Development and Validation of a Practical Model to Identify Patients at Risk of Bleeding After TAVR.
Navarese EP, Zhang Z, Kubica J, Andreotti F, Farinaccio A, Bartorelli AL, Bedogni F, Rupji M, Tomai F, Giordano A, Reimers B, Spaccarotella C, Wilczek K, Stepinska J, Witkowski A, Grygier M, Kukulski T, Wanha W, Wojakowski W, Lesiak M, Dudek D, Zembala MO, Berti S; a Joint Effort of the Italian and Polish Cardiac Interventional Societies.
Navarese EP, et al. Among authors: andreotti f.
JACC Cardiovasc Interv. 2021 Jun 14;14(11):1196-1206. doi: 10.1016/j.jcin.2021.03.024.
JACC Cardiovasc Interv. 2021.
PMID: 34112454
Free article.
METHODS: Using machine learning and multivariate regression, more than 100 clinical variables from 5,185 consecutive patients undergoing TAVR in the prospective multicenter RISPEVA (Registro Italiano GISE sull'Impianto di Valvola Aortica Percutanea; NCT02713932) registry were ana …
METHODS: Using machine learning and multivariate regression, more than 100 clinical variables from 5,185 consecutive patients undergoing TAV …