Long-Term Mortality Predictors Using a Machine-Learning Approach in Patients With Chronic Limb-Threatening Ischemia After Peripheral Vascular Intervention.

Journal: Journal of the American Heart Association

PMID: 38761075

Abstract

BACKGROUND: Patients with chronic limb-threatening ischemia (CLTI) face a high long-term mortality risk. Identifying novel mortality predictors and risk profiles would enable individual health care plan design and improved survival. We aimed to leverage a random survival forest machine-learning algorithm to identify long-term all-cause mortality predictors in patients with CLTI undergoing peripheral vascular intervention.

Authors

Santiago Callegari

Vascular Medicine Outcomes Program Yale University New Haven CT.
Gaëlle Romain

Vascular Medicine Outcomes Program Yale University New Haven CT.
Jacob Cleman

Vascular Medicine Outcomes Program Yale University New Haven CT.
Lindsey Scierka

Vascular Medicine Outcomes Program Yale University New Haven CT.
Francky Jacque

Vascular Medicine Outcomes Program Yale University New Haven CT.
Kim G Smolderen

Vascular Medicine Outcomes Program Yale University New Haven CT.
Carlos Mena-Hurtado

Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, Connecticut, USA.

Keywords

Aged Aged, 80 and over Algorithms Catheterization, Peripheral Chronic Limb-Threatening Ischemia Female Humans Machine Learning Male Middle Aged Peripheral Vascular Diseases Random Forest Risk Factors

External Resources

View on PubMed Access via DOI PubMed (38761075)

Long-Term Mortality Predictors Using a Machine-Learning Approach in Patients With Chronic Limb-Threatening Ischemia After Peripheral Vascular Intervention.

Abstract

Authors

Keywords

External Resources

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