A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE

Michele Di Mauro; Guglielmo Mario Actis Dato; Fabio Barili; Sandro Gelsomino; Pasquale Santse; Alessandro Della Corte; Antonio Carrozza; Ester Della Ratta; Diego Cugola; Lorenzo Galletti; Roger Devotini; Riccardo Casabona; Francesco Santini; Antonio Salsano; Roberto Scrofani; Carlo Antona; Luca Botta; Claudio Russo; Samuel Mancuso; Mauro Rinaldi; Carlo De Vincentiis; Andrea Biondi; Cesare Beghi; Giangiuseppe Cappabianca; Vincenzo Tarzia; Gino Gerosa; Michele De Bonis; Alberto Pozzoli; Francesco Nicolini; Filippo Benassi; Francesco Rosato; Elena Grasso; Ugolino Livi; Sponga Sandro; Davide Pacini; Roberto Di Bartolomeo; Andrea DeMartino; Uberto Bortolotti; Francesco Onorati; Giuseppe Faggian; Roberto Lorusso; Enrico Vizzardi; Gabriele Di Giammarco; Daniele Marinelli; Emmanuel Villa; Giovanni Troise; Marco Piciche; Francesco Musumeci; Domenico Paparella; Vito Margari; Grp Res Outcome Cardiac Surg GIROC

doi:10.1016/j.ijcard.2017.03.148

A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE

Michele Di Mauro^*, Guglielmo Mario Actis Dato, Fabio Barili, Sandro Gelsomino, Pasquale Santse, Alessandro Della Corte, Antonio Carrozza, Ester Della Ratta, Diego Cugola, Lorenzo Galletti, Roger Devotini, Riccardo Casabona, Francesco Santini, Antonio Salsano, Roberto Scrofani, Carlo Antona, Luca Botta, Claudio Russo, Samuel Mancuso, Mauro RinaldiCarlo De Vincentiis, Andrea Biondi, Cesare Beghi, Giangiuseppe Cappabianca, Vincenzo Tarzia, Gino Gerosa, Michele De Bonis, Alberto Pozzoli, Francesco Nicolini, Filippo Benassi, Francesco Rosato, Elena Grasso, Ugolino Livi, Sponga Sandro, Davide Pacini, Roberto Di Bartolomeo, Andrea DeMartino, Uberto Bortolotti, Francesco Onorati, Giuseppe Faggian, Roberto Lorusso, Enrico Vizzardi, Gabriele Di Giammarco, Daniele Marinelli, Emmanuel Villa, Giovanni Troise, Marco Piciche, Francesco Musumeci, Domenico Paparella, Vito Margari, Grp Res Outcome Cardiac Surg GIROC

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Background: The aim of this large retrospective study was to provide a logistic risk model along an additive score to predict early mortality after surgical treatment of patients with heart valve or prosthesis infective endocarditis (IE).

Methods: From 2000 to 2015, 2715 patients with native valve endocarditis (NVE) or prosthesis valve endocarditis (PVE) were operated on in 26 Italian Cardiac Surgery Centers. The relationship between early mortality and covariates was evaluated with logistic mixed effect models. Fixed effects are parameters associated with the entire population or with certain repeatable levels of experimental factors, while random effects are associated with individual experimental units (centers).

Results: Early mortality was 11.0% (298/2715); At mixed effect logistic regression the following variables were found associated with early mortality: age class, female gender, LVEF, preoperative shock, COPD, creatinine value above 2 mg/dl, presence of abscess, number of treated valve/prosthesis (with respect to one treated valve/prosthesis) and the isolation of Staphylococcus aureus, Fungus spp., Pseudomonas Aeruginosa and other micro-organisms, while Streptococcus spp., Enterococcus spp. and other Staphylococci did not affect early mortality, as well as no micro-organisms isolation. LVEF was found linearly associated with outcomes while non-linear association between mortality and age was tested and the best model was found with a categorization into four classes (AUC = 0.851).

Conclusions: The following study provides a logistic risk model to predict early mortality in patients with heart valve or prosthesis infective endocarditis undergoing surgical treatment, called "The EndoSCORE". (C) 2017 Published by Elsevier Ireland Ltd.

