The Predictive Value of Coronary Artery Calcium Scoring for Major Adverse Cardiac Events According to Renal Function (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes: An International Multicenter [CONFIRM] Registry)

Ji Hyun Lee, Asim Rizvi, Bríain Hartaigh, Donghee Han, Mahn Won Park, Hadi Mirhedayati Roudsari, Wijnand J. Stuijfzand, Heidi Gransar, Yao Lu, Tracy Q. Callister, Daniel S. Berman, Augustin DeLago, Martin Hadamitzky, Joerg Hausleiter, Mouaz H. Al-Mallah, Matthew J. Budoff, Philipp A. Kaufmann, Gilbert L. Raff, Kavitha Chinnaiyan, Filippo CademartiriErica Maffei, Todd C. Villines, Yong Jin Kim, Jonathon Leipsic, Gudrun Feuchtner, Gianluca Pontone, Daniele Andreini, Hugo Marques, Pedro de Araújo Gonçalves, Ronen Rubinshtein, Stephan Achenbach, Leslee J. Shaw, Benjamin J.W. Chow, Ricardo C. Cury, Jeroen J. Bax, Hyuk Jae Chang, Erica C. Jones, Fay Y. Lin, James K. Min, Jessica M. Peña

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The prognostic performance of coronary artery calcium score (CACS) for predicting adverse outcomes in patients with decreased renal function remains unclear. We aimed to examine whether CACS improves risk stratification by demonstrating incremental value beyond a traditional risk score according to renal function status. 9,563 individuals without known coronary artery disease were enrolled. Estimated glomerular filtration rate (eGFR, ml/min/1.73 m 2 ) was ascertained using the modified Modification of Diet in Renal Disease formula, and was categorized as: ≥90, 60 to 89, and <60. CACS was categorized as 0, 1 to 100, 101 to 400, and >400. Multivariable Cox regression was used to estimate hazard ratios (HR) with 95% confidence intervals (95% CI) for major adverse cardiac events (MACE), comprising all-cause mortality, myocardial infarction, and late revascularization (>90 days). Mean age was 55.8 ± 11.5 years (52.8% male). In total, 261 (2.7%) patients experienced MACE over a median follow-up of 24.5 months (interquartile range: 16.9 to 41.1). Incident MACE increased with higher CACS across each eGFR category, with the highest rate observed among patients with CACS >400 and eGFR <60 (95.1 per 1,000 person-years). A CACS >400 increased MACE risk with HR 4.46 (95% CI 1.68 to 11.85), 6.63 (95% CI 4.03 to 10.92), and 6.14 (95% CI 2.85 to 13.21) for eGFR ≥90, 60 to 89, and <60, respectively, as compared with CACS 0. Further, CACS improved discrimination and reclassification beyond Framingham 10-year risk score (FRS) (AUC: 0.70 vs 0.64; category free-NRI: 0.51, all p <0.001) for predicting MACE in patients with impaired renal function (eGFR < 90). In conclusion, CACS improved risk stratification and provided incremental value beyond FRS for predicting MACE, irrespective of eGFR status.

Original languageEnglish
Pages (from-to)1435-1442
Number of pages8
JournalAmerican Journal of Cardiology
Volume123
Issue number9
DOIs
Publication statusPublished - 2019 May 1

Bibliographical note

Funding Information:
The research reported in this publication was supported by the National Institutes of Health (Bethesda, Maryland) under grant number R01 HL115150. The research was also supported, in part, by a generous gift from the Dalio Institute of Cardiovascular Imaging (New York, New York) and the Michael Wolk Foundation (New York, New York). Further, this research was supported by Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) grant number 2012027176. The research reported in this publication was supported by the National Institutes of Health (Bethesda, Maryland) under grant number R01 HL115150. The research was also supported, in part, by a generous gift from the Dalio Institute of Cardiovascular Imaging (New York, New York) and the Michael Wolk Foundation (New York, New York). Further, this research was supported by Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) grant number 2012027176.

Funding Information:
The research reported in this publication was supported by the National Institutes of Health (Bethesda, Maryland) under grant number R01 HL115150 . The research was also supported, in part, by a generous gift from the Dalio Institute of Cardiovascular Imaging (New York, New York) and the Michael Wolk Foundation (New York, New York). Further, this research was supported by Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) grant number 2012027176 .

Funding Information:
Dr. James K. Min receives funding from the Dalio Foundation, National Institutes of Health, and GE Healthcare. Dr. Min serves on the scientific advisory board of Arineta and GE Healthcare, and has an equity interest in Cleerly. Dr. Gianluca Pontone is a member of the speakers’ bureau for GE Healthcare, Bracco, and Medtronic. He also conducts research for GE Healthcare and Heartflow. Dr. Matthew Budoff receives grant support from GE Healthcare and the NIH. All other authors have no relevant disclosures.

Publisher Copyright:
© 2019

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine

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