Novel CAC Dispersion and Density Score to Predict Myocardial Infarction and Cardiovascular Mortality.
Authors
Affiliations (20)
Affiliations (20)
- Department of Cardiology, Fiona Stanley Hospital, Perth, WA, Australia (G.H., A.R.I., H.S., L.D., G.D.).
- Medical School, The University of Western Australia, Perth (G.H., J.L., H.S., G.D.).
- Harry Perkins Institute of Medical Research, Murdoch, WA, Australia (G.H., A.R.I., G.D.).
- Faculty of Health Sciences, Curtin University, Bentley, WA, Australia (A.R.I.).
- Artrya Ltd, Perth, WA, Australia (S.K., J.K., K.N.).
- University of Sydney, NSW, Australia (G.A.F., C.K.C., S.M.G.).
- Envision Medical Imaging, Perth, WA, Australia (L.D., B.A.).
- University of Queensland, Brisbane, Australia (C.H.-C.).
- Chinese University of Hong Kong, China (M.T.V.C.).
- University of Alberta, Edmonton, Canada (C.B.).
- McMaster University, Hamilton, ON, Canada (V.T., W.S., P.J.D., T.S.).
- The University of Chicago, IL (P.N.).
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO (P.K.W.).
- Western University, London, ON, Canada (M.M.).
- Center for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Kraków, Poland (W.S.).
- Intensive Care and Anaesthesiology Department, 5th Military Hospital, Kraków, Poland (W.S.).
- Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia (Y.F.A.A.).
- University of Cape Town, South Africa (B.B.).
- University of Ottawa Heart Institute, ON, Canada (G.D., B.J.W.C.).
- Victor Chang Cardiac Research Institute, Crawley, WA, Australia (G.D.).
Abstract
Coronary artery calcification (CAC) provides robust prediction for major adverse cardiovascular events (MACE), but current techniques disregard plaque distribution and protective effects of high CAC density. We investigated whether a novel CAC-dispersion and density (CAC-DAD) score will exhibit superior prognostic value compared with the Agatston score (AS) for MACE prediction. We conducted a multicenter, retrospective, cross-sectional study of 961 patients (median age, 67 years; 61% male) who underwent cardiac computed tomography for cardiovascular or perioperative risk assessment. Blinded analyzers applied deep learning algorithms to noncontrast scans to calculate the CAC-DAD score, which adjusts for the spatial distribution of CAC and assigns a protective weight factor for lesions with ≥1000 Hounsfield units. Associations were assessed using frailty regression. Over a median follow-up of 30 (30-460) days, 61 patients experienced MACE (nonfatal myocardial infarction or cardiovascular mortality). An elevated CAC-DAD score (≥2050 based on optimal cutoff) captured more MACE than AS ≥400 (74% versus 57%; <i>P</i>=0.002). Univariable analysis revealed that an elevated CAC-DAD score, AS ≥400 and AS ≥100, age, diabetes, hypertension, and statin use predicted MACE. On multivariable analysis, only the CAC-DAD score (hazard ratio, 2.57 [95% CI, 1.43-4.61]; <i>P</i>=0.002), age, statins, and diabetes remained significant. The inclusion of the CAC-DAD score in a predictive model containing demographic factors and AS improved the C statistic from 0.61 to 0.66 (<i>P</i>=0.008). The fully automated CAC-DAD score improves MACE prediction compared with the AS. Patients with a high CAC-DAD score, including those with a low AS, may be at higher risk and warrant intensification of their preventative therapies.