Simon Winther1, Samuel Emil Schmidt2, Thomas Mayrhofer3, Hans Erik Bøtker4, Udo Hoffmann5, Pamela S Douglas6, William Wijns7, Jeroen Bax8, Louise Nissen9, Vibeke Lynggaard9, Jens Juel Christiansen10, Antti Saraste11, Morten Bøttcher9, Juhani Knuuti11. 1. Department of Cardiology, Gødstrup Hospital, Herning, Denmark. Electronic address: sw@dadlnet.dk. 2. Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. 3. Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany. 4. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark. 5. Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 6. Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina. 7. Lambe Institute for Translational Medicine, National University of Ireland, Galway, Ireland; CÚRAM, National University of Ireland, Galway, Ireland. 8. Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. 9. Department of Cardiology, Gødstrup Hospital, Herning, Denmark. 10. Department of Medicine, Gødstrup Hospital, Herning, Denmark. 11. Heart Center, Turku University Hospital, Turku, Finland; Turku PET Centre, Turku University Hospital, University of Turku, Turku, Finland.
Abstract
BACKGROUND: The prevalence of obstructive coronary artery disease (CAD) in symptomatic patients referred for diagnostic testing has declined, warranting optimization of individualized diagnostic strategies. OBJECTIVES: This study sought to present a simple, clinically applicable tool enabling estimation of the likelihood of obstructive CAD by combining a pre-test probability (PTP) model (Diamond-Forrester approach using sex, age, and symptoms) with clinical risk factors and coronary artery calcium score (CACS). METHODS: The new tool was developed in a cohort of symptomatic patients (n = 41,177) referred for diagnostic testing. The risk factor-weighted clinical likelihood (RF-CL) was calculated through PTP and risk factors, while the CACS-weighted clinical likelihood (CACS-CL) added CACS. The 2 calculation models were validated in European and North American cohorts (n = 15,411) and compared with a recently updated PTP table. RESULTS: The RF-CL and CACS-CL models predicted the prevalence of obstructive CAD more accurately in the validation cohorts than the PTP model, and markedly increased the area under the receiver-operating characteristic curves of obstructive CAD: for the PTP model, 72 (95% confidence intervals [CI]: 71 to 74); for the RF-CL model, 75 (95% CI: 74 to 76); and for the CACS-CL model, 85 (95% CI: 84 to 86). In total, 38% of the patients in the RF-CL group and 54% in the CACS-CL group were categorized as having a low clinical likelihood of CAD, as compared with 11% with the PTP model. CONCLUSIONS: A simple risk factor and CACS-CL tool enables improved prediction and discrimination of patients with suspected obstructive CAD. The tool empowers reclassification of patients to low likelihood of CAD, who need no further testing.
BACKGROUND: The prevalence of obstructive coronary artery disease (CAD) in symptomatic patients referred for diagnostic testing has declined, warranting optimization of individualized diagnostic strategies. OBJECTIVES: This study sought to present a simple, clinically applicable tool enabling estimation of the likelihood of obstructive CAD by combining a pre-test probability (PTP) model (Diamond-Forrester approach using sex, age, and symptoms) with clinical risk factors and coronary artery calcium score (CACS). METHODS: The new tool was developed in a cohort of symptomatic patients (n = 41,177) referred for diagnostic testing. The risk factor-weighted clinical likelihood (RF-CL) was calculated through PTP and risk factors, while the CACS-weighted clinical likelihood (CACS-CL) added CACS. The 2 calculation models were validated in European and North American cohorts (n = 15,411) and compared with a recently updated PTP table. RESULTS: The RF-CL and CACS-CL models predicted the prevalence of obstructive CAD more accurately in the validation cohorts than the PTP model, and markedly increased the area under the receiver-operating characteristic curves of obstructive CAD: for the PTP model, 72 (95% confidence intervals [CI]: 71 to 74); for the RF-CL model, 75 (95% CI: 74 to 76); and for the CACS-CL model, 85 (95% CI: 84 to 86). In total, 38% of the patients in the RF-CL group and 54% in the CACS-CL group were categorized as having a low clinical likelihood of CAD, as compared with 11% with the PTP model. CONCLUSIONS: A simple risk factor and CACS-CL tool enables improved prediction and discrimination of patients with suspected obstructive CAD. The tool empowers reclassification of patients to low likelihood of CAD, who need no further testing.
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Authors: Göran Bergström; Margaretha Persson; Martin Adiels; Elias Björnson; Carl Bonander; Håkan Ahlström; Joakim Alfredsson; Oskar Angerås; Göran Berglund; Anders Blomberg; John Brandberg; Mats Börjesson; Kerstin Cederlund; Ulf de Faire; Olov Duvernoy; Örjan Ekblom; Gunnar Engström; Jan E Engvall; Erika Fagman; Mats Eriksson; David Erlinge; Björn Fagerberg; Agneta Flinck; Isabel Gonçalves; Emil Hagström; Ola Hjelmgren; Lars Lind; Eva Lindberg; Per Lindqvist; Johan Ljungberg; Martin Magnusson; Maria Mannila; Hanna Markstad; Moman A Mohammad; Fredrik H Nystrom; Ellen Ostenfeld; Anders Persson; Annika Rosengren; Anette Sandström; Anders Själander; Magnus C Sköld; Johan Sundström; Eva Swahn; Stefan Söderberg; Kjell Torén; Carl Johan Östgren; Tomas Jernberg Journal: Circulation Date: 2021-09-20 Impact factor: 29.690
Authors: Peter Loof Møller; Palle D Rohde; Simon Winther; Peter Breining; Louise Nissen; Anders Nykjaer; Morten Bøttcher; Mette Nyegaard; Mads Kjolby Journal: Front Cardiovasc Med Date: 2021-04-16
Authors: Antonio Esposito; Marco Francone; Daniele Andreini; Vitaliano Buffa; Filippo Cademartiri; Iacopo Carbone; Alberto Clemente; Andrea Igoren Guaricci; Marco Guglielmo; Ciro Indolfi; Ludovico La Grutta; Guido Ligabue; Carlo Liguori; Giuseppe Mercuro; Saima Mushtaq; Danilo Neglia; Anna Palmisano; Roberto Sciagrà; Sara Seitun; Davide Vignale; Gianluca Pontone; Nazario Carrabba Journal: Radiol Med Date: 2021-06-23 Impact factor: 3.469