Resumen:
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[EN] Background Magnetic resonance imaging (MRI) is the most accurate imaging technique for left ventricular ejection fraction (LVEF) quantification, but as yet the prognostic value of LVEF assessment at any time after ...[+]
[EN] Background Magnetic resonance imaging (MRI) is the most accurate imaging technique for left ventricular ejection fraction (LVEF) quantification, but as yet the prognostic value of LVEF assessment at any time after ST-segment elevation myocardial infarction (STEMI) for subsequent major adverse cardiac event (MACE) prediction is uncertain.
Purpose To explore the prognostic impact of MRI-derived LVEF at any time post-STEMI to predict subsequent MACE (cardiovascular death or re-admission for acute heart failure).
Study Type Prospective.
Population One thousand thirteen STEMI patients were included in a multicenter registry.
Field Strength/Sequence 1.5-T. Balanced steady-state free precession (cine imaging) and segmented inversion recovery steady-state free precession (late gadolinium enhancement) sequences.
Assessment Post-infarction MRI-derived LVEF (reduced [r]: <40%; mid-range [mr]: 40%-49%; preserved [p]: >= 50%) was sequentially quantified at 1 week and after >3 months of follow-up.
Statistical Tests Multi-state Markov model to determine the prognostic value of each LVEF state (r-, mr- or p-) at any time point assessed to predict subsequent MACE. A P-value During a 6.2-year median follow-up, 105 MACE (10%) were registered. Transitions toward improved LVEF predominated and only r-LVEF (at any time assessed) was significantly related to a higher incidence of subsequent MACE. The observed transitions from r-LVEF, mr-LVEF, and p-LVEF states to MACE were: 15.3%, 6%, and 6.7%, respectively. Regarding the adjusted transition intensity ratios, patients in r-LVEF state were 4.52-fold more likely than those in mr-LVEF state and 5.01-fold more likely than those in p-LVEF state to move to MACE state. Nevertheless, no significant differences were found in transitions from mr-LVEF and p-LVEF states to MACE state (P-value = 0.6).
Data Conclusion LVEF is an important MRI index for simple and dynamic post-STEMI risk stratification. Detection of r-LVEF by MRI at any time during follow-up identifies a subset of patients at high risk of subsequent events.
Level of Evidence 2
Technical Efficacy Stage 2
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Código del Proyecto:
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info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/PI20%2F00637/ES/RESOLUCION DE LA OBSTRUCCION MICROVASCULAR TRAS UN INFARTO DE MIOCARDIO: EVALUACION DE LAS CONSECUENCIAS ESTRUCTURALES Y CLINICAS Y BUSQUEDA DE NUEVAS OPCIONES TERAPEUTICAS./
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info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 (ISCIII)/PI20%2F00637/ES/RESOLUCION DE LA OBSTRUCCION MICROVASCULAR TRAS UN INFARTO DE MIOCARDIO: EVALUACION DE LAS CONSECUENCIAS ESTRUCTURALES Y CLINICAS Y BUSQUEDA DE NUEVAS OPCIONES TERAPEUTICAS./
info:eu-repo/grantAgreement/Fundació La Marató de TV3//20153030-31-32/
info:eu-repo/grantAgreement/MINECO//CB16%2F11%2F00486/ES/ENFERMEDADES CARDIOVASCULARES/
info:eu-repo/grantAgreement/CDTI//E9113//EUROSTARS project/
info:eu-repo/grantAgreement/MINECO//PI15%2F00531/ES/Papel del sistema GAS6-TAM en la diferenciación fibroblástica y el remodelado ventricular tras el infarto agudo de miocardio/
info:eu-repo/grantAgreement/GVA//AEST%2F2019%2F037/
info:eu-repo/grantAgreement/GVA//AEST%2F2020%2F029//Aplicación de técnicas de deep learning (aprendizaje profundo) para un análisis automático de imágenes de Resonancia/
info:eu-repo/grantAgreement/ISCIII//PI17%2F01836/
info:eu-repo/grantAgreement/MCIU//CIIP-20192020/
info:eu-repo/grantAgreement/MINECO//CB16%2F11%2F00486//ENFERMEDADES CARDIOVASCULARES/
info:eu-repo/grantAgreement/AVI//INNCAD00%2F19%2F085//Proyecto 4DTools: nuevas técnicas y biomarcadores para diagnóstico-pronóstico de patologías de la aorta ascendente a través de técnicas de imagen médica/
info:eu-repo/grantAgreement/AVI//INNCAD%2F2020%2F84/
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Agradecimientos:
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This work was supported by "Instituto de Salud Carlos III," "Fondos Europeos de Desarrollo Regional FEDER" (grants PI15/00531, PI17/01836, PI20/00637, and CIBERCV16/11/00486), and "Marato TV3" (grant 20153030-31-32), a ...[+]
This work was supported by "Instituto de Salud Carlos III," "Fondos Europeos de Desarrollo Regional FEDER" (grants PI15/00531, PI17/01836, PI20/00637, and CIBERCV16/11/00486), and "Marato TV3" (grant 20153030-31-32), a grant from the Catalonian Society of Cardiology 2015 and a grant from La Caixa Foundation (HR17-00527). David Moratal and Jose Gavara acknowledge financial support from the "Conselleria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana" (grants AEST/2019/037 and AEST/2020/029), "Agencia Valenciana de la Innovacion, Generalitat Valenciana" (ref. INNCAD00/19/085 and INNCAD/2020/84), and "Centro para el Desarrollo Tecnologico Industrial" (Programa Eurostars-2, actuacion Interempresas Internacional), Spanish "Ministerio de Ciencia, Innovacion y Universidades" (ref. CIIP-20192020).
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