{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:40:06Z","timestamp":1786977606289,"version":"build-2736575974"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"content-version":"vor","delay-in-days":1998,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T00:00:00Z","timestamp":1593475200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Left ventricular non-compaction cardiomyopathy (LVNC) is a genetic heart disease, with heart failure, arrhythmias, and embolic events as main clinical manifestations. The goal of this study was to analyse a large set of echocardiographic (echo) and cardiac magnetic resonance imaging (CMRI) parameters using machine learning (ML) techniques to find imaging predictors of clinical outcomes in a long-term follow-up of LVNC patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Patients with echo and\/or CMRI criteria of LVNC, followed from January 2011 to December 2017 in the heart failure section of a tertiary referral cardiologic hospital, were enrolled in a retrospective study. Two-dimensional colour Doppler echocardiography and subsequent CMRI were carried out. Twenty-four hour Holter monitoring was also performed in all patients. Death, cardiac transplantation, heart failure hospitalization, aborted sudden cardiac death, complex ventricular arrhythmias (sustained and non-sustained ventricular tachycardia), and embolisms (i.e. stroke, pulmonary thromboembolism and\/or peripheral arterial embolism) were registered and were referred to as major adverse cardiovascular events (MACEs) in this study. Recruited for the study were 108 LVNC patients, aged 38.3 \u00b1 15.5 years, 48.1% men, diagnosed by echo and CMRI criteria. They were followed for 5.8 \u00b1 3.9 years, and MACEs were registered. CMRI and echo parameters were analysed via a supervised ML methodology. Forty-seven (43.5%) patients had at least one MACE. The best performance of imaging variables was achieved by combining four parameters: left ventricular (LV) ejection fraction (by CMRI), right ventricular (RV) end-systolic volume (by CMRI), RV systolic dysfunction (by echo), and RV lower diameter (by CMRI) with accuracy, sensitivity, and specificity rates of 75.5%, 77%, 75%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings show the importance of biventricular assessment to detect the severity of this cardiomyopathy and to plan for early clinical intervention. In addition, this study shows that even patients with normal LV function and negative late gadolinium enhancement had MACE. ML is a promising tool for analysing a large set of parameters to stratify and predict prognosis in LVNC patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ehf2.12795","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T23:09:34Z","timestamp":1593558574000},"page":"2431-2439","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Biventricular Imaging Markers to Predict Outcomes in Non-Compaction Cardiomyopathy: A Machine Learning Study"],"prefix":"10.1093","volume":"7","author":[{"given":"Camila","family":"Rocon","sequence":"first","affiliation":[{"name":"Heart Institute (InCor) do Hospital das Cl\u00ednicas da Faculdade de Medicina da Universidade de S\u00e3o Paulo , Av. Dr. En\u00e9as de Carvalho Aguiar, 44, S\u00e3o Paulo, 05403-000 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Tabassian","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Sciences, University of Leuven , Leuven ,","place":["Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo Dantas","family":"Tavares de Melo","sequence":"additional","affiliation":[{"name":"Heart Institute (InCor) do Hospital das Cl\u00ednicas da Faculdade de Medicina da Universidade de S\u00e3o Paulo , Av. Dr. En\u00e9as de Carvalho Aguiar, 44, S\u00e3o Paulo, 05403-000 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Arimateia","family":"de Araujo Filho","sequence":"additional","affiliation":[{"name":"Heart Institute (InCor) do Hospital das Cl\u00ednicas da Faculdade de Medicina da Universidade de S\u00e3o Paulo , Av. Dr. En\u00e9as de Carvalho Aguiar, 44, S\u00e3o Paulo, 05403-000 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cesar Jos\u00e9","family":"Grupi","sequence":"additional","affiliation":[{"name":"Heart Institute (InCor) do Hospital das Cl\u00ednicas da Faculdade de Medicina da Universidade de S\u00e3o Paulo , Av. Dr. En\u00e9as de Carvalho Aguiar, 44, S\u00e3o Paulo, 05403-000 ,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Rodrigues","family":"Parga Filho","sequence":"additional","affiliation":[{"name":"Heart Institute (InCor) do Hospital das Cl\u00ednicas da Faculdade de Medicina da Universidade de S\u00e3o Paulo , Av. 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