Research Article: 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

Date Published: July 6, 2018

Publisher: Public Library of Science

Author(s): Omar A. V. Mejia, Manuel J. Antunes, Maxim Goncharov, Luís R. P. Dallan, Elinthon Veronese, Gisele A. Lapenna, Luiz A. F. Lisboa, Luís A. O. Dallan, Carlos M. A. Brandão, Jorge Zubelli, Flávio Tarasoutchi, Pablo M. A. Pomerantzeff, Fabio B. Jatene, Markus M. Bachschmid.


Mortality prediction after cardiac procedures is an essential tool in clinical decision making. Although rheumatic cardiac disease remains a major cause of heart surgery in the world no previous study validated risk scores in a sample exclusively with this condition.

Develop a novel predictive model focused on mortality prediction among patients undergoing cardiac surgery secondary to rheumatic valve conditions.

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 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.

Partial Text

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 [1].

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 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).

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 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.

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 valve conditions.




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