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,Pablo M A Pomerantzeff,Elinthon Veronese,Luiz A F Lisboa,Jorge Zubelli,Maxim Goncharov,Gisele A Lapenna,Fabio B Jatene,Flávio Tarasoutchi,Luís R P Dallan,Luís A O Dallan,Carlos M A Brandão,Manuel J Antunes,Markus M Bachschmid

doi:10.1371/journal.pone.0199277

Abstract

BackgroundMortality 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.ObjectivesDevelop a novel predictive model focused on mortality prediction among patients undergoing cardiac surgery secondary to rheumatic valve conditions.MethodsWe 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).ResultsA 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.ConclusionsThe RheSCORE model outperformed pre-existing scores in a sample of patients with rheumatic cardiac disease.

Highlights

80% of countries worldwide present with rheumatic fever (RF) and with one of its most prevalent complications, the rheumatic heart disease (RHD)
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
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

Summary

Introduction

80% of countries worldwide present with rheumatic fever (RF) and with one of its most prevalent complications, the rheumatic heart disease (RHD). 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. If the sample which validated the risk score was different from the patient population where it is being applied, prediction performance could be compromised, resulting in misleading clinical decisions. Mortality prediction after cardiac procedures is an essential tool in clinical decision making. 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

Objectives

Methods

Results

Conclusion