{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:14:27Z","timestamp":1776975267795,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The addition of coronary artery calcium score (CACS) to prediction models has been verified to improve performance. Machine learning (ML) algorithms become important medical tools in an era of precision medicine, However, combined utility by CACS and ML algorithms in hypertensive patients to forecast obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CCTA) is rare.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This retrospective study was composed of 1,273 individuals with hypertension and without a history of CAD, who underwent dual-source computed tomography evaluation. We applied five ML algorithms, coupled with clinical factors, imaging parameters, and CACS to construct predictive models. Moreover, 80% individuals were randomly taken as a training set on which 5-fold cross-validation was done and the remaining 20% were regarded as a validation set.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>16.7% (212 out of 1,273) of hypertensive patients had obstructive CAD. Extreme Gradient Boosting (XGBoost) posted the biggest area under the receiver operator characteristic curve (AUC) of 0.83 in five ML algorithms. Continuous net reclassification improvement (NRI) was 0.55 (95% CI (0.39\u20130.71), <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001), and integrated discrimination improvement (IDI) was 0.04 (95% CI (0.01\u20130. 07), <jats:italic>p<\/jats:italic>\u2009=\u20090.0048) when the XGBoost model was compared with traditional Models. In the subgroup analysis stratified by hypertension levels, XGBoost still had excellent performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The ML model incorporating clinical features and CACS may accurately forecast the presence of obstructive CAD on CCTA among hypertensive patients. XGBoost is superior to other ML algorithms.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02352-8","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T16:03:07Z","timestamp":1698681787000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients"],"prefix":"10.1186","volume":"23","author":[{"given":"Minxian","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengting","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinsheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongkui","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"2352_CR1","doi-asserted-by":"publisher","first-page":"e2160","DOI":"10.1097\/md.0000000000002160","volume":"94","author":"CY Wu","year":"2015","unstructured":"Wu CY, Hu HY, Chou YJ, Huang N, Chou YC, Li CP. High Blood Pressure and all-cause and Cardiovascular Disease mortalities in Community-Dwelling older adults. Medicine. 2015;94:e2160. https:\/\/doi.org\/10.1097\/md.0000000000002160.","journal-title":"Medicine"},{"key":"2352_CR2","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1016\/j.jacc.2008.07.031","volume":"52","author":"MJ Budoff","year":"2008","unstructured":"Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, et al. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery Disease: results from the prospective multicenter ACCURACY (Assessment by Coronary computed Tomographic Angiography of individuals undergoing invasive coronary angiography) trial. J Am Coll Cardiol. 2008;52:1724\u201332. https:\/\/doi.org\/10.1016\/j.jacc.2008.07.031.","journal-title":"J Am Coll Cardiol"},{"key":"2352_CR3","doi-asserted-by":"publisher","first-page":"2324","DOI":"10.1056\/NEJMoa0806576","volume":"359","author":"JM Miller","year":"2008","unstructured":"Miller JM, Rochitte CE, Dewey M, Arbab-Zadeh A, Niinuma H, Gottlieb I, et al. Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med. 2008;359:2324\u201336. https:\/\/doi.org\/10.1056\/NEJMoa0806576.","journal-title":"N Engl J Med"},{"key":"2352_CR4","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1016\/j.jacc.2012.06.008","volume":"60","author":"KM Chinnaiyan","year":"2012","unstructured":"Chinnaiyan KM, Peyser P, Goraya T, Ananthasubramaniam K, Gallagher M, Depetris A, et al. Impact of a continuous quality improvement initiative on appropriate use of coronary computed tomography angiography. Results from a multicenter, statewide registry, the Advanced Cardiovascular Imaging Consortium. J Am Coll Cardiol. 2012;60:1185\u201391. https:\/\/doi.org\/10.1016\/j.jacc.2012.06.008.","journal-title":"J Am Coll Cardiol"},{"key":"2352_CR5","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.jacc.2018.05.027","volume":"72","author":"P Greenland","year":"2018","unstructured":"Greenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE. Coronary calcium score and Cardiovascular Risk. J Am Coll Cardiol. 2018;72:434\u201347. https:\/\/doi.org\/10.1016\/j.jacc.2018.05.027.","journal-title":"J Am Coll Cardiol"},{"key":"2352_CR6","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1056\/NEJMoa072100","volume":"358","author":"R Detrano","year":"2008","unstructured":"Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358:1336\u201345. https:\/\/doi.org\/10.1056\/NEJMoa072100.","journal-title":"N Engl J Med"},{"key":"2352_CR7","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.beem.2014.01.004","volume":"28","author":"TM Okwuosa","year":"2014","unstructured":"Okwuosa TM, Mallikethi-Reddy S, Jones DM. Strategies for treating lipids for prevention: risk stratification models with and without imaging. Best practice. Res Clin Endocrinol Metabolism. 2014;28:295\u2013307. https:\/\/doi.org\/10.1016\/j.beem.2014.01.004.","journal-title":"Res Clin Endocrinol Metabolism"},{"key":"2352_CR8","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1093\/eurheartj\/ehu358","volume":"36","author":"I Cho","year":"2015","unstructured":"Cho I, Chang HJ, \u00d3H B, Shin S, Sung JM, Lin FY, et al. Incremental prognostic utility of coronary CT angiography for asymptomatic patients based upon extent and severity of coronary artery calcium: results from the COronary CT angiography EvaluatioN for clinical outcomes InteRnational Multicenter (CONFIRM) study. Eur Heart J. 2015;36:501\u20138. https:\/\/doi.org\/10.1093\/eurheartj\/ehu358.","journal-title":"Eur Heart J"},{"key":"2352_CR9","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.jacc.2010.11.078","volume":"58","author":"FY Lin","year":"2011","unstructured":"Lin FY, Shaw LJ, Dunning AM, Labounty TM, Choi JH, Weinsaft JW, et al. Mortality risk in symptomatic patients with nonobstructive coronary artery Disease: a prospective 2-center study of 2,583 patients undergoing 64-detector row coronary computed tomographic angiography. J Am Coll Cardiol. 2011;58:510\u20139. https:\/\/doi.org\/10.1016\/j.jacc.2010.11.078.","journal-title":"J Am Coll Cardiol"},{"key":"2352_CR10","doi-asserted-by":"publisher","unstructured":"Budoff MJ, Diamond GA, Raggi P, Arad Y, Guerci AD, Callister TQ et al. Continuous probabilistic prediction of angiographically significant coronary artery Disease using electron beam tomography. Circulation.(2002) 105:1791\u20136. https:\/\/doi.org\/10.1161\/01.cir.0000014483.43921.8c.","DOI":"10.1161\/01.cir.0000014483.43921.8c"},{"key":"2352_CR11","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1097\/00004872-200403000-00024","volume":"22","author":"J Shemesh","year":"2004","unstructured":"Shemesh J, Morag-Koren N, Goldbourt U, Grossman E, Tenenbaum A, Fisman EZ, et al. Coronary calcium by spiral computed tomography predicts cardiovascular events in high-risk hypertensive patients. J Hypertens. 2004;22:605\u201310. https:\/\/doi.org\/10.1097\/00004872-200403000-00024.","journal-title":"J Hypertens"},{"key":"2352_CR12","doi-asserted-by":"publisher","unstructured":"Kalra SS, Shanahan CM. Vascular calcification and hypertension: cause and effect. Annals of medicine.