Elevated Lipoprotein(a) Independently Increases Risk for Short-Term Atherosclerotic Cardiovascular Events in Machine Learning Predictive Models

Lp(a) is a significant predictor of short-term ASCVD risk. Assessing Lp(a) and imminent ASCVD risk may assist in identifying patients who may benefit from escalation of preventative therapies.

Authors

Institutions

Publication Details

Journal
JACC Advances
Published
2025-10-25
DOI
https://doi.org/10.1016/j.jacadv.2025.102253
Primary Topic
Lipoproteins and Cardiovascular Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Elevated Lipoprotein(a) Independently Increases Risk for Short-Term Atherosclerotic Cardiovascular Events in Machine Learning Predictive Models

Rajesh Dash, Fátima Rodríguez, Abha Khandelwal, Anthony Lozama et al.
JACC Advances
Lipoproteins and Cardiovascular Health
article

Elevated Lipoprotein(a) Independently Increases Risk for Short-Term Atherosclerotic Cardiovascular Events in Machine Learning Predictive Models

Rajesh Dash, Fátima Rodríguez, Abha Khandelwal, Anthony Lozama, Brynn Kron, Shriram Nallamshetty, Andrew Ward, Alexander T. Sandhu
article en

Abstract

Lp(a) is a significant predictor of short-term ASCVD risk. Assessing Lp(a) and imminent ASCVD risk may assist in identifying patients who may benefit from escalation of preventative therapies.

JACC AdvancesVol. 4(11)
VA Palo Alto Health Care System (US), Novartis (United States) (US), Box (United States) (US), Stanford Medicine (US)
Openalex Percentile: Top 52%
Lipoproteins and Cardiovascular Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.