| Institution: | 1. Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;2. School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany;3. Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, USA;4. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA;5. Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA;1. Department of Cardiovascular Radiology & Endovascular Interventions, All India Institute of Medical Sciences, New Delhi, 110029, India;2. Department of Cardiology, All India Institute of Medical Sciences, New Delhi, 110029, India;1. Department of Medicine I, Klinikum der LMU München, Ludwig-Maximilians-Universität München, Marchioninistr. 15, Munich, Germany;2. German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Germany;3. Department of Radiology, Klinikum der LMU München, Ludwig-Maximilians-Universität München, Marchioninistr. 15, Munich, Germany;4. Department of Heart Surgery, Klinikum der LMU München, Ludwig-Maximilians-Universität München, Marchioninistr. 15, Munich, Germany;1. Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium;2. Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy;3. Department of Translational Medical Sciences, University of Campania ‘Luigi Vanvitelli’, Naples, Italy;4. Centro Cardiologico Monzino, IRCCS, Milan, Italy;5. Department of Biomedical and Clinical Sciences “Luigi Sacco”, University of Milan, Milan, Italy;6. Cardiology Clinic, “Alexandrovska” University Hospital, Medical University of Sofia, Sofia, Bulgaria;7. Department of Internal Medicine, Discipline of Cardiology, University of Campinas, Campinas, Brazil;8. Department of Cardiology, Lausanne University Hospital, Lausanne, Switzerland |
| Abstract: | BackgroundPretest probability (PTP) calculators utilize epidemiological-level findings to provide patient-level risk assessment of obstructive coronary artery disease (CAD). However, their limited accuracies question whether dissimilarities in risk factors necessarily result in differences in CAD. Using patient similarity network (PSN) analyses, we wished to assess the accuracy of risk factors and imaging markers to identify ≥50% luminal narrowing on coronary CT angiography (CCTA) in stable chest-pain patients.MethodsWe created four PSNs representing: patient characteristics, risk factors, non-coronary imaging markers and calcium score. We used spectral clustering to group individuals with similar risk profiles. We compared PSNs to a contemporary PTP score incorporating calcium score and risk factors to identify ≥50% luminal narrowing on CCTA in the CT-arm of the PROMISE trial. We also conducted subanalyses in different age and sex groups.ResultsIn 3556 individuals, the calcium score PSN significantly outperformed patient characteristic, risk factor, and non-coronary imaging marker PSNs (AUC: 0.81 vs. 0.57, 0.55, 0.54; respectively, p ?< ?0.001 for all). The calcium score PSN significantly outperformed the contemporary PTP score (AUC: 0.81 vs. 0.78, p ?< ?0.001), and using 0, 1–100 and ?> ?100 cut-offs provided comparable results (AUC: 0.81 vs. 0.81, p ?= ?0.06). Similar results were found in all subanalyses.ConclusionCalcium score on its own provides better individualized obstructive CAD prediction than contemporary PTP scores incorporating calcium score and risk factors. Risk factors may not be able to improve the diagnostic accuracy of calcium score to predict ≥50% luminal narrowing on CCTA. |
