Predictive performance of six mortality risk scores and the development of a novel model in a prospective cohort of patients undergoing valve surgery secondary to rheumatic fever
Predictive performance of six mortality risk scores and the development of a novel model in a prospective cohort of patients undergoing valve surgery secondary to rheumatic fever
Omar A. V. Mejia 0 1
Manuel J. Antunes 0
Maxim Goncharov 0 1
LuÂõs R. P. Dallan 0 1
Elinthon Veronese 0 1
Gisele A. Lapenna 0 1
Luiz A. F. Lisboa 0 1
LuÂõs A. O. Dallan 0 1
Carlos M. A. Brandão 0 1
Jorge Zubelli 0
FlaÂ vio Tarasoutchi 0 2
Pablo M. A. Pomerantzeff 0 1
Fabio B. Jatene 0 1
0 Editor: Markus M. Bachschmid, Boston University , UNITED STATES
1 Department of Thoracic and Cardiovascular Surgery, Heart Institute±University of São Paulo Medical Center , São Paulo , Brazil , 2 Center of Cardiothoracic Surgery, University Hospital and Faculty of Medicine , Coimbra , Portugal , 3 National Institute for Pure and Applied Mathematics , Rio de Janeiro, RJ , Brazil
2 Department of the Clinical Unit of Heart Valve Diseases, Heart Institute±University of São Paulo Medical Center , São Paulo , Brazil
Data Availability Statement: The data underlying
this study cannot be made available due to ethical
restrictions; patients did not consent to their
deidentified data being publicly shared. De-identified
data can be made available to qualified researchers
under their responsibility and assuming the
penalties if public disclosure of the data. Data
requests should be sent to Renata do Val, Director
of the Scientific Committee, Ethics Committee of
the Heart InstituteÐUniversity of São Paulo
We conducted prospective consecutive all-comers patients with rheumatic heart disease
(RHD) referred for surgical treatment of valve disease between May 2010 and July of 2015.
Risk scores for hospital mortality were calculated using the 2000 Bernstein-Parsonnet,
EuroSCORE II, InsCor, AmblerSCORE, GuaragnaSCORE, and the New York SCORE. In
addition, we developed the rheumatic heart valve surgery score (RheSCORE).
A total of 2,919 RHD patients underwent heart valve surgery. After evaluating 13 different
models, the top performing areas under the curve were achieved using Random Forest
(0.982) and Neural Network (0.952). Most influential predictors across all models included
comissao-cientifica/158-fale-conosco ) or Prof.
Dr. Alfredo JoseÂ Mansur, Coordinator, Comissão
de EÂtica para AnaÂlise de Projetos de PesquisaÐ
CAPPesq (, http://
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
left atrium size, high creatinine values, a tricuspid procedure, reoperation and pulmonary
hypertension. Areas under the curve for previously developed scores were all below the
performance for the RheSCORE model: 2000 Bernstein-Parsonnet (0.876), EuroSCORE II
(0.857), InsCor (0.835), Ambler (0.831), Guaragna (0.816) and the New York score (0.834).
A web application is presented where researchers and providers can calculate predicted
mortality based on the RheSCORE.
The RheSCORE model outperformed pre-existing scores in a sample of patients with
rheumatic cardiac disease.
Approximately 80% of countries worldwide present with rheumatic fever (RF) and with one of
its most prevalent complications, the rheumatic heart disease (RHD). People presenting
advanced RHD without access to cardiac surgery die .
An improvement in our ability to predict who the best surgical candidates might be can
partially account for recent improvements in mortality rates after cardiac procedures. This
prediction is frequently accomplished through risk scores. As a consequence, numerous risk
scores have been developed over time [2±5]. Although the widespread use of risk scores is
deemed to be a sign of improvement in our clinical decision support system, clinicians often
fail to notice that the performance of a given risk score only remains adequate under certain
conditions. For example, if the sample which validated the risk score was different from the
patient population where it is being applied, then prediction performance could be
compromised, ultimately resulting in misleading clinical decisions.
Around the world, there are over 15 million people with RHD accompanied by 300,000
new cases per year and over 200,000 annual deaths . The public health system of Brazil
spends over 90 million dollars a year to treat patients with RF and RHD, thus the creation of a
task force for the prevention  and improvement of quality initiatives for the surgical
treatment of patients with RHD such as repair rather than replacement of diseased valves because
of renowned consequences . However, RHD damages the valve leaflets and the subvalvular
apparatus, making the repair more difficult. For these reasons, proper risk assessment for
purposes of informed consent and the determination of current treatment in these patients is
important because the traditional risk scores often emerge from non-rheumatic populations.
