Precision phenotyping from routine laboratory parameters for out of hospital survival prediction in an all comers prospective PCI registry

Abstract Out-of-hospital mortality in coronary artery disease (CAD) is particularly high and established adverse event prediction tools are yet to be available. Our study aimed to investigate whether precision phenotyping can be performed using routine laboratory parameters for the prediction of out...

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Principais autores:	Paul-Adrian Călburean, Marius Harpa, Anda-Cristina Scurtu, Paul Grebenișan, Ioana-Andreea Nistor, Victor Vacariu, Reka-Katalin Drincal, Ioana Paula Şulea, Tiberiu Oltean, Petru-Vasile Mesaroş, László Hadadi
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2024-10-01
Series:	Scientific Reports
Assuntos:	Machine learning Coronary artery disease Percutaneous coronary intervention Survival analysis
Acceso en liña:	https://doi.org/10.1038/s41598-024-76936-3
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