Original language	English
Pages (from-to)	97-102
Number of pages	6
Journal	International Journal of Cardiology
Volume	241
DOIs	https://doi.org/10.1016/j.ijcard.2017.03.148
Publication status	Published - 15 Aug 2017

Keywords

Infective endocarditis
Risk score
Valve surgery
CARDIAC-SURGERY
LOGISTIC-MODELS
EUROSCORE
RISK
DETERMINANTS
PERFORMANCE
MULTICENTER
PROFILE
SYSTEM
ADULTS

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10.1016/j.ijcard.2017.03.148

1 Erratum / corrigendum / retractions

Corrigendum to "A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis". The EndoSCORE (vol 241, pg 97, 2017)
Di Mauro, M., Dato, G. M. A., Barili, F., Gelsomino, S., Sante, P., Della Corte, A., Carrozza, A., Della Ratta, E., Cugola, D., Galletti, L., Devotini, R., Casabona, R., Santini, F., Salsano, A., Scrofani, R., Antona, C., Botta, L., Russo, C., Mancuso, S., Rinaldi, M., & 41 othersDe Vincentiis, C., Biondi, A., Beghi, C., Cappabianca, G., Tarzia, V., Gerosa, G., De Bonis, M., Pozzoli, A., Nicolini, F., Benassi, F., Rosato, F., Grasso, E., Livi, U., Sponga, S., Pacini, D., Di Bartolomeo, R., DeMartino, A., Bortolotti, U., Onorati, F., Faggian, G., Lorusso, R., Vizzardi, E., Di Giammarco, G., Marinelli, D., Villa, E., Troise, G., Piciche, M., Musumeci, F., Paparella, D., Margari, V., Tritto, F., Damiani, G., Scrascia, G., Zaccaria, S., Renzulli, A., Serraino, G., Mariscalco, G., Maselli, D., Foschi, M., Parolari, A. & Nappi, G., 1 May 2018, In: International Journal of Cardiology. 258, p. 337-337 1 p.
Research output: Contribution to journal › Erratum / corrigendum / retractions › Academic

Open Access

Cite this

Di Mauro, M., Dato, G. M. A., Barili, F., Gelsomino, S., Santse, P., Della Corte, A., Carrozza, A., Della Ratta, E., Cugola, D., Galletti, L., Devotini, R., Casabona, R., Santini, F., Salsano, A., Scrofani, R., Antona, C., Botta, L., Russo, C., Mancuso, S., ... Grp Res Outcome Cardiac Surg GIROC (2017). A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE. International Journal of Cardiology, 241, 97-102. https://doi.org/10.1016/j.ijcard.2017.03.148

@article{399b29768fed400f98d8fe9e710d5392,

title = "A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE",

abstract = "Background: The aim of this large retrospective study was to provide a logistic risk model along an additive score to predict early mortality after surgical treatment of patients with heart valve or prosthesis infective endocarditis (IE).Methods: From 2000 to 2015, 2715 patients with native valve endocarditis (NVE) or prosthesis valve endocarditis (PVE) were operated on in 26 Italian Cardiac Surgery Centers. The relationship between early mortality and covariates was evaluated with logistic mixed effect models. Fixed effects are parameters associated with the entire population or with certain repeatable levels of experimental factors, while random effects are associated with individual experimental units (centers).Results: Early mortality was 11.0% (298/2715); At mixed effect logistic regression the following variables were found associated with early mortality: age class, female gender, LVEF, preoperative shock, COPD, creatinine value above 2 mg/dl, presence of abscess, number of treated valve/prosthesis (with respect to one treated valve/prosthesis) and the isolation of Staphylococcus aureus, Fungus spp., Pseudomonas Aeruginosa and other micro-organisms, while Streptococcus spp., Enterococcus spp. and other Staphylococci did not affect early mortality, as well as no micro-organisms isolation. LVEF was found linearly associated with outcomes while non-linear association between mortality and age was tested and the best model was found with a categorization into four classes (AUC = 0.851).Conclusions: The following study provides a logistic risk model to predict early mortality in patients with heart valve or prosthesis infective endocarditis undergoing surgical treatment, called {"}The EndoSCORE{"}. (C) 2017 Published by Elsevier Ireland Ltd.",

keywords = "Infective endocarditis, Risk score, Valve surgery, CARDIAC-SURGERY, LOGISTIC-MODELS, EUROSCORE, RISK, DETERMINANTS, PERFORMANCE, MULTICENTER, PROFILE, SYSTEM, ADULTS",