Given that most risk scores to date were developed and validated mostly among patients in
developed countries, it is questionable whether their predictive performance would still be
optimal when applied to rheumatic patients. Unfortunately, we are not aware of any previous
scores validated among a large, prospective sample exclusively composed of patients with a
diagnosis of rheumatic valve disease.
In the face of this gap in the literature, our study aimed to evaluate the predictive
performance of six different risk scores: the 2000 Bernstein-Parsonnet, EuroSCORE II, InsCor,
Ambler, Guaragna and the New York scores. In addition, we developed the RheSCORE
model, optimized for mortality risk prediction among patients with rheumatic valve disease. It
was our hypothesis that a prediction model specifically designed to be used among patients
with rheumatic valve disease would outperform previously existing scores.
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Materials and methods
This study is a prospective consecutive all-comers cohort of patients referred to the
Department of Thoracic and Cardiovascular Surgery, Heart Institute±University of São Paulo
Medical Center, São Paulo, Brazil.
Between May 2010 and July of 2015, a total of 2,919 consecutive all-comers patients with RHD
referred for surgical treatment of valve disease. Symptomatic RHD was characterized by 2004
World Health Organization criteria for the diagnosis of first onset, recurrence and chronic
RHD (modified Jones criteria) and transthoracic echocardiography. Patients were excluded
from the study if the primary diagnosis of the valvular disease was not RHD or if they were
undergoing associated procedures such as myocardial revascularization, ASD closure, thoracic
aorta procedures, etc. The ESC/EACTS 2012 guideline was used for surgical indication of
valvular heart disease. All data were extracted from the general prospective institutional register
(Si3) and stored in compliance with institutional security and privacy governance rules. To
ensure data accuracy, quality checks were performed over time by the postgraduate student
and the supervisors (authors).
Our choice of risk scores was defined by a consensus among participating surgeons, decisions
being made on the basis of their methodology and popularity in the literature as well as
applicability (Table 1). Clinical and laboratory-related variables for the 2000 Bernstein Parsonnet
, EuroSCORE II , InsCor , Ambler , New York  and Guaragna  (Table 2)
were prospectively collected and subsequently scored according to the criteria and definitions
stipulated by their developers as well as for our new proposed model, RheSCORE.
The outcome variable of interest was hospital mortality, defined as death in the hospital or
within 30 days of cardiac surgery.
Sample characteristics Score validation
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Multicenter (10 centers)/New Retrospective; 8593
Multicenter (154 centers) / Prospective:22381
European & Non-European patients
Single center/Brazil Prospective:2000
Multicenter (New York Retrospective:10702
Multicenter (Great Britain and Prospective:16679
Ireland Database)/England patients
Single center/Brazil Retrospective:699
EuroSCO-RE II InsCor Amblerscore
New York score Guaragnascore RheSCORE
2000 BP EuroSCO-RE II InsCor Amblerscore New York score Guaragnascore RheSCORE
x x x x
We followed international reporting guidelines as well as an expert recommendation in our
modeling strategy. We started the analysis by performing a graphical exploratory analysis
evaluating the frequency, percentage and near-zero variance for categorical variables, distribution
for numeric variables, and missing values and patterns across all variables. In addition, a
Maximal Information Nonparametric Exploration algorithm was run to guide bivariate plot
inspection. Feature engineering then proceeded by attempting variable transformations and dummy
coding for variables with distributions that were not normal at inspection, variable
recategorization or removal for near-zero variation, and different imputation algorithms for variables
with missing values. We modeled hospital mortality as an outcome variable. To train and test
our models, we used a five-fold model validation. The RheSCORE modeling processed
involved Random Forests, Quadratic Discriminant Analysis, Generalized Linear Model with
binomial distribution family, Linear Discriminant Analysis, Partial Least Squares, Penalized
Logistic Regression, Nearest Shrunken Centroids, Mixture Discriminant Analysis, Neural
Network, Flexible Discriminant Analysis, Support Vector Machines with Radial Basis Function
Kernel, k-Nearest Neighbors and Naive Bayes. Model tuning was performed using the
parameters listed in Table 3.
