{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:11:34Z","timestamp":1768565494698,"version":"3.49.0"},"posted":{"date-parts":[[2023,9,11]]},"group-title":"Cardiovascular Medicine","reference-count":21,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,9,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2023.09.10.23295251","type":"posted-content","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T19:05:12Z","timestamp":1694459112000},"source":"Crossref","is-referenced-by-count":1,"title":["DCM-PROGRESS: predicting end-stage heart failure in non-ischemic dilated cardiomyopathy patients"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1327-0424","authenticated-orcid":false,"given":"A F","family":"Schmidt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P","family":"Leinveber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R","family":"Panovsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L","family":"Soukup","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P","family":"Machac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4779-5870","authenticated-orcid":false,"given":"R R","family":"van de Leur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A","family":"Sammani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K","family":"Lekadir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A ter","family":"Riele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F W","family":"Asselbergs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M J","family":"Boonstra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2023091409200685000_2023.09.10.23295251v1.1","doi-asserted-by":"publisher","DOI":"10.1136\/heart.83.6.645"},{"key":"2023091409200685000_2023.09.10.23295251v1.2","first-page":"e003704","article-title":"Prevalence and Disease Expression of Pathogenic and Likely Pathogenic Variants Associated With Inherited Cardiomyopathies in the General Population","volume":"15","year":"2022","journal-title":"Circ Genom Precis Med"},{"key":"2023091409200685000_2023.09.10.23295251v1.3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/ehf2.13100","article-title":"Cardiomyopathy & Myocarditis Registry Investigators Group. Differences between familial and sporadic dilated cardiomyopathy: ESC EORP Cardiomyopathy & Myocarditis registry","volume":"8","year":"2021","journal-title":"ESC Heart Fail"},{"key":"2023091409200685000_2023.09.10.23295251v1.4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-8159.2008.02286.x"},{"key":"2023091409200685000_2023.09.10.23295251v1.5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s12968-018-0466-7","article-title":"Prognostic value of myocardial strain and late gadolinium enhancement on cardiovascular magnetic resonance imaging in patients with idiopathic dilated cardiomyopathy with moderate to severely reduced ejection fraction","volume":"20","year":"2018","journal-title":"J Cardiovasc Magn Reson"},{"key":"2023091409200685000_2023.09.10.23295251v1.6","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehab368"},{"key":"2023091409200685000_2023.09.10.23295251v1.7","doi-asserted-by":"crossref","first-page":"2048004017734476","DOI":"10.1177\/2048004017734476","article-title":"Myocardial remodelling and recovery in dilated cardiomyopathy","volume":"6","year":"2017","journal-title":"JRSM Cardiovasc Dis"},{"key":"2023091409200685000_2023.09.10.23295251v1.8","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1002\/ehf2.12809","article-title":"Mortality risk in dilated cardiomyopathy: the accuracy of heart failure prognostic models and dilated cardiomyopathy-tailored prognostic model","volume":"7","year":"2020","journal-title":"ESC Heart Fail"},{"key":"2023091409200685000_2023.09.10.23295251v1.9","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehs337"},{"key":"2023091409200685000_2023.09.10.23295251v1.10","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000001063"},{"key":"2023091409200685000_2023.09.10.23295251v1.11","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1007\/s12471-019-1288-4","article-title":"UNRAVEL: big data analytics research data platform to improve care of patients with cardiomyopathies using routine electronic health records and standardised biobanking","volume":"27","year":"2019","journal-title":"Neth Heart J"},{"key":"2023091409200685000_2023.09.10.23295251v1.12","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ijcard.2018.12.069","article-title":"Conversion formula from B-type natriuretic peptide to N-terminal proBNP values in patients with cardiovascular diseases","volume":"280","year":"2019","journal-title":"Int J Cardiol"},{"key":"2023091409200685000_2023.09.10.23295251v1.13","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.6.520"},{"key":"2023091409200685000_2023.09.10.23295251v1.14","unstructured":"Hastie T , Tibshirani R , Friedman J. The Elements of Statistical Learning. 2017."},{"key":"2023091409200685000_2023.09.10.23295251v1.15","doi-asserted-by":"crossref","unstructured":"James G , Witten D , Hastie T , Tibishirani R. An Introduction to Statistical Learning. 2013.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"2023091409200685000_2023.09.10.23295251v1.16","unstructured":"Steyerberg EW . Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009."},{"key":"2023091409200685000_2023.09.10.23295251v1.17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","article-title":"A simple, step-by-step guide to interpreting decision curve analysis","volume":"3","year":"2019","journal-title":"Diagnostic and Prognostic Research"},{"key":"2023091409200685000_2023.09.10.23295251v1.18","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X06295361"},{"key":"2023091409200685000_2023.09.10.23295251v1.19","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.105.584102"},{"key":"2023091409200685000_2023.09.10.23295251v1.20","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-156-11-201206050-00003"},{"key":"2023091409200685000_2023.09.10.23295251v1.21","doi-asserted-by":"publisher","DOI":"10.1177\/0962280220921415"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2023.09.10.23295251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T19:40:44Z","timestamp":1768506044000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2023.09.10.23295251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,11]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1101\/2023.09.10.23295251","relation":{},"subject":[],"published":{"date-parts":[[2023,9,11]]},"subtype":"preprint"}}