Antonio Gallo1, Leopoldo Pérez de Isla2, Sybil Charrière3, Alexandre Vimont4, Rodrigo Alonso5, Ovidio Muñiz-Grijalvo6, José L Díaz-Díaz7, Daniel Zambón8, Philippe Moulin3, Eric Bruckert9, Pedro Mata10, Sophie Béliard11. 1. Department of Endocrinology and Prevention of Cardiovascular Disease, Institute of Cardio Metabolism and Nutrition, La Pitié-Salpêtrière Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France; Laboratoire d'imagerie Biomédicale, Institut National de la Santé de la Recherche Médicale (INSERM) 1146, Centre National de la Recherche Scientifique 7371, Sorbonne University, Paris, France. Electronic address: antoniogallo.md@gmail.com. 2. Cardiology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Facultad de Medicina, Universidad Complutense, Madrid, Spain; Fundación Hipercolesterolemia Familiar, Madrid, Spain. 3. Department of Endocrinology, Metabolic disease, Diabetes and Nutrition, Hospices Civils de Lyon-Laboratory INSERM 1060 Cardiovascular Metabolism Endocrinology and Nutrition (CarMEN), Lyon, France. 4. Public Health Expertise, Paris, France. 5. Fundación Hipercolesterolemia Familiar, Madrid, Spain; Center for Advanced Metabolic Medicine and Nutrition, Santiago, Chile. 6. Unidad Clinico-Experimental de Riesgo Vascular, Hospital Virgen del Rocío, Sevilla, Spain. 7. Department of Internal Medicine, Hospital Abente y Lago, A Coruña, Spain. 8. Lipids Clinic, Department of Endocrinology, Hospital Clinic, Institut d'Investigacions Biomèdiques August Pi i Sunyer University of Barcelona, Barcelona, Spain. 9. Department of Endocrinology and Prevention of Cardiovascular Disease, Institute of Cardio Metabolism and Nutrition, La Pitié-Salpêtrière Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France. 10. Fundación Hipercolesterolemia Familiar, Madrid, Spain. 11. Aix Marseille University, INSERM, Institut National de Recherche pour l'agriculture, l'Alimentation et l'Environnement, C2VN, Marseille, France; Department of Nutrition, Metabolic Diseases, Endocrinology, La Conception Hospital, Marseille, France.
Abstract
OBJECTIVES: This study aimed at investigating the additional contribution of coronary artery calcium (CAC) score to SAFEHEART (Spanish Familial Hypercholesterolemia Cohort Study) risk equation (SAFEHEART-RE) for cardiovascular risk prediction in heterozygous familial hypercholesterolemia (HeFH). BACKGROUND: Common cardiovascular risk equations are imprecise for HeFH. Because of the high phenotype variability of HeFH, CAC score could help to better stratify the risk of atherosclerotic cardiovascular disease (ASCVD). METHODS: REFERCHOL (French Registry of Familial Hypercholesterolemia) and SAFEHEART are 2 ongoing national registries on HeFH. We analyzed data from primary prevention HeFH patients undergoing CAC quantification. We used probability-weighted Cox proportional hazards models to estimate HRs. Area under the receiver-operating characteristic curve (AUC) and net reclassification improvement (NRI) were used to compare the incremental contribution of CAC score when added to the SAFEHEART-RE for ASCVD prediction. ASCVD was defined as coronary heart disease, stroke or transient ischemic attack, peripheral artery disease, resuscitated sudden death, and cardiovascular death. RESULTS: We included 1,624 patients (mean age: 48.5 ± 12.8 years; men: 45.7%) from both registries. After a median follow-up of 2.7 years (interquartile range: 0.4-5.0 years), ASCVD occurred in 81 subjects. The presence of a CAC score of >100 was associated with an HR of 32.05 (95% CI: 10.08-101.94) of developing ASCVD as compared to a CAC score of 0. Receiving-operating curve analysis showed a good performance of CAC score alone in ASCVD prediction (AUC: 0.860 [95% CI: 0.853-0.869]). The addition of log(CAC + 1) to SAFEHEART-RE resulted in a significantly improved prediction of ASCVD (AUC: 0.884 [95% CI: 0.871-0.894] for SAFEHEART-RE + log(CAC + 1) vs AUC: 0.793 [95% CI: 0.779-0.818] for SAFEHEART-RE; P < 0.001). These results were confirmed also when considering only hard cardiovascular endpoints. The addition of CAC score was associated with an estimated overall net reclassification improvement of 45.4%. CONCLUSIONS: CAC score proved its use in improving cardiovascular risk stratification and ASCVD prediction in statin-treated HeFH.
