{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T19:38:21Z","timestamp":1791833901314,"version":"4.3.4"},"reference-count":36,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Imaging"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We conducted a multicenter, retrospective, cross-sectional study of 961 patients (median age, 67 years; 61% men) 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 \u22651000 Hounsfield units. Associations were assessed using frailty regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Over a median follow-up of 30 (30\u2013460) days, 61 patients experienced MACE (nonfatal myocardial infarction or cardiovascular mortality). An elevated CAC-DAD score (\u22652050 based on optimal cutoff) captured more MACE than AS \u2265400 (74% versus 57%;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =0.002). Univariable analysis revealed that an elevated CAC-DAD score, AS \u2265400 and AS \u2265100, age, diabetes, hypertension, and statin use predicted MACE. On multivariable analysis, only the CAC-DAD score (hazard ratio, 2.57 [95% CI, 1.43\u20134.61];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =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 (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      =0.008).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circimaging.125.018059","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:00:13Z","timestamp":1751605213000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Novel CAC Dispersion and Density Score to Predict Myocardial Infarction and Cardiovascular Mortality"],"prefix":"10.1161","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2244-6033","authenticated-orcid":true,"given":"Gavin","family":"Huangfu","sequence":"first","affiliation":[{"name":"Department of Cardiology, Fiona Stanley Hospital, Perth, WA, Australia (G.H., A.R.I., H.S., L.D., G.D.)."},{"name":"Medical School, The University of Western Australia, Perth (G.H., J.L., H.S., G.D.)."},{"name":"Harry Perkins Institute of Medical Research, Murdoch, WA, Australia (G.H., A.R.I., G.D.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0501-0980","authenticated-orcid":true,"given":"Abdul R.","family":"Ihdayhid","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Fiona Stanley Hospital, Perth, WA, Australia (G.H., A.R.I., H.S., L.D., G.D.)."},{"name":"Harry Perkins Institute of Medical Research, Murdoch, WA, Australia (G.H., A.R.I., G.D.)."},{"name":"Faculty of Health Sciences, Curtin University, Bentley, WA, Australia (A.R.I.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Kwok","sequence":"additional","affiliation":[{"name":"Artrya Ltd, Perth, WA, Australia (S.K., J.K., K.N.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8915-318X","authenticated-orcid":true,"given":"John","family":"Konstantopoulos","sequence":"additional","affiliation":[{"name":"Artrya Ltd, Perth, WA, Australia (S.K., J.K., K.N.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3357-6130","authenticated-orcid":true,"given":"Kai","family":"Niu","sequence":"additional","affiliation":[{"name":"Artrya Ltd, Perth, WA, Australia (S.K., J.K., K.N.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7508-0565","authenticated-orcid":true,"given":"Juan","family":"Lu","sequence":"additional","affiliation":[{"name":"Medical School, The University of Western Australia, Perth (G.H., J.L., H.S., G.D.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harry","family":"Smallbone","sequence":"additional","affiliation":[{"name":"Department of 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