Stefano Figliozzi1, Antonia Camporeale2, Sara Boveri3, Federico Pieruzzi4, Maurizio Pieroni5, Paola Lusardi6, Marco Spada7, Renzo Mignani8, Alessandro Burlina9, Francesca Graziani10, Silvia Pica11, Lara Tondi12, Andrea Bernardini13, Kelvin Chow14, Mehdi Namdar15, Massimo Lombardi16. 1. Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, S. Luca Hospital, Milan 20149, Italy; Multimodality Cardiac Imaging Section, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: stefanofigliozzi@hotmail.it. 2. Multimodality Cardiac Imaging Section, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: antonia.camporeale@grupposandonato.it. 3. Scientific Directorate, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: sara.boveri@grupposandonato.it. 4. Department of Medicine and Surgery, University of Milano Bicocca, Nephrology and Dialysis Unit, ASST-Monza San Gerardo Hospital, Monza, Italy. Electronic address: federico.pieruzzi@unimib.it. 5. Department of Cardiology, San Donato Hospital, Arezzo, Italy. 6. Department of Cardiology, Humanitas Hospital, Torino, Italy. 7. Department of Pediatrics, University of Torino, Torino, Italy. Electronic address: marco.spada@unito.it. 8. Nephrology and Dialysis Department, Infermi Hospital, Rimini, Italy. Electronic address: renzo.mignani@auslromagna.it. 9. Neurological Unit, St. Bassiano Hospital, Bassano del Grappa, Italy. Electronic address: alessandro.burlina@aulss7.veneto.it. 10. Department of Cardiovascular and Thoracic Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. Electronic address: francesca.graziani@policlinicogemelli.it. 11. Multimodality Cardiac Imaging Section, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: silvia.pica@grupposandonato.it. 12. Multimodality Cardiac Imaging Section, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: lara.tondi@grupposandonato.it. 13. Arrhythmology and Electrophysiology Department, IRCCS Policlinico San Donato, San Donato Milanese, Milano, Italy. 14. Siemens Medical Solutions USA, Inc., Chicago, United States. Electronic address: kelvin.chow@siemens-healthineers.com. 15. Cardiology Division, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland. Electronic address: mehdi.namdar@hcuge.ch. 16. Multimodality Cardiac Imaging Section, IRCCS Policlinico San Donato, San Donato Milanese, Italy. Electronic address: massimo.lombardi@grupposandonato.it.
Abstract
OBJECTIVES: To elaborate an ECG-based nomogram estimating the probability to detect cardiac involvement by cardiac magnetic resonance (CMR) in Fabry Disease (FD). METHODS: 119 FD patients and 26 healthy controls underwent ECG and CMR. Test (n = 88, 60%) and validation cohorts (n = 57, 40%) were randomly derived. Cardiac involvement was defined as the presence of low myocardial T1 value, a CMR-surrogate of myocardial glycosphingolipid storage. ECG changes associated with low T1 value were identified in the test cohort, included in the nomogram and then tested in the validation cohort. RESULTS: Sokolow-Lyon index (AUC = 0.769), ratio between P-wave and PR-segment durations (Pwave/PRsegment) (AUC = 0.778), QRS duration (AUC = 0.703), QT (AUC = 0.769) duration were independently associated with the presence of low T1 on CMR at multivariate analysis. An ECG-based nomogram including these four parameters was accurate in identifying patients with CMR evidence of glycosphingolipid storage (c-index of the derived-nomogram = 0.90 in the test group; 0.81 in the validation group). CONCLUSION: We propose a practical ECG-based nomogram accurately estimating the probability to detect low T1 values by CMR in FD patients. The application of this tool in clinical practice could improve early detection of FD cardiac involvement.
OBJECTIVES: To elaborate an ECG-based nomogram estimating the probability to detect cardiac involvement by cardiac magnetic resonance (CMR) in Fabry Disease (FD). METHODS: 119 FD patients and 26 healthy controls underwent ECG and CMR. Test (n = 88, 60%) and validation cohorts (n = 57, 40%) were randomly derived. Cardiac involvement was defined as the presence of low myocardial T1 value, a CMR-surrogate of myocardial glycosphingolipid storage. ECG changes associated with low T1 value were identified in the test cohort, included in the nomogram and then tested in the validation cohort. RESULTS: Sokolow-Lyon index (AUC = 0.769), ratio between P-wave and PR-segment durations (Pwave/PRsegment) (AUC = 0.778), QRS duration (AUC = 0.703), QT (AUC = 0.769) duration were independently associated with the presence of low T1 on CMR at multivariate analysis. An ECG-based nomogram including these four parameters was accurate in identifying patients with CMR evidence of glycosphingolipid storage (c-index of the derived-nomogram = 0.90 in the test group; 0.81 in the validation group). CONCLUSION: We propose a practical ECG-based nomogram accurately estimating the probability to detect low T1 values by CMR in FD patients. The application of this tool in clinical practice could improve early detection of FD cardiac involvement.
Authors: Matthew Zada; Queenie Lo; Siddharth J Trivedi; Mehmet Harapoz; Anita C Boyd; Kerry Devine; Norman Sadick; Michel C Tchan; Liza Thomas Journal: J Cardiovasc Dev Dis Date: 2022-01-03