author = "{Di Mauro}, Michele and Dato, {Guglielmo Mario Actis} and Fabio Barili and Sandro Gelsomino and Pasquale Santse and {Della Corte}, Alessandro and Antonio Carrozza and {Della Ratta}, Ester and Diego Cugola and Lorenzo Galletti and Roger Devotini and Riccardo Casabona and Francesco Santini and Antonio Salsano and Roberto Scrofani and Carlo Antona and Luca Botta and Claudio Russo and Samuel Mancuso and Mauro Rinaldi and {De Vincentiis}, Carlo and Andrea Biondi and Cesare Beghi and Giangiuseppe Cappabianca and Vincenzo Tarzia and Gino Gerosa and {De Bonis}, Michele and Alberto Pozzoli and Francesco Nicolini and Filippo Benassi and Francesco Rosato and Elena Grasso and Ugolino Livi and Sponga Sandro and Davide Pacini and {Di Bartolomeo}, Roberto and Andrea DeMartino and Uberto Bortolotti and Francesco Onorati and Giuseppe Faggian and Roberto Lorusso and Enrico Vizzardi and {Di Giammarco}, Gabriele and Daniele Marinelli and Emmanuel Villa and Giovanni Troise and Marco Piciche and Francesco Musumeci and Domenico Paparella and Vito Margari and {Grp Res Outcome Cardiac Surg GIROC}",

year = "2017",

month = aug,

day = "15",

doi = "10.1016/j.ijcard.2017.03.148",

language = "English",

volume = "241",

pages = "97--102",

journal = "International Journal of Cardiology",

issn = "0167-5273",

publisher = "Elsevier Ireland Ltd",

}

Di Mauro, M, Dato, GMA, Barili, F, Gelsomino, S, Santse, P, Della Corte, A, Carrozza, A, Della Ratta, E, Cugola, D, Galletti, L, Devotini, R, Casabona, R, Santini, F, Salsano, A, Scrofani, R, Antona, C, Botta, L, Russo, C, Mancuso, S, Rinaldi, M, De Vincentiis, C, Biondi, A, Beghi, C, Cappabianca, G, Tarzia, V, Gerosa, G, De Bonis, M, Pozzoli, A, Nicolini, F, Benassi, F, Rosato, F, Grasso, E, Livi, U, Sandro, S, Pacini, D, Di Bartolomeo, R, DeMartino, A, Bortolotti, U, Onorati, F, Faggian, G, Lorusso, R, Vizzardi, E, Di Giammarco, G, Marinelli, D, Villa, E, Troise, G, Piciche, M, Musumeci, F, Paparella, D, Margari, V & Grp Res Outcome Cardiac Surg GIROC 2017, 'A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE', International Journal of Cardiology, vol. 241, pp. 97-102. https://doi.org/10.1016/j.ijcard.2017.03.148

