Chiara Caselli1, Daniele Rovai2, Valentina Lorenzoni3, Clara Carpeggiani2, Anna Teresinska4, Santiago Aguade5, Giancarlo Todiere6, Alessia Gimelli6, Stephen Schroeder7, Giancarlo Casolo8, Rosa Poddighe8, Francesca Pugliese9, Dominique Le Guludec10, Serafina Valente11, Gianmario Sambuceti12, Pasquale Perrone-Filardi13, Silvia Del Ry2, Martina Marinelli2, Stephan Nekolla14, Mikko Pietila15, Massimo Lombardi6, Rosa Sicari2, Arthur Scholte16, José Zamorano17, Philipp A Kaufmann18, S Richard Underwood19, Juhani Knuuti15, Daniela Giannessi2, Danilo Neglia20. 1. Institute of Clinical Physiology, National Research Council, Pisa, Italy. Electronic address: chiara.caselli@ifc.cnr.it. 2. Institute of Clinical Physiology, National Research Council, Pisa, Italy. 3. Scuola Superiore Sant'Anna, Pisa, Italy. 4. Institute of Cardiology, Warsaw, Poland. 5. Institut Catala de la Salut, Barcelona, Spain. 6. Fondazione Toscana G. Monasterio, Pisa, Italy. 7. Kliniken des Landkreises Göppingen, Göppingen, Germany. 8. Ospedale della Versilia, Lido di Camaiore, Italy. 9. Centre for Advanced Cardiovascular Imaging, NIHR Cardiovascular Biomedical Research Unit at Barts, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University of London, London, United Kingdom. 10. APHP, Groupe Hospitalier Bichat-Claude Bernard, Département Hospitalo-Universitaire FIRE and Université Paris Diderot, Paris, France. 11. Azienda Ospedaliero Universitaria Careggi, Firenze, Italy. 12. Università di Genova, Genoa, Italy. 13. Università di Napoli Federico II, Naples, Italy. 14. Klinikum rechts der Isar der Technischen Universitat Munchen, Munchen, Germany. 15. University of Turku and Turku University Hospital, Turku, Finland. 16. Leiden University Medical Center, Leiden, The Netherlands. 17. University Hospital Clinico San Carlos, Madrid, Spain. 18. University Hospital Zurich, Zurich, Switzerland. 19. Imperial College London, United Kingdom. 20. Institute of Clinical Physiology, National Research Council, Pisa, Italy; Fondazione Toscana G. Monasterio, Pisa, Italy.
Abstract
BACKGROUND: In patients with chronic angina-like chest pain, the probability of coronary artery disease (CAD) is estimated by symptoms, age, and sex according to the Genders clinical model. We investigated the incremental value of circulating biomarkers over the Genders model to predict functionally significant CAD in patients with chronic chest pain. METHODS: In 527 patients (60.4 years, standard deviation, 8.9 years; 61.3% male participants) enrolled in the European Evaluation of Integrated Cardiac Imaging (EVINCI) study, clinical and biohumoral data were collected. RESULTS: Functionally significant CAD-ie, obstructive coronary disease seen at invasive angiography causing myocardial ischemia at stress imaging or associated with reduced fractional flow reserve (FFR < 0.8), or both-was present in 15.2% of patients. High-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST) levels, and high-sensitivity C-reactive protein (hs-CRP) were the only independent predictors of disease among 31 biomarkers analyzed. The model integrating these biohumoral markers with clinical variables outperformed the Genders model by receiver operating characteristic curve (ROC) (area under the curve [AUC], 0.70 [standard error (SE), 0.03] vs 0.58 [SE, 0.03], respectively, P < 0.001) and reclassification analysis (net reclassification improvement, 0.15 [SE, 0.07]; P = 0.04). Cross-validation of the ROC analysis confirmed the discrimination ability of the new model (AUC, 0.66). As many as 56% of patients who were assigned to a higher pretest probability by the Genders model were correctly reassigned to a low probability class (< 15%) by the new integrated model. CONCLUSIONS: The Genders model has a low accuracy for predicting functionally significant CAD. A new model integrating HDL cholesterol, AST, and hs-CRP levels with common clinical variables has a higher predictive accuracy for functionally significant CAD and allows the reclassification of patients from an intermediate/high to a low pretest likelihood of CAD.
BACKGROUND: In patients with chronic angina-like chest pain, the probability of coronary artery disease (CAD) is estimated by symptoms, age, and sex according to the Genders clinical model. We investigated the incremental value of circulating biomarkers over the Genders model to predict functionally significant CAD in patients with chronic chest pain. METHODS: In 527 patients (60.4 years, standard deviation, 8.9 years; 61.3% male participants) enrolled in the European Evaluation of Integrated Cardiac Imaging (EVINCI) study, clinical and biohumoral data were collected. RESULTS: Functionally significant CAD-ie, obstructive coronary disease seen at invasive angiography causing myocardial ischemia at stress imaging or associated with reduced fractional flow reserve (FFR < 0.8), or both-was present in 15.2% of patients. High-density lipoprotein (HDL) cholesterol, aspartate aminotransferase (AST) levels, and high-sensitivity C-reactive protein (hs-CRP) were the only independent predictors of disease among 31 biomarkers analyzed. The model integrating these biohumoral markers with clinical variables outperformed the Genders model by receiver operating characteristic curve (ROC) (area under the curve [AUC], 0.70 [standard error (SE), 0.03] vs 0.58 [SE, 0.03], respectively, P < 0.001) and reclassification analysis (net reclassification improvement, 0.15 [SE, 0.07]; P = 0.04). Cross-validation of the ROC analysis confirmed the discrimination ability of the new model (AUC, 0.66). As many as 56% of patients who were assigned to a higher pretest probability by the Genders model were correctly reassigned to a low probability class (< 15%) by the new integrated model. CONCLUSIONS: The Genders model has a low accuracy for predicting functionally significant CAD. A new model integrating HDL cholesterol, AST, and hs-CRP levels with common clinical variables has a higher predictive accuracy for functionally significant CAD and allows the reclassification of patients from an intermediate/high to a low pretest likelihood of CAD.
Authors: Borek Foldyna; James E Udelson; Júlia Karády; Dahlia Banerji; Michael T Lu; Thomas Mayrhofer; Daniel O Bittner; Nandini M Meyersohn; Hamed Emami; Tessa S S Genders; Christopher B Fordyce; Maros Ferencik; Pamela S Douglas; Udo Hoffmann Journal: Eur Heart J Cardiovasc Imaging Date: 2019-05-01 Impact factor: 6.875
Authors: Elena Michelucci; Nicoletta Di Giorgi; Francesco Finamore; Jeff M Smit; Arthur J H A Scholte; Giovanni Signore; Silvia Rocchiccioli Journal: Sci Rep Date: 2021-06-18 Impact factor: 4.379