OBJECTIVES: This study aimed at investigating the additional contribution of coronary artery calcium (CAC) score to SAFEHEART (Spanish Familial Hypercholesterolemia Cohort Study) risk equation (SAFEHEART-RE) for cardiovascular risk prediction in heterozygous familial hypercholesterolemia (HeFH). BACKGROUND: Common cardiovascular risk equations are imprecise for HeFH. Because of the high phenotype variability of HeFH, CAC score could help to better stratify the risk of atherosclerotic cardiovascular disease (ASCVD). METHODS: REFERCHOL (French Registry of Familial Hypercholesterolemia) and SAFEHEART are 2 ongoing national registries on HeFH. We analyzed data from primary prevention HeFH patients undergoing CAC quantification. We used probability-weighted Cox proportional hazards models to estimate HRs. Area under the receiver-operating characteristic curve (AUC) and net reclassification improvement (NRI) were used to compare the incremental contribution of CAC score when added to the SAFEHEART-RE for ASCVD prediction. ASCVD was defined as coronary heart disease, stroke or transient ischemic attack, peripheral artery disease, resuscitated sudden death, and cardiovascular death. RESULTS: We included 1,624 patients (mean age: 48.5 ± 12.8 years; men: 45.7%) from both registries. After a median follow-up of 2.7 years (interquartile range: 0.4-5.0 years), ASCVD occurred in 81 subjects. The presence of a CAC score of >100 was associated with an HR of 32.05 (95% CI: 10.08-101.94) of developing ASCVD as compared to a CAC score of 0. Receiving-operating curve analysis showed a good performance of CAC score alone in ASCVD prediction (AUC: 0.860 [95% CI: 0.853-0.869]). The addition of log(CAC + 1) to SAFEHEART-RE resulted in a significantly improved prediction of ASCVD (AUC: 0.884 [95% CI: 0.871-0.894] for SAFEHEART-RE + log(CAC + 1) vs AUC: 0.793 [95% CI: 0.779-0.818] for SAFEHEART-RE; P < 0.001). These results were confirmed also when considering only hard cardiovascular endpoints. The addition of CAC score was associated with an estimated overall net reclassification improvement of 45.4%. CONCLUSIONS: CAC score proved its use in improving cardiovascular risk stratification and ASCVD prediction in statin-treated HeFH.
Authors: Francesco Castagna; Jeremy Miles; Javier Arce; Ephraim Leiderman; Patrick Neshiwat; Paul Ippolito; Patricia Friedmann; Aldo Schenone; Lili Zhang; Carlos J Rodriguez; Michael J Blaha; Jeffrey M Levsky; Mario J Garcia; Leandro Slipczuk Journal: Circ Cardiovasc Imaging Date: 2022-06-21 Impact factor: 8.589
Authors: Pétra Eid; Louis Arnould; Pierre-Henry Gabrielle; Ludwig S Aho; Michel Farnier; Catherine Creuzot-Garcher; Yves Cottin Journal: J Pers Med Date: 2022-05-26
Authors: Miguel Cainzos-Achirica; Renato Quispe; Reed Mszar; Ramzi Dudum; Mahmoud Al Rifai; Raimund Erbel; Andreas Stang; Karl-Heinz Jöckel; Nils Lehmann; Sara Schramm; Börge Schmidt; Peter P Toth; Jamal S Rana; Joao A C Lima; Henrique Doria de Vasconcellos; Donald Lloyd-Jones; Parag H Joshi; Colby Ayers; Amit Khera; Michael J Blaha; Philip Greenland; Khurram Nasir Journal: J Am Heart Assoc Date: 2022-08-09 Impact factor: 6.106