TY - JOUR

T1 - A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE

AU - Di Mauro, Michele

AU - Dato, Guglielmo Mario Actis

AU - Barili, Fabio

AU - Gelsomino, Sandro

AU - Santse, Pasquale

AU - Della Corte, Alessandro

AU - Carrozza, Antonio

AU - Della Ratta, Ester

AU - Cugola, Diego

AU - Galletti, Lorenzo

AU - Devotini, Roger

AU - Casabona, Riccardo

AU - Santini, Francesco

AU - Salsano, Antonio

AU - Scrofani, Roberto

AU - Antona, Carlo

AU - Botta, Luca

AU - Russo, Claudio

AU - Mancuso, Samuel

AU - Rinaldi, Mauro

AU - De Vincentiis, Carlo

AU - Biondi, Andrea

AU - Beghi, Cesare

AU - Cappabianca, Giangiuseppe

AU - Tarzia, Vincenzo

AU - Gerosa, Gino

AU - De Bonis, Michele

AU - Pozzoli, Alberto

AU - Nicolini, Francesco

AU - Benassi, Filippo

AU - Rosato, Francesco

AU - Grasso, Elena

AU - Livi, Ugolino

AU - Sandro, Sponga

AU - Pacini, Davide

AU - Di Bartolomeo, Roberto

AU - DeMartino, Andrea

AU - Bortolotti, Uberto

AU - Onorati, Francesco

AU - Faggian, Giuseppe

AU - Lorusso, Roberto

AU - Vizzardi, Enrico

AU - Di Giammarco, Gabriele

AU - Marinelli, Daniele

AU - Villa, Emmanuel

AU - Troise, Giovanni

AU - Piciche, Marco

AU - Musumeci, Francesco

AU - Paparella, Domenico

AU - Margari, Vito

AU - Grp Res Outcome Cardiac Surg GIROC

PY - 2017/8/15

Y1 - 2017/8/15

N2 - Background: The aim of this large retrospective study was to provide a logistic risk model along an additive score to predict early mortality after surgical treatment of patients with heart valve or prosthesis infective endocarditis (IE).Methods: From 2000 to 2015, 2715 patients with native valve endocarditis (NVE) or prosthesis valve endocarditis (PVE) were operated on in 26 Italian Cardiac Surgery Centers. The relationship between early mortality and covariates was evaluated with logistic mixed effect models. Fixed effects are parameters associated with the entire population or with certain repeatable levels of experimental factors, while random effects are associated with individual experimental units (centers).Results: Early mortality was 11.0% (298/2715); At mixed effect logistic regression the following variables were found associated with early mortality: age class, female gender, LVEF, preoperative shock, COPD, creatinine value above 2 mg/dl, presence of abscess, number of treated valve/prosthesis (with respect to one treated valve/prosthesis) and the isolation of Staphylococcus aureus, Fungus spp., Pseudomonas Aeruginosa and other micro-organisms, while Streptococcus spp., Enterococcus spp. and other Staphylococci did not affect early mortality, as well as no micro-organisms isolation. LVEF was found linearly associated with outcomes while non-linear association between mortality and age was tested and the best model was found with a categorization into four classes (AUC = 0.851).Conclusions: The following study provides a logistic risk model to predict early mortality in patients with heart valve or prosthesis infective endocarditis undergoing surgical treatment, called "The EndoSCORE". (C) 2017 Published by Elsevier Ireland Ltd.

AB - Background: The aim of this large retrospective study was to provide a logistic risk model along an additive score to predict early mortality after surgical treatment of patients with heart valve or prosthesis infective endocarditis (IE).Methods: From 2000 to 2015, 2715 patients with native valve endocarditis (NVE) or prosthesis valve endocarditis (PVE) were operated on in 26 Italian Cardiac Surgery Centers. The relationship between early mortality and covariates was evaluated with logistic mixed effect models. Fixed effects are parameters associated with the entire population or with certain repeatable levels of experimental factors, while random effects are associated with individual experimental units (centers).Results: Early mortality was 11.0% (298/2715); At mixed effect logistic regression the following variables were found associated with early mortality: age class, female gender, LVEF, preoperative shock, COPD, creatinine value above 2 mg/dl, presence of abscess, number of treated valve/prosthesis (with respect to one treated valve/prosthesis) and the isolation of Staphylococcus aureus, Fungus spp., Pseudomonas Aeruginosa and other micro-organisms, while Streptococcus spp., Enterococcus spp. and other Staphylococci did not affect early mortality, as well as no micro-organisms isolation. LVEF was found linearly associated with outcomes while non-linear association between mortality and age was tested and the best model was found with a categorization into four classes (AUC = 0.851).Conclusions: The following study provides a logistic risk model to predict early mortality in patients with heart valve or prosthesis infective endocarditis undergoing surgical treatment, called "The EndoSCORE". (C) 2017 Published by Elsevier Ireland Ltd.

KW - Infective endocarditis

KW - Risk score

KW - Valve surgery

KW - CARDIAC-SURGERY

KW - LOGISTIC-MODELS

KW - EUROSCORE

KW - RISK

KW - DETERMINANTS

KW - PERFORMANCE

KW - MULTICENTER

KW - PROFILE

KW - SYSTEM

KW - ADULTS

U2 - 10.1016/j.ijcard.2017.03.148

DO - 10.1016/j.ijcard.2017.03.148

M3 - Article

C2 - 28390740

SN - 0167-5273

VL - 241

SP - 97

EP - 102

JO - International Journal of Cardiology

JF - International Journal of Cardiology

ER -

A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis. The EndoSCORE

Abstract

Keywords

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Research output

Corrigendum to "A predictive model for early mortality after surgical treatment of heart valve or prosthesis infective endocarditis". The EndoSCORE (vol 241, pg 97, 2017)

Cite this