Literature DB >> 29778857

Development of a Risk Score Based on Aortic Calcification to Predict 1-Year Mortality After Transcatheter Aortic Valve Replacement.

Pierre Lantelme1, Hélène Eltchaninoff2, Muriel Rabilloud3, Géraud Souteyrand4, Marion Dupré2, Marco Spaziano5, Marc Bonnet6, Clément Becle7, Benjamin Riche3, Eric Durand2, Erik Bouvier8, Jean-Nicolas Dacher9, Pierre-Yves Courand7, Lucie Cassagnes10, Eduardo E Dávila Serrano11, Pascal Motreff4, Loic Boussel12, Thierry Lefèvre8, Brahim Harbaoui7.   

Abstract

OBJECTIVES: The aim of this study was to develop a new scoring system based on thoracic aortic calcification (TAC) to predict 1-year cardiovascular and all-cause mortality.
BACKGROUND: A calcified aorta is often associated with poor prognosis after transcatheter aortic valve replacement (TAVR). A risk score encompassing aortic calcification may be valuable in identifying poor TAVR responders.
METHODS: The C4CAPRI (4 Cities for Assessing CAlcification PRognostic Impact) multicenter study included a training cohort (1,425 patients treated using TAVR between 2010 and 2014) and a contemporary test cohort (311 patients treated in 2015). TAC was measured by computed tomography pre-TAVR. CAPRI risk scores were based on the linear predictors of Cox models including TAC in addition to comorbidities and demographic, atherosclerotic disease and cardiac function factors. CAPRI scores were constructed and tested in 2 independent cohorts.
RESULTS: Cardiovascular and all-cause mortality at 1 year was 13.0% and 17.9%, respectively, in the training cohort and 8.2% and 11.8% in the test cohort. The inclusion of TAC in the model improved prediction: 1-cm3 increase in TAC was associated with a 6% increase in cardiovascular mortality and a 4% increase in all-cause mortality. The predicted and observed survival probabilities were highly correlated (slopes >0.9 for both cardiovascular and all-cause mortality). The model's predictive power was fair (AUC 68% [95% confidence interval [CI]: 64% to 72%]) for both cardiovascular and all-cause mortality. The model performed similarly in the training and test cohorts.
CONCLUSIONS: The CAPRI score, which combines the TAC variable with classical prognostic factors, is predictive of 1-year cardiovascular and all-cause mortality. Its predictive performance was confirmed in an independent contemporary cohort. CAPRI scores are highly relevant to current practice and strengthen the evidence base for decision making in valvular interventions. Its routine use may help prevent futile procedures.
Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  TAVR; aortic stiffness; mortality; outcome; risk stratification

Mesh:

Year:  2018        PMID: 29778857     DOI: 10.1016/j.jcmg.2018.03.018

Source DB:  PubMed          Journal:  JACC Cardiovasc Imaging        ISSN: 1876-7591


  4 in total

1.  Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement.

Authors:  Dagmar F Hernandez-Suarez; Yeunjung Kim; Pedro Villablanca; Tanush Gupta; Jose Wiley; Brenda G Nieves-Rodriguez; Jovaniel Rodriguez-Maldonado; Roberto Feliu Maldonado; Istoni da Luz Sant'Ana; Cristina Sanina; Pedro Cox-Alomar; Harish Ramakrishna; Angel Lopez-Candales; William W O'Neill; Duane S Pinto; Azeem Latib; Abiel Roche-Lima
Journal:  JACC Cardiovasc Interv       Date:  2019-07-22       Impact factor: 11.195

2.  Comorbidities may offset expected improved survival after transcatheter aortic valve replacement.

Authors:  Pierre Lantelme; Matthieu Aubry; Jacques Chan Peng; Benjamin Riche; Géraud Souteyrand; Philippe Jaafar; Muriel Rabilloud; Brahim Harbaoui; Olivier Muller; Benoit Cosset; Mattia Pagnoni; Thibaut Manigold
Journal:  Eur Heart J Open       Date:  2022-04-16

3.  TAVI-CT score to evaluate the anatomic risk in patients undergoing transcatheter aortic valve implantation.

Authors:  Nicola Corcione; Alberto Morello; Paolo Ferraro; Michele Cimmino; Michele Albanese; Martino Pepe; Palma Luisa Nestola; Salvatore Giordano; Luca Bardi; Giuseppe Biondi-Zoccai; Arturo Giordano
Journal:  Sci Rep       Date:  2022-05-09       Impact factor: 4.996

4.  Machine Learning-Based Personalized Risk Prediction Model for Mortality of Patients Undergoing Mitral Valve Surgery: The PRIME Score.

Authors:  Ning Zhou; Zhili Ji; Fengjuan Li; Bokang Qiao; Rui Lin; Wenxi Jiang; Yuexin Zhu; Yuwei Lin; Kui Zhang; Shuanglei Li; Bin You; Pei Gao; Ran Dong; Yuan Wang; Jie Du
Journal:  Front Cardiovasc Med       Date:  2022-04-01
  4 in total

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