Comparison across models was performed using metrics for the area under the curve,
sensitivity, specificity, Kappa as well as positive and negative predictive values. All calculations were
performed using the statistical language R, including packages ggplot2, caret, rmarkdown, vcd,
randomforest, MASS, glmnet, mda, pROC, corrplot, and tabplot. Finally, total scores for the
2000 Bernstein-Parsonnet, EuroSCORE II, InsCor, Ambler, Guaragna, and the New York
score were used to predict mortality using logistic regression models under identical validation
criteria used for the RheSCORE model.
Number of Randomly Selected Predictors
No tuning parameters
No tuning parameters
Number of Discriminant Functions
Number of Components
L2 penalty and complexity parameter
Number of subclasses per class
Number of hidden units, weight decay
Product degree and number of terms
Sigma, cost, weight
Maximum number of neighbors, distance, kernel
Laplace correction, distribution type
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This study is part of the project: ªMortality prediction in coronary bypass surgery and/or heart
valve surgery at InCor: Validation of two external risk models and comparison to the locally
developed model (InsCor)º approved with the number 1063/07 (SDC: 3073/07/148) by the
Ethics Committee of the Heart Institute of the Hospital das Clinicas, Medicine School,
University of São Paulo, Brazil. Because our study used a pre-established database, the use of informed
consent forms was waived.
Companion web site
A companion site was designed to contain additional, up-to-date information on the data set,
model as well as a Web Application that can perform mortality predictions based on individual
patient characteristics. The application was developed using the Shiny framework.
A total of 2,919 RHD patients underwent heart valve surgery. A hospital mortality rate of
3,51% was recorded for the entire population. Mortality rates associated with aortic, mitral
and tricuspid surgery were 2,43%, 3,85%, and 7,25% respectively. Our study sample mostly
composed of patients above the age of 50 years, with over 40% having undergone at least one
previous surgical procedure, and with the aortic valve being the most common valve location.
A number of baseline variables were significantly different for the group of patients who died
and those who did not, including lower ejection fraction, pulmonary hypertension,
reoperations, emergency, cardiogenic shock, aortic valve surgery, tricuspid valve surgery, renal failure,
dialysis and high creatinine values (Table 4). A more pronounced heterogeneity demonstrated
Death (N = 99)
54.4 ± 17.4
52.7 ± 15.7
63.4 ± 16.9
43.1 ± 35.2
by increased variability was observed among variables such as pulmonary hypertension,
reoperation and aortic and tricuspid valve surgery procedures (Fig 1A and 1B). In these graphics,
all variables are presented in relation to the distribution of age (left-most column).
Results for bivariate associations with mortality from the MINE analysis, a test used to
detect overall associations, indicated that pulmonary hypertension, left atrium size, high
creatinine, renal failure, tricuspid procedure and aortic valve procedure were the main unadjusted
predictors of mortality according to the Maximal Information Coefficient (Table 5).
During our feature engineering, the following variables were deemed as having high
nearzero variance frequency ratios and percent uniqueness, and despite their clinical relevance,
were eliminated from our final model: emergency surgery, cardiogenic shock, concomitant
valve and revascularization procedure, presence of pacemaker, myocardial infarct within 48
hours from the surgical procedure, dialysis, and renal failure. Since the percentage of missing
values in our cohort was negligible, we opted for not performing imputation.
Results for all 13 models regarding their overall performance are displayed in Table 6, with
the top performing models being Random Forest and Neural Network with areas of 0.982 and
0.952, respectively (Fig 2).
When evaluating the main predictors among our top two models, we observed that the
variables left atrium size, high creatinine, tricuspid procedure, reoperation and pulmonary
hypertension were consistently the most influential ones predicting mortality (Fig 3). Comparison
across model performance was conducted using an area under the curve, where larger values
represent better-combined sensitivity and specificity.
Table 7 summarizes the main finding of our paper by comparing the area under the curve
for the best performing RheSCORE model, the 2000 Bernstein-Parsonnet, EuroSCORE II,
InsCor, Ambler, Guaragna, and the New York score, demonstrating a substantial
improvement in predictive performance in favor of the RheSCORE model making use of Random
Finally, we have published a Web application containing the best performing random forest
model so that healthcare professionals can calculate predicted mortality rates for individual
patients. The application is available at http://www.incor.usp.br/quick/app.html.
