Intelligence.}2005;163(2):139-162.



\textbf{Figure Legends:}

\textbf{Figure 1.} Study cohort.

\textbf{Figure 2.} Schematic of final ensemble ML model training and
internal validation methodology.

\textbf{Figure 3.} Area under receiver-operating-characteristic curve
comparison of each model's prognostic ability.

\textbf{Figure 4.} Decision curve analysis demonstrating improved risk
prediction of the full ensemble ML model across the entire spectrum of
predicted risk scores.

\textbf{Table 1. Patient Characteristics of Training and Testing
Cohorts}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Training} \textbf{(n=26,926)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{\% or SD}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Validation (n=6,731)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Validation (n=6,731)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{\% or SD}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{p-value}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Recipient}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Recipient}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Age (years)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
52.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12.5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12.5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
52.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.873\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Female\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6683\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1706\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.378\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Height (cm)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
173.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
173.7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.099\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
BMI (kg/m\textsuperscript{2})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
27.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
27.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.453\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
White\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
18819\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
70\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
70\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4723\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
70\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.666\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cardiomyopathy\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Non-ischemic\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12815\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
48\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
48\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3239\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
48\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.445\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Ischemic\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
10986\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
41\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
41\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2703\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
40\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.339\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Congenital\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
715\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
196\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.257\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Restrictive\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
670\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
171\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.793\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Valvular\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
509\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
130\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.842\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Hypertrophic\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
580\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
152\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.607\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Prior Cardiac Surgery\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12012\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
45\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
45\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2975\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
44\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.546\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
History of Dialysis\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
596\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
169\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.144\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Karnofsky Functional Status \textgreater{}70\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6748\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1704\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.671\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Support: Ventilator\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
506\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
115\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.389\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Support: ICU\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7789\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1967\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.631\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Support: Inotropes\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
11037\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
41\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
41\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2694\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
40\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.149\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Support: ECMO\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
127\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
38\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.329\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Support: IABP\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1576\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
379\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.503\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Mechanical Circulatory Support\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
LVAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7465\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1908\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
28\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.309\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
RVAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
50\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.419\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
TAH\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
208\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
43\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.268\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
LVAD+RVAD\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
599\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
149\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Total Bilirubin\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.271\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Creatinine\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.713\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Total days on waiting list\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
224.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
369.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
369.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
225.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
369.2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.836\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Donor}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Donor}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Age (years)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
31.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
11.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
11.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
31.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.413\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Female\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7830\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
29\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1986\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
30\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.491\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Height (cm)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
174.3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
174.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.290\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
BMI (kg/m\textsuperscript{2})\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
26.9\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
5.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
5.6\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
27.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
5.8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.336\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Mechanism of Death\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Trauma\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
14513\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
54\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
54\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3634\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
54\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.902\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cerebrovascular\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6859\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
25\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1741\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
26\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.512\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Drug Overdose\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1892\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
456\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.487\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Other\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3662\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
14\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
14\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
900\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
13\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.633\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Blood Infection\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2081\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
8\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
8\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
486\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.166\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Other}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Other}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Sex matched\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
19891\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
66\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
66\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
4987\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
74\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.721\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Ischemic time (hours)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3.2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3.2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.0\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.493\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Blood Type Incompatible\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3931\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
15\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
15\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
966\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
14\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.615\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Transplant year 2000-2010\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
13149\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
49\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
49\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3365\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.091\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

BMI: body mass index; IABP: intra-aortic balloon pump; ICU: intensive
care unit; ECMO: extracorporeal membrane oxygenation; LVAD: left
ventricular assist device; MCS: mechanical circulatory support; RVAD:
right ventricular assist device; TAH: total artificial heart\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