(2012) 44 Suppl 1:S85-92. https:\/\/doi.org\/10.3109\/07853890.2012.660498.","DOI":"10.3109\/07853890.2012.660498"},{"key":"2352_CR13","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1038\/ajg.2013.332","volume":"108","author":"AG Singal","year":"2013","unstructured":"Singal AG, Mukherjee A, Elmunzer BJ, Higgins PD, Lok AS, Zhu J, et al. Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma. Am J Gastroenterol. 2013;108:1723\u201330. https:\/\/doi.org\/10.1038\/ajg.2013.332.","journal-title":"Am J Gastroenterol"},{"key":"2352_CR14","doi-asserted-by":"publisher","first-page":"577537","DOI":"10.3389\/fendo.2020.577537","volume":"11","author":"Y Wu","year":"2020","unstructured":"Wu Y, Rao K, Liu J, Han C, Gong L, Chong Y, et al. Machine learning algorithms for the prediction of Central Lymph Node Metastasis in patients with papillary thyroid Cancer. Front Endocrinol. 2020;11:577537. https:\/\/doi.org\/10.3389\/fendo.2020.577537.","journal-title":"Front Endocrinol"},{"key":"2352_CR15","doi-asserted-by":"publisher","first-page":"600497","DOI":"10.3389\/fcvm.2020.600497","volume":"7","author":"R Lai","year":"2020","unstructured":"Lai R, Ju J, Lin Q, Xu H. Coronary artery calcification under statin therapy and its Effect on Cardiovascular outcomes: a systematic review and Meta-analysis. Front Cardiovasc Med. 2020;7:600497. https:\/\/doi.org\/10.3389\/fcvm.2020.600497.","journal-title":"Front Cardiovasc Med"},{"key":"2352_CR16","doi-asserted-by":"publisher","first-page":"3963","DOI":"10.1038\/ncomms4963","volume":"5","author":"L Han","year":"2014","unstructured":"Han L, Yuan Y, Zheng S, Yang Y, Li J, Edgerton ME, et al. The Pan-cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes. Nat Commun. 2014;5:3963. https:\/\/doi.org\/10.1038\/ncomms4963.","journal-title":"Nat Commun"},{"key":"2352_CR17","doi-asserted-by":"publisher","unstructured":"Al\u2019Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. European heart journal.(2020) 41:359 \u2013 67. https:\/\/doi.org\/10.1093\/eurheartj\/ehz565.","DOI":"10.1093\/eurheartj\/ehz565"},{"key":"2352_CR18","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.ijcard.2015.11.011","volume":"203","author":"HA Isma\u2019eel","year":"2016","unstructured":"Isma\u2019eel HA, Serhan M, Sakr GE, Lamaa N, Garabedian T, Elhajj I, et al. Diamond-Forrester and Morise risk models perform poorly in predicting obstructive coronary Disease in Middle Eastern Cohort. Int J Cardiol. 2016;203:803\u20135. https:\/\/doi.org\/10.1016\/j.ijcard.2015.11.011.","journal-title":"Int J Cardiol"},{"key":"2352_CR19","doi-asserted-by":"publisher","unstructured":"Baskaran L, Danad I, Gransar H, Schulman-Marcus B\u00d3H, Lin J et al. FY, A Comparison of the Updated Diamond-Forrester, CAD Consortium, and CONFIRM History-Based Risk Scores for Predicting Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: The SCOT-HEART Coronary CTA Cohort. JACC Cardiovascular imaging.(2019) 12:1392 \u2013 400. https:\/\/doi.org\/10.1016\/j.jcmg.2018.02.020.","DOI":"10.1016\/j.jcmg.2018.02.020"},{"key":"2352_CR20","doi-asserted-by":"publisher","unstructured":"Zhou J, Liu Y, Huang L, Tan Y, Li X, Zhang H et al. Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: A coronary computed tomographic angiography study. Journal of cardiovascular computed tomography.(2017) 11:317 \u2013 23. https:\/\/doi.org\/10.1016\/j.jcct.2017.05.004.","DOI":"10.1016\/j.jcct.2017.05.004"},{"key":"2352_CR21","doi-asserted-by":"publisher","unstructured":"He T, Liu X, Xu N, Li Y, Wu Q, Liu M et al. Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic review. Clinics (Sao Paulo, Brazil).