Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, an...

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Veröffentlicht in:	The Lancet (British edition) 2021-10, Vol.398 (10309), p.1427-1435
Hauptverfasser:	Karwath, Andreas, Bunting, Karina V, Gill, Simrat K, Tica, Otilia, Pendleton, Samantha, Aziz, Furqan, Barsky, Andrey D, Chernbumroong, Saisakul, Duan, Jinming, Mobley, Alastair R, Cardoso, Victor Roth, Slater, Karin, Williams, John A, Bruce, Emma-Jane, Wang, Xiaoxia, Flather, Marcus D, Coats, Andrew J S, Gkoutos, Georgios V, Kotecha, Dipak
Format:	Artikel
Sprache:	eng
Schlagworte:	Adrenergic beta-Antagonists - therapeutic use Aged Artificial intelligence Atrial Fibrillation - drug therapy Beta blockers Bias Cardiac arrhythmia Clinical medicine Clinical trials Cluster Analysis Clustering Comorbidity Congestive heart failure Demographics Double-Blind Method Drug therapy Ejection fraction Electrocardiography Female Fibrillation General & Internal Medicine Health risks Heart failure Heart Failure - drug therapy Heart Failure - mortality Humans Learning algorithms Life Sciences & Biomedicine Literature reviews Machine Learning Male Medical prognosis Medical research Medicine, General & Internal Middle Aged Mortality Mortality risk Neural networks Patients Placebos Principal components analysis Rhythm Science & Technology Sinuses Statistical analysis Stroke Volume Ventricle Ventricular Function, Left
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