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Fig 1. A. Distribution of the sampling variables. Age: age at surgery; eject_fracti: ejection fraction; atrium_size: left atrial size; hypertensio: pulmonary hypertension;
reoperation: number of previous cardiac surgeries; emergency: emergency surgery; cardiac_sh: cardiogenic shock; aortic valve: aortic valve surgery B. Distribution of
sampling variables. Valve_revasc: heart valve surgery and CABG; tricuspid: tricuspid valve surgery; pacemaker: pacemaker dependency; ami48h: acute myocardial
infarction 48h after cardiac surgery; dialysis: renal replacement therapy after cardiac surgery; renal_failure: acute kidney injury after cardiac surgery; high_creatinine:
creatinine levels higher than 2mg/dl.
To the best of our knowledge, this is the first report of a predictive model specifically designed
for patients with rheumatic valve conditions undergoing cardiac procedures, making model
results available not as a score but as a Web application. This Web application is promptly
available to peers as well as to practitioners at the bedside. We have demonstrated that the
RheSCORE model using a random forests algorithm provides a substantially improved predictive
Left atrial size
Left atrial size
Left atrial size
performance over previous scores. We also observed that, among the top performing models,
the following variables were consistently ranked among the most important in predicting
mortality: left atrium size, high creatinine, a tricuspid procedure, a reoperation procedure and the
presence of pulmonary hypertension.
We obtained a better prediction performance with the RheSCORE model than with
traditional scores; traditional scores have been designed with the intention of being simple to
calculate as long as the practitioner could recall their scoring formula at the bedside. Despite their
simplicity, efforts to improve the predictive performance of traditional scores have mostly
Fig 2. Receiver operating curves compared across models.
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Fig 3. Variable importance grid for the top models.
come to a halt in the past decade. Parsonnet  was one of the first authors to analyze
mortality risk factors in a sample of patients only undergoing coronary artery bypass graft surgery.
Eleven years later, a study involving 10,703 patients undergoing coronary artery bypass graft
surgery as well as valve procedures in 10 centers in New Jersey (USA) led to the 2000
Bernstein-Parsonnet score . Given that Parsonnet only involved an American-based sample, the
EuroSCOREs  was subsequently developed with 19,000 patients from 128 European centers,
this score later being reformulated to create EuroSCORE II . With scores now validated in
both European and American populations, our team validated the 2000 Bernstein-Parsonnet
and EuroSCORE scores among a Brazilian group of patients undergoing coronary artery
bypass graft surgery and valve procedures, also generating the new InsCor model . Given
that the InsCor score was specifically designed to address the needs of a patient population
that is essentially different from their American and European counterparts, the InsCor was
the most appropriate of all three . Although there are a number of other scores in the
literature, to our knowledge none of them has substantially improved prediction performance,
including the Ambler score  with an area under the curve of 0.77 in the original
publication and 0.73 in the Brazilian population . Scores specifically designed for patients
undergoing valve procedures have also not achieved substantially greater performance, including
Hannan's score  with an area under the curve of 0.79. To our knowledge, Hannan's score
has not been previously validated in a sample involving patients from developing countries.
Finally, Guaragna published a valve-specific model validated in a Brazilian population ,
with a resulting area under the curve of 0.83 in the original publication and 0.78 in a
subsequent validation . Our current development of the RheSCORE model can be considered as
the next generation in model development, with prediction results that far surpass the ones
from classical scores.
Our finding regarding the importance of left atrial size is aligned with previous reports ,
often surpassing the combination of multiple isolated predictors. For example, in one previous
series evaluating surgical outcome predictors, left atrial size was found to be the primary
outcome predictor, although this association might vanish in the presence of atrial fibrillation
. The importance of left atrial size can be explained since this parameter reflects both the
severity and duration of mitral regurgitation, both of which can significantly affect mortality
Regarding the predictive importance of high creatinine levels, our findings concur with
many previous publications demonstrating its association with high mortality after cardiac
surgery when compared with controls. Of importance, previous findings have demonstrated
that even small serum creatinine changes after surgery can significantly affect mortality, this
association being independent of other well-established perioperative risk indicators .