(2017) 72:188 \u2013 96. https:\/\/doi.org\/10.6061\/clinics\/2017(03)10.","DOI":"10.6061\/clinics\/2017(03)10"},{"key":"2352_CR22","doi-asserted-by":"publisher","unstructured":"Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery Disease: a 5-year multicentre prospective registry analysis. European heart journal.(2017) 38:500\u20137. https:\/\/doi.org\/10.1093\/eurheartj\/ehw188.","DOI":"10.1093\/eurheartj\/ehw188"},{"key":"2352_CR23","doi-asserted-by":"publisher","unstructured":"Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A et al. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium. Circulation Cardiovascular imaging.(2018) 11:e007217. https:\/\/doi.org\/10.1161\/circimaging.117.007217.","DOI":"10.1161\/circimaging.117.007217"},{"key":"2352_CR24","doi-asserted-by":"publisher","unstructured":"Kalscheur MM, Kipp RT, Tattersall MC, Mei C, Buhr KA, DeMets DL et al. Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. Circulation Arrhythmia and electrophysiology.(2018) 11:e005499. https:\/\/doi.org\/10.1161\/circep.117.005499.","DOI":"10.1161\/circep.117.005499"},{"key":"2352_CR25","doi-asserted-by":"publisher","unstructured":"Nakanishi R, Slomka PJ, Rios R, Betancur J, Blaha MJ, Nasir K et al. Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths. JACC Cardiovascular imaging.(2021) 14:615 \u2013 25. https:\/\/doi.org\/10.1016\/j.jcmg.2020.08.024.","DOI":"10.1016\/j.jcmg.2020.08.024"},{"key":"2352_CR26","doi-asserted-by":"publisher","unstructured":"Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ (Clinical research ed).(2012) 344:e3485. https:\/\/doi.org\/10.1136\/bmj.e3485.","DOI":"10.1136\/bmj.e3485"},{"key":"2352_CR27","doi-asserted-by":"publisher","unstructured":"Erbel R, Lehmann N, M\u00f6hlenkamp S, Churzidse S, Bauer M, K\u00e4lsch H et al. Subclinical coronary atherosclerosis predicts cardiovascular risk in different stages of hypertension: result of the Heinz Nixdorf Recall Study. Hypertension (Dallas, Tex: 1979).(2012) 59:44\u201353. https:\/\/doi.org\/10.1161\/hypertensionaha.111.180489.","DOI":"10.1161\/hypertensionaha.111.180489"},{"key":"2352_CR28","doi-asserted-by":"publisher","unstructured":"Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S et al. Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation.(2018) 137:e67-e492. https:\/\/doi.org\/10.1161\/cir.0000000000000558.","DOI":"10.1161\/cir.0000000000000558"},{"key":"2352_CR29","doi-asserted-by":"publisher","unstructured":"Mackey RH, Venkitachalam L, Sutton-Tyrrell K. Calcifications, arterial stiffness and atherosclerosis. Advances in cardiology.(2007) 44:234 \u2013 44. https:\/\/doi.org\/10.1159\/000096744.","DOI":"10.1159\/000096744"},{"key":"2352_CR30","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1038\/hr.2017.21","volume":"40","author":"SW Lee","year":"2017","unstructured":"Lee SW, Kim HC, Lee JM, Yun YM, Lee JY, Suh I. Association between changes in systolic blood pressure and incident Diabetes in a community-based cohort study in Korea. Hypertens Research: Official J Japanese Soc Hypertens. 2017;40:710\u20136. https:\/\/doi.org\/10.1038\/hr.2017.21.","journal-title":"Hypertens Research: Official J Japanese Soc Hypertens"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02352-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02352-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02352-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T16:04:12Z","timestamp":1698681852000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02352-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2352"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02352-8","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"14 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1975, as revised in 2000. This study was approved by the Ethics Committee Board of the First Affiliated Hospital of Dalian Medical University. All participants gave their written informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"244"}}