Our results regarding the importance of tricuspid procedures in predicting mortality align
with previous publications point to these interventions as the second highest risk for mortality
after valvular heart surgery [
]. In a separate series evaluating determinants of surgical
mortality after cardiac surgery, the tricuspid procedure was again shown to be the second highest
determinant of mortality [
] among a selected group of 19 predictors. Although not evaluated
in our study, studies report that mortality rates for re-operated patients undergoing tricuspid
procedures can rise by up to 37% [
], a factor that should be taken into account when
planning procedures as well as discussing potential risks with patients.
Given the increased surgical trauma as well as the underlying reasons leading to a
re-operation, heart valve re-operations are known to be performed with an acceptable operative
mortality with some patient categories presenting elevated risks [
] ultimately underscoring the
need for appropriate risk prediction and stratification in relation to therapeutic options and
Age remains an independent predictor of mortality in this population although a lower
value is associated with rheumatic patients [
]. Our data indicate that the left ventricular
dysfunction, analyzed by LVEF, also is associated with mortality after heart valve surgery in
rheumatic patients. Like the previous report on non-specific rheumatic patients [
], the number
of valve reoperation was an independent predictor of hospital mortality. Finally, as opposed to
previous publications, there was no report of an association between gender and hospital
Despite a significant improvement in predictive performance when compared to previously
reported scores, our study does have limitations. First, although our model is transparent
enough to point to the most important variables predicting mortality, it does not provide a
clear causal path. In other words, our model does not offer an instrument that could help us
better design quality improvement programs toward a reduction in mortality rates after
cardiac surgery among patients with rheumatic valve conditions. This limitation could be
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addressed in future studies where causal models such as Bayesian Networks can be used to
predict not only mortality but also to determine a clinically interpretable causal model as well as
to conduct causal experiments. Second, our cohort of rheumatic patients comes from one of
the cities with the highest income in Latin America. This probably explains why the average
age of our subjects is in the upper limit of upper-middle-income countries included in the
REMEDY Study [
In conclusion, we believe that future studies should further validate the predictive performance
of the RheSCORE model among patient populations from other countries, evaluate how
healthcare professionals might use our Web application in daily clinical practice, and also
investigate how that use might affect their clinical decision making. Despite these pending
evaluations, and in view of our results steering to a superior predictive performance, we
recommend the incorporation of the RheSCORE model into daily practice when attempting to
predict mortality risk among patients undergoing cardiac surgical procedures for rheumatic
· The RheSCORE model, developed specifically for rheumatic-related conditions, has superior
predictive performance when compared to previous traditional scores.
· The most important variables predicting mortality across different models were left atrium
size, high creatinine, a tricuspid procedure, a reoperation procedure and the presence of
· As a model-based mortality prediction tool, the RheSCORE model can be accessed through
Web browsers and smartphones at http://www.incor.usp.br/quick/app.html
Conceptualization: Omar A. V. Mejia, Luiz A. F. Lisboa, Carlos M. A. Brandão, FlaÂvio
Tarasoutchi, Pablo M. A. Pomerantzeff, Fabio B. Jatene.
Data curation: Omar A. V. Mejia, Maxim Goncharov, Jorge Zubelli.
Formal analysis: Omar A. V. Mejia, Maxim Goncharov, Jorge Zubelli.
Funding acquisition: Fabio B. Jatene.
Investigation: Omar A. V. Mejia, LuÂõs R. P. Dallan, Elinthon Veronese, Gisele A. Lapenna.
Methodology: Omar A. V. Mejia, Luiz A. F. Lisboa, Jorge Zubelli, Pablo M. A. Pomerantzeff.
Project administration: Omar A. V. Mejia, Fabio B. Jatene.
Resources: FlaÂvio Tarasoutchi, Pablo M. A. Pomerantzeff.
Software: Maxim Goncharov, Jorge Zubelli.
Supervision: Manuel J. Antunes, Pablo M. A. Pomerantzeff, Fabio B. Jatene.
Validation: Luiz A. F. Lisboa, Jorge Zubelli.
Visualization: Omar A. V. Mejia, Carlos M. A. Brandão.
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Writing ± original draft: Omar A. V. Mejia, Maxim Goncharov, LuÂõs R. P. Dallan, Luiz A. F.
Writing ± review & editing: Manuel J. Antunes, Luiz A. F. Lisboa, LuÂõs A. O. Dallan, FlaÂvio
Tarasoutchi, Pablo M. A. Pomerantzeff, Fabio B. Jatene.
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